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Presentation Tips to Improve Your Data Science Communication Skills

In data science, communication is critical.

Of course, all data science work requires the technical skills to acquire your data, clean it, and perform your analysis. But as you're doing this, it’s also important to keep the why in mind. When you’re given a project, it’s worth stopping to ask yourself what value it has to the company, and where it fits into the larger picture.

Knowing the answers to those why questions is the first step in a process that’s as important as your actual analysis: communicating your findings to an audience of (usually) non-data scientists.

Data science communication is a topic Kristen Sosulski knows a lot about. She’s the Clinical Associate Professor of Information, Operations, and Management Sciences at New York University Stern School of Business, and she has essentially made a career out of teaching how to effectively communicate, both in academia and in business. She’s even written a book, Data Visualization Made Simple , about communicating data science results effectively with visualizations.

“Presenting and communicating your insights across an organization can be really, really powerful,” says Kristen.

So how can you approach communicating your models in a way that’s effective?

Relating The Problem

Let’s say you’ve built a model and have the opportunity to present your findings in front of a major decision-maker in the company. It’s your job to explain what the model means and the impact it could have on the business.

Kristen advocates starting by identifying the problem or challenge you’re addressing. Relate the problem to the interests of the audience, and help them understand the larger context. To get the audience on your side, ask questions before proposing your solution. For example:

Have you ever experienced this?

Have you ever observed that in our business?

This isn’t just a rhetorical technique, it’s a way of measuring what information your audience needs to understand the rest of your pitch. “If no one thinks this is a problem, then you have to start by introducing the problem, and then building the case for the problem,” says Kristen “You don't want to lose your audience by alienating them because they think this isn't a problem at all.”

Keep in mind that what seems like an obvious problem to you isn’t necessarily going to be obvious to your audience, particularly if you’ve spent the last few weeks with your head buried deep in data sets nobody else has seen yet. The problem you found in the data and are attempting to solve with your model could be something that nobody else is really aware of yet.

Once you’ve made the case for the problem itself, you can then present common solutions and why those aren’t the best, most effective fit.

“You want to create some type of suspense, but you're rooting all of this in a narrative,” says Kristen. “Starting with a problem, showing alternative solutions, and then you're ultimately going to reveal your solution.”

Communicating with Data

Although your pitch is often going to be primarily language-based (whether it’s a written report or a standup presentation at a meeting), representing your data visually is absolutely crucial to communicating its meaning with your audience. Very few people can look at a spreadsheet or table and draw quick, clear conclusions about what the data says. Anyone can compare the size of bars on a bar chart, or follow the trend on a line graph.

Data visualization is a crucial skill at every stage of the data science process, of course. “There are a lot of angles that you can take with visualization, and ways to look at it,” says Kristen. “You can look at it purely from the technical viewpoint, you can look at it from the exploratory viewpoint, like using visualization as a tool to explore your data.”

But it’s also critical for communication.

“I think about data visualization as something that we have in the toolkit to help people better understand our insights and our data,” says Kristen.

“Just on a human level, visualizations just allow us to perceive information a lot more clearly when they're well designed.”

When designing visuals for communication outside your own team, it’s important to keep your audience in mind. Your coworkers probably don’t have the context on your problem that your team has, and they may not have the technical knowledge, either. One of the biggest challenges of data science communication is tailoring your presentation to your audience's technical level and still getting your point across without overwhelming them (or patronizing them). 

A good trick for putting yourself in the shoes of a non-technical audience is thinking about the information you want reported to you when you’ve taken a car into the auto repair shop (assuming you’re not a car mechanic yourself). Generally, the most convincing mechanics are going to be the ones who can:

• Explain your problem in clear, simple terms.
• Show you the evidence the problem exists. ​
• Explain in clear, simple terms how the problem can be fixed.
• Give you a clear timeline and price for what the fix will cost.

You don’t want a 30-minute lecture on the factors that affect engine efficiency. You just want to be confident that you know what the problem really is and that the mechanic knows the best way to fix it.

This applies to communicating in data science, too, but now you’re the mechanic. When in doubt, the best approach is to keep it simple. Leaving in all of the details can be confusing and make your charts less readable, so include only what is necessary to communicate your point.

“Know that you don't have to show every data point at once, that you can slow it down. You can show a few data points at a time to help build your story and your narrative,” says Kristen.

Remember: you can always provide more information by answering questions if your coworkers feel they haven’t seen enough. But if you throw a series of complicated, difficult-to-read charts at them, you risk completely losing them, and that's difficult to undo.

Presentation Tips

Incorporating visualizations into a presentation is a bit of an art form, especially with highly technical data. To keep things simple and effective, Kristen suggests keeping a few guidelines in mind.

First: don’t force the chart to speak for itself. Make sure that you are taking the time to clearly explain what's shown on the screen. If you’re displaying data in a graph, only show one graph at a time, and explain what it’s showing and what it means in the broader context of the problem you’re addressing. You can also show where relationships exist, where outliers are, and how effective your model is compared to other models.

Pace is important, too.

“Don't go too fast, but this whole type of presentation shouldn't be more than 10 or 15 minutes,” says Kristen. “You want to make sure that you can do this type of pitch in a short period of time without overwhelming the audience with detail, but also being able to show the data clearly, and use the data as convincing evidence.”

Don’t be afraid to talk specifics. While you don’t want to overwhelm your audience with technical details, you do need to make sure you’ve included the details that are required to understand your presentation, and the charts they’ll be looking at. Are you talking about new leads generated over a period of hours, or years? Do the math for your audience. If you’re making a prediction, quantify it for them.

It also helps to direct the audience’s attention to certain visualizations. It can be tough correlating spoken word with visual data. If you’re talking about a particular section on your graph, point to it. Build your story from there.

Ultimately, you need to remember that communication is first and foremost a human interaction. “You’re the one sitting in front of the CEO, allow yourself to provide the explanations supported by the graphics, not the other way around.”

Data Science Communication Tools of the Trade

Of course, the first step in creating any presentation like this is actually creating the data visualizations. What you use to do that depends on your programming language of choice. “For me, my tool of choice is R and R Studio, and the various packages that go along with that, which are numerous,” says Kristen.

Python programmers also have a plethora of options for data visualization.

If you don’t know yet how you like visualizing data, Dataquest has interactive online courses on exploratory data visualization and storytelling with data viz in Python as well as a free course on data visualization in R . We also have a quick guide with some design tips that’ll help you make your charts easier to read.

Whatever tools you use, remember these basic tips for data science communication and you’ll have a better chance of nailing your next presentation:

• Start with the problem. Is this a problem your audience knows about already? If not, you’ll have to begin by establishing in clear terms that there is a problem.
• Have empathy for your audience and present them with the information they want in a format and in language they can understand.
• Illustrate your conclusions with data visualizations, but let your own explanation - not the charts - drive your presentation.
• Keep it simple, and leave out unnecessary detail in both your explanations and your charts. Don’t exceed 10 to 15 minutes for the whole presentation.

More learning resources

Data engineer salary and job description 2024, 21 data science projects for beginners (with source code).

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7 Tips for Delivering a Great Data Science Presentation

Delivering a great data science presentation can seem daunting. By no means am I a communications expert, but I have presented my fair share of talks to a diverse group of audiences. Through my experience, I’ve developed a few easy-to-remember tips to hopefully make your next data science presentation your best yet. These are tips that have worked for me, and I hope they’re helpful!

Without further ado, here are seven tips for delivering a great data science presentation .

1. Practice, practice, practice

This is the most important tip I can give to anyone. Practicing your presentation (in front of colleagues, friends, family, or the mirror) is one of the best ways to make sure you’ll feel comfortable on the day of your talk. In my opinion, it’s the best way to “stress-test” your talk and make sure you’re prepared.

By practicing multiple times, you can find portions of your presentation that can be edited, enhanced, or eliminated. By presenting to others and taking their feedback seriously, you can prepare yourself for your real audience and the questions they might have.

A corollary of practice, practice, practice , is perfect practice makes perfect . In your practice talks, try to simulate the environment of your talk as close as possible. It may help to deliver your practice talk while standing, dress up for your practice talk, or even practice in a public space if you expect your room to be noisy.

2. Talk about what you know, but don’t be afraid to branch out a little

Another way to feel more comfortable is to make sure you’re comfortable with the material you’re presenting. I’ve been to a few talks where it was clear that the presenter wasn’t entirely confident with the subject of their talk. Data science is a broad field, so it’s nearly impossible to be an expert in everything . Even though you probably can’t be an expert in everything , you can be knowledgeable about something . Talk about that something!

That being said, delivering a presentation can be an excellent way to familiarize yourself with a topic you haven’t explored in depth. Using the pressure of delivering a presentation may help you learn something you’ve been struggling to learn.

3. Look yourself in the mirror and say “I’m not an impostor”

Imposter syndrome is real and it sucks.

One of my favorite posters growing up was this one:

We’re all at different levels of developing our skills. I’ve found that some of my biggest data science aha moments come from having conversations with people outside of the field. Sometimes, knowing too much can be a bad thing, especially when it introduces rigidity or bias to a problem-solving strategy.

No matter what level you’re at, I guarantee that you can provide useful information to your audience .

4. Put yourself in the shoes of an audience member

If you were attending your talk, what would you want to learn? how would you expect the presentation to be delivered? how much information do you think you can absorb? You should also remind yourself that most people in the audience are coming to your presentation to have a positive experience.

By taking an empathetic approach to understand the preferences of your audience, you can better prepare your talk and make it as effective as possible.

One of my favorite (and most critical) ways to evaluate my own presentation is to ask myself “so what?” or “why should I care?” for each slide in my deck. If the slide can’t pass that test, I either remove it or edit it until it does. It’s probably an extreme approach, but I like doing this because it ensures that I’ve taken every step I can to reduce any irrelevant sections from my talk.

5. Play your game, not anyone else’s

One of the most notable data presentations is this one from Hans Rosling. He’s engaging, dynamic, and entertaining. His presentation style seems effortless. He makes data fun!

Although I’d like to give presentations like Hans Rosling, I know I can’t right now. The best I can do is to present in the style that best fits me. This doesn’t mean I’ve resigned to delivering flat, boring, and dry talks. Instead, I stay realistic and try to do the best that I can do, not the best Hans Rosling could do.

Speaking of being realistic…

6. Set realistic expectations, strive to meet them, and hold yourself accountable

Another way to say this is to set yourself up for success, not disappointment .

Here’s an example.

For my first presentation at a professional conference, the conference organizers provided ways to promote your session through social media and email. I thought this was really cool, but I had no interest taking part in these promotional activities. I had already committed to delivering my first talk at a professional conference, which took a lot of time to prepare for. I set a goal of delivering the best talk I could, and promoting my session was not part of my plan to achieve this goal. I stayed focused on my primary goal, and avoided getting distracted by anything else.

Here are a few different ways of turning unrealistic goals into realistic and attainable ones.

7. Take audience feedback and questions seriously, and use them to learn and improve

This tip doesn’t apply to delivering your presentation, it’s about your next presentation.

After you’ve finished your talk, do an analysis of your presentation.

• What went well?
• What could have been improved?
• An indicator I use to judge engagement are how many people laugh at one of my lame jokes and how many people ask questions
• You can use these questions to understand how well you conveyed your points and how engaging your presentation was.
• Did you receive any feedback or constructive criticism on your content, delivery, and presentation style?

You can learn a lot just by paying attention to how your audience received you and your own perception of your performance.

Just like how most machine learning algorithms get better at minimizing a loss function through iteration after iteration, you can improve your presentation through actively learning and iterating on your presentation style.

Wrapping up

Just to recap, here are my tips for a great data science presentation:

• Practice, practice, practice
• Talk about what you know
• Look yourself in the mirror and say “I’m not an impostor”
• Put yourself in the shoes of an audience member
• Play your game, not anyone else’s
• Set realistic expectations, strive to meet them, and hold yourself accountable
• Take audience feedback and questions seriously, and use them to learn and improve

Do you have any tips for giving a great talk? If so, go ahead and leave them in the comments below!

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How to Present a Data Science Project (With Examples)

After passing a company’s take-home challenge, you might get asked to present your data science project to data scientists and the hiring manager. Presentations are high-pressure, especially if public speaking is not a strong skill for you.

Fortunately, making your data science presentation more engaging (and using it to land you the job) is a straightforward process.

In this article, we’ll discuss how to present a data science project and share tips to help you overcome these challenges and land your next data science job.

Data Science Presentations: Where to Start

Let’s first discuss the elements of your data science project that you will need to start preparing the presentation.

Learning the Purpose of the Project

For your own purposes, make sure you grasp the problem or question your project addresses. This helps set the stage for your audience, showing why your work matters. What might work for you is outlining the specific objectives you aim to achieve. This could include solving a particular business problem, making a prediction, or uncovering patterns in data.

Brainstorming the significance of the project’s outcome usually enables you to discuss its benefit to the business and community in the presentation. It also allows you to highlight the value of your findings. This might include cost savings, improved efficiency, new insights, or strategic advantages.

These are the things that the interviewers desire in data science project presentations, but candidates often overlook the details.

Identify Your Audience Type

To present an excellent data science project, you must first identify your audience type. While it may not be possible for you to know all about your audience, you should at least try to find out if your audience is familiar with data science concepts. And that information must influence how you present your findings. Consider who will benefit from your findings. Are they business executives, data scientists, or a general audience?

Tailor your content accordingly. For a technical audience, use more technical jargon, include detailed methodologies, and focus on the specifics of your data analysis. For others, simplify the language, avoid overly complex explanations, and focus on the implications and actionable insights. However, when in doubt, always embrace the simpler approach.

Focus on Relevance

You’ve already determined the purpose of your project; now, carefully incorporate it into the presentation by focusing on the relevance of your findings. Ensure your presentation aligns with the strategic goals or needs of the organization. Make sure it answers how your conclusions address the key issues or objectives and how they apply to real-world scenarios or business decisions. This helps in making your findings more relatable and impactful.

Furthermore, highlight the most relevant insights from your analysis. Emphasize the actionable takeaways for your audience. Use charts, graphs, and visualizations to make complex data more accessible and to highlight key points.

Questions Related to Your Project

You’ll likely be subjected to thorough Q&A during interview sessions regarding your data science presentations. Consider potential questions your audience might have regarding your methodology, data, or conclusions. Be ready to explain any aspects of your project that may be unclear or complex. This includes discussing limitations, assumptions, or alternative approaches.

Many data scientists overlook it, but fostering an environment where the audience feels comfortable asking questions can provide additional insights and demonstrate your expertise. Use questions as a feedback mechanism to gauge understanding and adjust your presentation if necessary.

What to Include in Data Science Presentations

Now that we’ve covered the foundational components of a successful data science project presentation, let’s discuss what your presentation should include:

Start by briefly stating the project’s objective, what are you going to cover, and its importance. Provide a roadmap for your presentation by outlining key sections to help your audience follow along and give a brief context about the industry or domain where the problem arises. This helps set the stage for why your project is relevant.

If you’re using PPTs (more on this later), a title slide, the purpose of the presentation, and a brief agenda should be discussed in the first few slides.

Problem Statement

Clearly describe the problem or question you are addressing. This should be specific and actionable. Explain why this problem is important and what impact solving it would have. This would help underscore the value of your data science project. The context and background of the problem statement should be clearly defined.

Moreover, state the objectives of your analysis. Discuss what you aim to achieve through your project. This can include solving a business problem, improving a process, or generating insights. An industry overview in the presentation often helps in better understanding the problem statement and your approach.

Data Source and Acquisition Methods

Thoroughly detail the sources of your data. Mention if they’re internal or external databases, APIs, or surveys. For technically savvy audiences, discuss whether the data is structured or unstructured. Explain why these sources were chosen and how they are relevant to your problem. Moreover, describe the methods used to collect the data. Was it through scraping, downloading, API calls, or manual entry?

Briefly outline any preprocessing steps taken to clean and prepare the data for analysis. Interviewers also love to know how you handled the missing values. Mention it just enough for them to ask about it, allowing you to showcase your data science knowledge.

Consider mentioning the initial insights you found while normalizing and transforming data. You could also attach a sample of the datasets in your presentation, especially when it comes to visual datasets.

Methodology and Model Selection

Methodology is critical when it comes to data science projects with source code . Explain the overall approach you took to address the problem. This might include exploratory data analysis, feature engineering, or hypothesis testing. Feel free to describe the models or algorithms used. Mention why you chose these particular models, any comparisons made, and the rationale behind your choices.

Furthermore, outline how you validated your models and which metrics you used to assess their performance (accuracy, precision, recall, F1 score). Let your interviewers know about any cross-validation or testing procedures used to ensure robustness and generalizability.

Results: Your Findings

For your data science interviewers, this is the most significant section of your presentation. Make it count by presenting the main findings of your analysis. Use clear visuals such as charts, graphs, and tables to illustrate the results. Highlight any significant insights or patterns discovered. This is where you make the data come alive and show its value.

If possible, visually generate a comparison of different models on the same dataset. Be sure to use ROC curves and AUC to solidify your arguments. Moreover, don’t forget to discuss the implications of your findings. Thoroughly discuss how they address the problem statement and may influence the business or the industry.

Don’t hesitate to include any unexpected results you found during the project. Present them in a compelling way to show the interviewers you genuinely worked on the project and found discrepancies.

Interpretation and Recommendation

Provide a detailed interpretation of the results in your data science project presentation. Discuss what they signify in the context of the problem, and relate your findings back to the real-world problem and the project’s objectives.

Be sure to offer specific recommendations that align with the interests of the company or the industry, and provide strategic advice, if applicable. Mention how the insights can be leveraged for better decision-making or workflow improvement.

Challenges and Limitations

Discuss any challenges or obstacles you faced during the project. This could include data quality issues, computational constraints, or unexpected findings.

Acknowledge the limitations of your analysis, including factors that impacted the accuracy or generalizability of your results. Likewise, mention any assumptions made during the analysis and how they might have affected the results.

Summarize the key points of your presentation. Reiterate the problem, findings, and recommendations, and provide any concluding thoughts or reflections on the project.

Introduce a call to action. Suggest the next steps or actions to be taken based on your findings. This might include implementing recommendations, conducting further research, or making strategic changes.

How to Present Your Data Science Project

Now we’ve come to the most anticipated part of the article, addressing something most beginner data scientists wonder about where to showcase their projects when applying for a company or presenting to interviewers. Let’s discuss:

DataLab is a great place to share your work because it lets you create interactive reports. You can include live code, charts, and explanations in one place, making it easy for others to see what you did and how. If you want to show off your coding skills and make your analysis look super polished, DataLab is a solid choice.

However, it mostly relies on the AI capabilities of the platform and allows very limited control over your projects.

GitHub is the go-to for code sharing and version control. It’s where a lot of developers and data scientists put their work. By posting your projects on GitHub, you can show off your code, documentation, and how you keep everything organized. Plus, having a well-managed GitHub profile can make you look professional and detail-oriented.

Kaggle is a bit like a playground for data scientists. It’s great for showcasing your skills through competitions and public notebooks. If you’ve tackled a tough dataset or participated in a challenge, Kaggle lets you share that with the community. It’s a cool way to get noticed and get feedback from other data science enthusiasts.

Kaggle also has a vast array of datasets to build your data science projects.

Personal Website and Slides

If you’re seeking freedom to present your work exactly how you want, personal websites and slides give you exactly that. A personal website is like your own online portfolio where you can show off detailed project descriptions, interactive demos, and more.

Slides, however, are perfect for summarizing your project in a neat, easy-to-follow format, especially useful for interviews or presentations. Many current presentation tools come equipped with AI capabilities to make the job easier for data scientists.

More Tips for a Data Science Project Presentation

As you build your presentation slides and rehearse, here are some of the best practices and tips to make your performance even stronger:

Keep it concise - Keep your presentation simple and to the point. You can’t show every step you took. Instead, keep it brief and to the point, focusing only on key details.

Choose your best visualizations - Images and charts make your presentation easier to follow and clearly display the impact/findings of your project. Include only vital information in the chart, and be sure to consider fonts, color theory, and other good practices of visualization design . A general rule of thumb : It should be clear to a layman what a chart is conveying.

Focus on the impact - If you’re presenting on a project from a previous job, show the impact it had using metrics. Increased revenue, reduced churn, customer acquisition, and other factors will illustrate how your work impacted the bottom line.

Include limitations - Every project has limitations and challenges. Although it might seem counterintuitive to talk about what went wrong, discussing limitations will make your presentation stronger. It shows you can identify potential flaws in reasoning and that you care about quality controls.

Talk through your decisions - Explain why you made the technical decisions you did. This will help the audience understand your approach, what factors lead to you making a certain decision, and how you personally use creative problem-solving.

Make it accessible - Explain the technical details of your project in layman’s terms. Examples and analogies can be helpful for audiences, and ideally, you should be able to explain an algorithm or complex data science technique in one or two sentences for a non-technical audience.

For the Presentation: Final Tips

Public speaking is nerve-wracking. But there are strategies you can take to calm your nerves and make the most of your presentation time. Here are public speaking tips for your data science presentation:

Make eye contact - Eye contact connects you with your audience and makes your presentation more engaging and impactful. One strategy: sustain eye contact with one person per thought. Be sure to practice this during your rehearsals.

Allow space for questions - Although there’s usually a Q&A at the end, questions can come up throughout. If you’re not sure if the audience has questions, take a pause and ask, “Does anyone have any questions?” Remember, you don’t want to talk AT them.

Avoid rushing - Focus on pacing. You should be talking at a normal conversational speed. Too fast, and you’ll end up losing the audience. Too slow, and you will bore them.

Breath, relax, and collect your thoughts - Before you begin, take some deep breaths. One strategy: reframe the focus from you (e.g., “What if I blow it?” ) to the audience ( “My focus is helping the audience understand and learn.” ).

The Bottom Line

Ensure that your presentation is tailored to the audience, is relevant, and provides actionable insights understandably and appealingly. Also, be prepared to handle post-presentation questions related or tangential to the project and your associated experience.

If you’re looking for more projects to tackle, we’ve got them at Interview Query:

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A Gentle Introduction to Data Science Presentation (Storytelling)

Data Science storytelling or data science presentation is one of the most underrated skills a data scientist can have. It is barely touched in most data science courses and programs. The importance of describing a compelling story about the results of an analysis is critical for a data scientist or data analyst. In fact, as a data scientist, more often than not, you will be working with people that do not know data. Consequently, you should be able to convey your story in a manner that is adequate to whoever is listening.

In this article, I will not go through any type of technical way of solving a data-related problem. However, I will try to review several data storytelling habits that would help you better present your analysis.

What is the problem of data presentations (storytelling) in Data Science?

Talking to the client is not the easiest of things. In fact, most of my data scientist colleagues do not particularly like talking to clients. It is, believe it or not, somewhat of a meme. 

The result of someone who does not know how to present is a boring presentation filled with numbers. The result of having a boring presentation i s a potential loss of clients. Indeed, if a client did not understand what you presenting in a report or during a presentation, he may feel that he did not derive enough insights to make a decision. Consequently, it may render your analysis useless even though you had some valuable insights

The importance of narrating a compelling story is critical to the success of the analysis work you did. Indeed, it is essential for the person you are communicating with to understand the main points of your analysis. Although you can impress them with your knowledge through your complicated algorithms, the results of all said algorithms are not always what should be reported to answer a person’s business question.

What are the goals of data analysis storytelling?

When you prepare your report or presentation, there should be 3 main goals you should attempt to achieve:

• Answer the business questions/ requirements presented at the beginning of a project.
• Illustrate knowledge, information, and insights derived from the analyzed data.
• Narrate a story in a simple way that contains valuable and relevant result-focussed recommendations.

Keeping those goals in mind during your data analysis presentation will allow you to drive value by delivering and presenting a compelling analysis. In fact, how you deliver a message extracted from the data can even be more important than what the data says.

How do you ensure a positive data science presentation?

Understand that if you go beyond what is common knowledge during the report/presentation, you present a complicated concept, or even if you show negative business performances, your presentation is likely to be disputed. We are all humans after all, we like to be correct even if we are wrong. People need to UNDERSTAND WHAT YOU ARE SAYING . I cannot emphasize that enough. Therefore, it is your job as the data analyst/data scientist to present the data in the most humanistic way possible. Indeed, you have to bring a context to your analysis and understand how your results affect human emotions, motivation, or organizational behavior.

It is not advisable to blast people with numbers, 30 pages reports, and 100 pages full of irrelevant charts and graphs. Do not make the mistakes of presenting just data visualizations. After 10/12 slides, people will stop listening. Therefore, you have to construct a narrative through the results of your analysis, or even better, tell a story. Tell your clients what they should remember from your analysis and how they should use your insights to make decisions. You have to create a piece that is so valuable that they come back to it, just like your favorite book.

How do you deliver a data science presentation/ data analysis report?

In this section, we are going to go through the process you should take to deliver a compelling data analysis presentation (data science presentation). You have to understand that every data analysis report will be different. However, you can follow these simple guidelines to create a compelling story from your data analysis. Consider applying the following guidelines in your next report.

1- Always answer WHY your client should care about the data/insight you are reporting 

 Before creating the report/presentation, ask the question WHY SHOULD THEY CARE? Indeed, knowing the why is primordial to telling a compelling story. So you have to identify why the story or section you are presenting has occurred and why it is important to present it. If you cannot answer the why then consider scrapping said part from your data science presentation.

Remember, business people are busy people and they always need context when reporting your data analysis results. You should give them a reason as to why they should care about every section you are presenting. If you can’t answer the why then that insight is probably a filler and should be removed.

2- Present challenges and bring solutions

Introduce the challenges you are providing insights for and explain the costs of not taking action . Furthermore, you have to frame the business issue that the analysis or part of the analysis solves and suggest recommendations on how it may be fixed or improved if possible. This will eliminate any confusions that may arise.

3- Explain the general data settings.

For your interlocutor to get a sense of the data set, do not forget to mention errors, date range, omissions … You always have to let people know in advance what you are talking about . For example, you can say something like “the conclusions are based on the year X. We did not consider any value above Y . ” This way, you shareholder knows under what circumstances you draw your conclusions. Again, for this step, keep it general as mentioned in the previous point. Your client does not need to know what Python function you used to omit certain values. Unless he/she asks, of course.

4- Humanize the data.

Try to use examples and scenarios to give context and humanize the data. Especially if you are explaining complicated concepts. Indeed, creating aliases, settings and abstractions allow you to not only simplify the problem you are presenting. But as well to reduce the risk of offending your stakeholder if you find any negative performance in the data . 

5- Emphasise important events.

 Do not be afraid to stop and emphasize the important events to help you demonstrate your data science presentation . For example, if you used external data that had an important impact on the analysis/results, do let your stakeholder know how that piece of information affects them. Consequently, emphasizing on important events goes a long way to help clarify and simplify your analysis.

6- Speak in simple terms.

You are not Shakespeare. Do not need to impress anybody with marvelous and complicated vocabulary. Keep your words simple . Speak to help people understand. Do not confuse them because certain jargon can be extremely confusing. I did not take a class in college because it contained “ Stochastic Processes “. Why didn’t they simply use “Random Processes”? So, remember, try to make your presentation and writings as simple as possible.

7- Use Pictures.

One of the most famous sentences about pictures. A picture is worth a thousand words. Use pictures to illustrate your points. There are multiple types of graphs that you can use in various situations. They vary from bar charts, histograms, trendlines, scatter plots, and many more. The only thing to be careful, do not create too many useless and repetitive charts. I have written a guide on charting that can help you with creating and illustrating your results through charts.

8- Share your thoughts and actions for a successful or improved project.

Do not be afraid to tell your shareholders what is required for a project to be successful or a result of analysis to be usefull . Use clear verbose and descriptive nouns. For instance, if you need data from somewhere, tell them, “ To get this level of accuracy, we need daily access to Xs data through an API “. 

9- Communicate the cost of not taking an action

 Tell them the n egative aspects of not taking any actions towards the implementation of a solution . Indeed, you have to state how important a piece of analysis is, and contrast the financial repercussions of doing nothing against doing something . For example, say that you find a relation between the website speed and the page load time. You found that a series of images is causing them. What you can do here, you can be like “leaving the page like this make you lose x$ amount of money, whereas hiring a web developer to improve the page can cost you y$. But in the long run, I am t% confident that making the changes will help you gain x$ after z year “. This sentence can obviously apply in many scenarios

10- Share your Recommendations during your data science presentation.

Always finish with a series of recommendations that can bring value to the person you are presenting to . Remember you are generating value, ergo since you have worked on a project for so long, you are the most suited to give recommendations on potential improvement. Do not be scared if you are not the most knowledgeable or experienced person in the room.

More often than not, y ou will be working with people with more experience than you in a particular field . However, the theory learned in your training can be applied to any type of data, and you should be able to give them insights that can help said-experts, to make better decisions. Do not forget the previous point. Your recommendations should be made clear, straightforward, and backed with data. You should be able to give a logical answer to any questions regarding something you recommend.

To wrap up.

These are the things to keep in mind to improve your data science presentation. Data science presentations, or data science storytelling, are not easy, especially for people that are not used to it. So, next time you have a presentation, keep the above recommendation in mind and, do not be afraid to try them. They should make your presentation more compelling, and you will be able to get your point across.

If you are scared that your presentations or report is too long or you feel like your shareholders are not getting enough value from your presentation, try giving some of these techniques a try next time you present, and let me know how it went. Do you have any additional tips on how to help people with their data presentation, share them in the comment section down below.

If you made this far in the article, thank you very much.

I hope this information was of use to you. .

Feel free to use any information from this page. I’d appreciate it if you can simply link to this article as the source. If you have any additional questions, you can reach out to [email protected]   or message me on Twitter . If you want more content like this, join my email list to receive the latest articles. I promise I do not spam. 

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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20+ Free PowerPoint and Google Slides Templates for Data Presentations

Graphs and diagrams are crucial in data presentations since they make complex information much more understandable . Imagine copying and pasting all 1,000 rows of data onto your slides and expecting your audience to understand it. It’s really hard, isn’t it?

Presenting your data analysis doesn’t have to be a struggle. These PowerPoint and Google Slides templates will significantly cut down your preparation time, allowing you to focus on ensuring the accuracy of your data analysis while we handle the design.

This article is divided into two sections: the first covers our free PowerPoint templates , and the second covers our free Google Slides templates . Oh, and in case you’re wondering, yes, you can use a PowerPoint template in Google Slides and vice versa .

PowerPoint Templates for Your Data Presentations

Let’s start with our data presentation templates in PowerPoint. 

As you may know, PowerPoint is one of the best presentation software programs available today. So, take advantage of all its features with our free templates! 

1. Playful Venn Diagram PowerPoint Template

Venn diagrams show the similarities and differences between 2 or more data sets. Your audience can tell if there’s anything familiar between them just by looking at the diagram.

Likewise, if you want to emphasize the differences between data sets, Venn diagrams are great for that purpose, too. Now, for this template pack, you’ve got 10 slides to choose from. Pick your favorite!

2. Graph, Diagram, and Data Sheet PowerPoint Template

Using graphics is the best way to create data presentations, and at 24Slides, we know that! 

If you’re looking for simple yet creative graphs, including a Gantt Chart in PowerPoint , this 5-slide template pack is perfect for you. Take a look at the previews and download the pack for free!

3. Generic Data-Driven PowerPoint Template

Here are more basic graphs for your presentation decks. This template can be used for many situations, including a job interview, a sales presentation, or even an academic one.

If you want to make the slides look even more unique, you can customize the background with some personal images.

4. Cockpit Chart PowerPoint Template

If you’re giving a high-level presentation to decision-makers who need to see complex data and proper analysis, then this free template pack is for you.

With this pack, each of the 9 slides brings a fresh example of charts and diagrams, ready to make your data come alive. Click on the title and pick the perfect one to captivate your audience!

5. Matrix Chart PowerPoint Template

A matrix chart allows you to compare and analyze different sets of data. You can use it to prove certain data sets are related. Plus, you can even show the strength of that relationship. 

Download our 8 matrix models for free now! 

6. Stair Diagram PowerPoint Template

Like their namesake, stair diagrams show steps or progression in data presentations. You can use good, old-fashioned bullet points, but it won’t be much fun. 

This template offers 10 stair diagrams; the screenshot above shows a steps stair diagram . Explore all of them for free!

7. Tables PowerPoint Template

Tables have been a staple in data visualization for a long time, and we believe they continue to be widely used today. Despite the evolution of various visualization tools and techniques, tables remain a fundamental way to present data clearly and effectively.

This template pack offers standard table slides as well as creative designs, including a subscription slide, a table with different symbols, and a matrix organizational structure. Choose your favorite based on your needs!

8. Flow Chart PowerPoint Template

Flowcharts are handy for documenting specific company procedures. They can even present the company hierarchy and who is responsible for certain tasks. 

Instead of verbally discussing processes, why not try using a flowchart? 

9. Financial Pie Graphs PowerPoint Template

Whether you’re presenting in front of the directors of your company or potential investors for your startup, these radial charts will help you get your point across. With a few clicks, you can customize these resources and make them your own!

This data visualization template includes 3 slides: a financial pie chart for comparison (shown above), a ring pie chart, and a doughnut pie chart slide.

10. Research and Development Data PowerPoint Template

Every successful startup needs a solid research and development (R&D) process, which can be lengthy and costly and often require external funding. 

This template pack is designed to help you create a concise, impactful presentation for potential investors. Remember, while design is important, your passion and persuasive skills will ultimately drive your success in a data presentation!

11. Sales Report PowerPoint Template

Our list of data presentation templates wouldn’t be complete without a sales report template in PowerPoint. 

This pack includes sales bar charts, line charts, radial charts, sales data visualization sections, and annual sales report slides. Everything you need in one presentation deck!

12. Data-Driven PowerPoint Template

This 9-slide template pack contains charts and diagrams for your business presentations or any project you lead. 

With its thoughtful design and diverse range of graphs, this template is perfect for most financial presentations. So, what are you waiting for? Check out our template pack now!

13. Block Chain Data PowerPoint Template

Cryptocurrency and blockchain are all the rage nowadays. Many people became millionaires overnight, but many more gambled and lost their entire life savings!

Don’t get left behind and explore more about digital currencies with our free template pack.

Google Slides Templates for Your Data Presentations

PowerPoint is awesome, but Google Slides is also a brilliant tool. If you haven’t used this platform, this is your signal to start doing so. Unlock the potential of your data with our free templates, crafted to transform your slides into stunning visual stories!

With Google Slides templates, there’s no need to download anything to your computer. Simply create an account on our Templates Repository and make a copy of the template. As you can imagine, editing it will be a breeze!

1. Corporate Data Presentation in Google Slides

Our Google Slides template provides essential charts for data presentation, including bar charts, pie charts, and line charts. 

The best part? Each chart is linked to a Google Sheets spreadsheet, giving you complete control over the data.

2. Life Cycle Diagram in Google Slides

A product’s life cycle—spanning from introduction to growth, maturity, and decline—directly influences your company's marketing and pricing strategies. So, you have to know how to monitor each stage.

This template pack includes a summary slide to introduce your objectives and guide the audience. It also features an area chart to visually represent product growth over time, helping to clarify the current stage. See it yourself by clicking on the title!

3. Playful Pie Chart in Google Slides

Unlike the other pie charts in this article, this one will be straightforward to use. You’ve got 8 pie chart slides to choose from, including 3D and 2D pie charts in Google Slides. 

Choose the ones that best convey your message, then edit and present them!

4. Dashboard Template in Google Slides

A dashboard slide can convey everything your audience needs in just one slide. While you can use separate slides for each chart, it won’t have the same impact as a dashboard (as you can see in the image). 

Dashboard templates are perfect for elevator pitches because they are highly eye-catching. Explore the designs we’ve prepared for you!

5. Waterfall Diagram Template in Google Slides

Waterfall charts are excellent for financial presentations, allowing you to show gains or losses over time. They are also helpful in demonstrating changes in cash flow or stock market performance. 

This template pack includes a waterfall performance comparison slide (pictured), a basic waterfall diagram, a project timeline slide, and more. Download all for free!

6. Playful Data-Driven Template in Google Slides

Do you think data presentation templates have to be serious? Think again! 

This 10-slide playful template is packed with various charts and graphs, including bar graphs, radar charts, waterfall statistics, treemaps, and more. Log in to our Template Repository to download this free Google Slides template.

7. Circle Diagrams in Google Slides

This template pack features 8 types of circle charts in Google Slides, including pie charts, timelines , cyclical processes, project management charts, and Venn diagrams. 

The design is both playful and professional, making it suitable for any audience!

8. Creative Data-Driven and Financial Charts in Google Slides

Number crunchers will love the clean design of these 7 data-driven slides. With ample white space and visually appealing graphics, it will help your audience grasp complex financial information. 

You only need to replace the placeholder content with your own information and practice your data presentation for the best results!

9. Graph, Diagram, and Data Sheet Presentation in Google Slides

This pack of 5 Google Slides templates includes a versatile collection of charts and diagrams, perfect for any presentation. 

Remember that each chart is fully customizable to meet your specific needs. Download this data visualization pack for free today!

10. SWOT Presentation Templates in Google Slides

Data visualization isn’t just for numbers; it also includes qualitative data. If you need to present a SWOT analysis, these templates are your go-to solution. 

With 8 pre-designed SWOT diagrams, you can easily create impactful presentations. Best of all, they’re free to download—what are you waiting for?

11. ICO Presentation Template in Google Slides

Planning to present an Initial Coin Offering (ICO) for your company or startup? 24Slides has you covered.

We’ve designed this data presentation template with the unpredictable nature of digital currencies in mind, featuring a chart that helps you clearly explain all the details to your audience.

12. Budget Presentation Template in Google Slides

Presenting a project’s budget doesn’t have to be boring!

This resource offers 8 different diagrams in Google Slides, making it easy to streamline your design process. Download our data visualization pack for free now! 

13. Financial Template Pack in Google Slides

You should know that effective financial management is crucial to every business’s success. So why not showcase that professionalism in your financial slides? 

Explore this final Google Slides template pack and impress your audience with professional and polished data slides!

I hope these 20+ free PowerPoint and Google Slides templates for data presentations are helpful for any project you have in mind. Our templates are designed to be visually attractive while maintaining a professional look. Follow us and stay tuned for all the content we’ve prepared for you!

Where can you find the best templates for FREE?

In 2024, it’s no mystery that there are various ways to optimize your time when designing presentations. One of the most effective methods is using pre-designed templates, and of course, 24Slides has its own repository.

When you enter our Template Repository , you’ll find data visualization templates, marketing templates, portfolio templates, planning templates, and much more!

It’s time to work smart, begin today .

If you like this content, you should check:

• Mastering the Art of Presenting Data in PowerPoint
• 20+ Self Introduction PowerPoint Templates: Download for free!   
• The Ultimate Brand Identity Presentation Guide [FREE PPT Template]
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Data Science Powerpoint Presentation Slides

Analyze and understand actual phenomena with data by using this Data Science Powerpoint Presentation Slides. Utilize this big data PPT visual to showcase the interactive social media platforms like Google, Facebook, Twitter, Youtube, etc. Take the assistance of this structured data PowerPoint graphics to explain the cloud storage which provides business with real-time information and on-demand insights. Also, present the web services that constitute big data that is widespread and easily accessible. Showcase interrelated computing devices that have the ability to transfer data over the network without requiring a human to computer interactions with the help of this data mining PPT slides. Mention some databases like oracle, SQL, Amazon, etc that are used to drive business profits by taking the assistance of this machine learning PowerPoint templates. You can also mention data warehouse applications such as Teradata, IBM Netezza, etc that are used for data analysis. Download this information science PPT presentation to understand the data processing methods.

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PowerPoint presentation slides

Enhance your audience's knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of a total of twenty slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs, and charts for your convenience. The PPT also supports the standard (4:3) and widescreen (16:9) sizes. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the color, text, and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Data Science Powerpoint Presentation Slides complete deck.

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Content of this Powerpoint Presentation

Data science blends statistical analysis, machine learning, and vast datasets to unlock unprecedented insights and opportunities in modern business. This multifaceted discipline enables organizations to harness (customer/campaign/project) data to transform it into actionable intelligence. These data-driven insights navigate decision-making, predict market trends, and personalize customer experiences. 

Data science can help businesses optimize operations to innovate quickly and gain a competitive edge. Extracting and analyzing data and communicating complex findings in a digestible format is part of data science. It is a complicated concept to understand and handle. This is where our data science presentation templates can help.

Click here to view our Top 10 Data Science Templates for Better Decision-Making !

Data Science Templates

SlideTeam's pre-designed data science templates are expertly designed to impart a comprehensive understanding of data science. With their 100% customizable nature, these PowerPoint Layouts provide users with the desired flexibility to create and edit a simple, easy-to-follow data science presentation from scratch.

The slides break down complex data science into smaller, easy-to-understand components like big data sources, technologies, repositories, etc. This deck provides a structured approach to presenting intricacies like big data analytics, cloud storage advantages, IoT connectivity, etc.

Are you ready to embrace the power of data science? Discover our Top 10 Data Science Framework Templates with Examples and Samples today!

Use these content-ready slides to create a well-structured presentation on data science with a minimum investment of resources and time.

Template 1: Media

This slide underscores the significance of Media as a rich repository for big data within the realm of Data Science. The PPT Template highlights the Media's role in capturing and reflecting consumer preferences and trends, including social media and interactive media platforms such as Google, Facebook, Twitter, YouTube, and Instagram. It also mentions generic media types like images, videos, and podcasts used for big data collection. Use this template to share analytical, quantitative, and qualitative user interaction insights to comprehend and predict user behavior.

Template 2: Cloud

The presentation layout highlights the role of cloud technology in Data Science. It helps to store, process, and analyze big data. This helps to provide real-time business insights and on-demand analytics. The template helps to focus on the attributes of cloud computing, such as its flexibility and scalability, which are essential in managing data-driven applications' vast and variable demands. Use it to facilitate a seamless, scalable, and efficient data infrastructure in a data science presentation.

Template 3: Web

The Web is a vast, accessible source of big data that is crucial for gathering real-time insights and trends. This template highlights the Web's capacity to house expansive datasets and flexibility for different applications. The slide underscores the Web's vastness and accessibility by providing a schematic representation of its infrastructure. It highlights the wealth of information that can be harnessed for analysis, from structured data to user-generated content. This slide will help data professionals and educators in data Science presentations demonstrate the integral role of web-based data in driving analytical decisions and strategies.

Template 4: Internet of Things 

The Internet of Things (IoT) generates vast quantities of machine-created data and contributes a significant stream of big data from an array of connected devices. The presentation slide outlines the IoT landscape and shows how sensors integrated into electronics yield a continuous flow of information with real-time analytics and insights. This template highlights the breadth and depth of data IoT offers and its potential for predictive analytics, trend analysis, and enhanced decision-making. It can be a critical resource for data scientists and analysts illustrating the expansive data networks created by IoT and their impact on modern data-driven strategies in data science presentations.

Template 5: Social Influencers

Social influencers in Data Science play a crucial role in interpreting and disseminating complex data insights to a broader audience, often influencing public opinion and decision-making processes. This slide centers around the multifaceted nature of social influencers, encompassing review-centric sites like Apple's App Store and Amazon, editor posts, analyst reports, and user forums. It highlights the network of diverse platforms that shape public discourse, like Yelp-style reviews, Twitter and Facebook interactions, blog comments, etc. This template illustrates the extensive impact of social influencers on big data analysis, especially regarding consumer sentiment and trend analysis. It will help data scientists, marketers, and business strategists understand and share social insights for informed decision-making.

Template 6: Activity Generated Data

In Data Science, activity-generated data is crucial for understanding user behavior, preferences, and patterns. This presentation layout illustrates the multifaceted nature of activity-generated data generated from computer and mobile device logs, web interaction trails, sensor data, and information processors in everyday technology. It portrays how these data points converge into a substantial digital footprint, underpinning analytical models and algorithms. Use this slide to refine predictive models and enhance decision-making processes.

Template 7: Data Warehouse Appliances

Data Warehouse Appliances streamline complex data analytics. These tools allow for the efficient processing and analysis of vast datasets by aggregating transactional data that is ready for analysis. This PPT Slide presents an overview of top-tier Data Warehouse Appliances like Teradata, IBM Netezza, and EMC Greenplum, which excel at collecting operational system data. The design suggests these appliances can enhance and expedite outcomes from Big Data implementations. It will set the stage for how these data warehouses can significantly optimize and reduce the processing time in a Big Data ecosystem, leading to quicker, more informed decision-making.

Template 8: Big Data Sources

Big data sources are the lifeblood of Data Science, providing the raw material from which valuable insights and strategies are derived. This PowerPoint Layout offers a comprehensive map of the varied sources that fuel Big Data analytics, including Network and In-Stream Monitoring Technologies, Data Warehouse Appliances, Activity Generated Data, Social Network Profiles, and more contemporary sources like the Internet of Things. Each element represents a data stream that, when harnessed, can yield crucial information for business intelligence. Use it to illustrate the ecosystem of Big Data and the importance of integrating diverse data streams to construct a holistic analytical framework.

Template 9: Network and In-stream Monitoring Technologies

Network and In-Stream Monitoring Technologies ensure real-time data integrity and security in data science. This PPT Design gets into the nuances of these technologies and highlights key components, such as Packet Evaluation, Distributed Query Processing for Application-like Applications, and Email Parsers. These elements are essential tools in the monitoring process, enabling the analysis and management of data traffic. It also helps in the optimization of data queries across networks and the structuring of unstructured data from communications. The presentation slide will help showcase how monitoring technologies underpin the stability and efficiency of data analysis platforms.

Template 10: Legacy Documents

Legacy documents are repositories of historical data and are vital for comprehensive analysis. This slide highlights the significance of these documents and identifies key types: Archives of Statements, Insurance Forms, Medical Records, and Customer Correspondence. Each represents a segment of data that, when leveraged, can provide insights into past trends and patterns essential for informed decision-making. This template emphasizes the need to incorporate legacy data for a robust analytical framework and as a bridge between past information and future predictions.

Our Contribution!

Data science presents challenges, from managing voluminous data sets to interpreting complex analytical results. SlideTeam's Data science presentation templates help share the nuances and components of this intricate concept in an easy-to-grasp format. By leveraging these templates, one can distill the complexity of components like data analytics, cloud, Web, legacy documents, etc., into strategic narratives. These digestible stories will resonate with stakeholders, streamline the communication flow, and facilitate data-driven decisions.

PS . Data science dashboards enable companies to transform raw data into strategic initiatives by revealing trends, patterns, and insights. Click here to read more.

Data Science Powerpoint Presentation Slides with all 20 slides:

Use our Data Science Powerpoint Presentation Slides to effectively help you save your valuable time. They are readymade to fit into any presentation structure.

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Data Science Consulting

It seems that you like this template, data science consulting presentation, free google slides theme, powerpoint template, and canva presentation template.

Do you want a high-impact representation of your data science consulting company? Don’t hit the panic button yet! Try using this futuristic presentation to promote your company and attract new clients.

This template has a consulting sales pitch structure. The background is sober: navy blue is the main color, which is combined with square shapes that seem to be falling—that look like large amounts of numbers being analyzed by a computer. They immerse yourself in a very technological and futuristic environment, perfect to represent the data or infographics related to your consulting company. The techy title typography completes the look with condensed and elongated letters, creating a great impact in your audience.

Features of this template

• A tech template with square shapes and futuristic vibes
• 100% editable and easy to modify
• 23 different slides to impress your audience
• Available in different colors
• Contains easy-to-edit graphics, maps and mockups
• Includes 500+ icons and Flaticon’s extension for customizing your slides
• Designed to be used in Google Slides, Canva, and Microsoft PowerPoint
• 16:9 widescreen format suitable for all types of screens
• Includes information about fonts, colors, and credits of the free resources used

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The NASA GeneLab Analysis Working Group has collaboratively generated four complementary datasets to interrogate changes to the gut microbiome of mice due to spaceflight. The four datasets use different technical approaches to determine which microbes and microbial genes are present within feces isolated from space-flown mice and corresponding ground controls.

While one of these datasets was generated by the GeneLab sample processing team, the rest were generated in collaboration with Dr. Stefan Green and Kevin Kuntsman at the University of Illinois Chicago and the GeneLab Microbial Analysis Working Group. In future work, the GeneLab Analysis Working Group will analyze this data to understand how gut microbiome may change during spaceflight as well as the relative value of each assay utilized. This will inform future data generation issues.

https://genelab-data.ndc.nasa.gov/genelab/accession/GLDS-249

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Pfizer Highlights Diverse Oncology Portfolio and Combination Approaches at ESMO 2024

• More than 10 oral and mini-oral presentations span Pfizer’s extensive Oncology portfolio of approved and investigational therapies
• Two late-breaking presentations include longer-term results from BRAFTOVI ® + MEKTOVI ® PHAROS study in BRAF V600E-mutant metastatic NSCLC and new Phase 2 ponsegromab data in cancer cachexia
• Encouraging early results for PD-L1 vedotin ADC, disitamab vedotin and the novel combination of CDK4 + CDK2 inhibitors highlight rapidly advancing pipeline

NEW YORK--(BUSINESS WIRE)-- Pfizer Inc. (NYSE: PFE) continues to showcase potential practice-changing research and next-generation candidates across its robust Oncology portfolio at the European Society for Medical Oncology (ESMO) Congress 2024, being held September 13-17 in Barcelona. Data from more than 50 company-sponsored, investigator-sponsored and collaborative research abstracts, including more than 10 oral and mini-oral presentations, will be presented across the company’s tumor areas and core scientific modalities, as well as a potential treatment for a cancer-related condition.

“At this year’s ESMO, we are looking forward to demonstrating our progress toward delivering next-generation biologics and novel combinations that have the potential to be new standards of care for patients,” said Chris Boshoff, Chief Oncology Officer and Executive Vice President, Pfizer. “Our key data presentations highlight our scientific leadership in developing targeted therapies, including small molecules and antibody-drug conjugates, across our core tumor areas, including breast, bladder and thoracic cancers.”

“At ESMO, Pfizer will share important data highlighting our commitment to transforming outcomes for patients living with lung cancer, including longer-term follow-up results from the BRAFTOVI + MEKTOVI PHAROS study in BRAF V600E-mutated metastatic non-small cell lung cancer,” said Karin Tollefson, Chief Oncology Medical Officer, Pfizer. “We are also looking forward to sharing progress on our industry-leading pipeline of new molecules, including encouraging early results for two novel, investigational antibody-drug conjugates and preliminary data on a novel combination of Pfizer’s next-generation CDK inhibitors.”

Key research includes a late-breaking presentation of updated results from the pivotal Phase 2 PHAROS* study of BRAFTOVI (encorafenib) + MEKTOVI (binimetinib) in patients with BRAF V600E-mutant metastatic non-small cell lung cancer (mNSCLC). Longer-term efficacy and safety data will be presented, following the initial primary overall response results (ORR) that supported the FDA approval for BRAFTOVI + MEKTOVI in this indication in 2023 and the recent approval by the European Commission in August 2024. Further, Pfizer will share updated data from the safety lead-in of the ongoing Phase 3 BREAKWATER trial, showing antitumor activity of BRAFTOVI + cetuximab + FOLFIRI in patients with untreated BRAF V600E-mutant metastatic colorectal cancer (mCRC) in a mini-oral presentation.

Additionally, Pfizer will present a late-breaking Proffered Paper Presentation on the Phase 2 efficacy and safety results for its GDF-15 inhibitor, ponsegromab, in patients with cancer-associated cachexia, highlighting the company’s commitment to improving the treatment journey for people living with cancer. Cancer cachexia is a common, life-threatening wasting condition characterized by severe weight loss. The condition affects patients with advanced cancers and can greatly impact a patient’s ability to tolerate cancer treatment and quality of life. Despite its severity, there are no FDA-approved treatments for cachexia. i,ii

Pfizer will also present early clinical-stage research for a number of priority pipeline areas, including encouraging Phase 1 results of the potential first-in-class antibody-drug conjugate (ADC) candidate SGN-PDL1V (PF-08046054) in NSCLC and head and neck squamous cell carcinoma (HNSCC); initial data for the investigational ADC disitamab vedotin in combination with KEYTRUDA ® (pembrolizumab) in human epidermal growth factor receptor 2 (HER2)-expressing locally advanced or metastatic urothelial cancer (la/mUC); and the first data combining atirmociclib, our highly-selective cyclin-dependent kinase 4 (CDK4) inhibitor (CDK4i), with a novel CDK2 inhibitor (CDK2i) in hormone receptor-positive (HR+)/HER2-negative metastatic breast cancer (MBC) from a Phase 1 dose-escalation study.

Key ESMO Presentations

Genitourinary Cancer

• PADCEV + KEYTRUDA**: additional analysis from the pivotal EV-302 trial continues to support the combination as a new standard of care for patients with previously untreated la/mUC. An exploratory analysis shows PADCEV + KEYTRUDA ® showed consistent progression free survival (PFS), overall survival (OS), and ORR versus chemotherapy regardless of Nectin-4 or PD-L1 expression.
• Disitamab Vedotin: preliminary efficacy and safety data for disitamab vedotin in combination with KEYTRUDA highlights Pfizer’s continued commitment to developing novel therapeutics to meet the needs of patients with bladder cancer. Results from the safety run-in of the ongoing Phase 2 trial showed encouraging early efficacy and a safety profile consistent with previously presented data in treatment-naive patients with HER2-expressing la/mUC.

Thoracic Cancer

• SGN-PDL1V (PF-08046054): encouraging Phase 1 results will be presented for PDL1V, a novel, investigational vedotin ADC directed to PD-L1-expressing solid tumors. Data from the dose-escalation and dose optimization cohorts of the ongoing Phase 1 study show PDL1V as monotherapy was generally well tolerated with no unexpected adverse events, and encouraging antitumor activity was observed in patients with heavily pretreated NSCLC and HNSCC.

Breast Cancer

• Atirmociclib (PF-07220060) + PF-07104091 : initial data from a dose-escalation study evaluating the innovative combination of atirmociclib, a potential first-in-class CDK4-selective inhibitor, with PF-07104091, a novel CDK2-selective inhibitor, showed a manageable safety profile and encouraging efficacy in patients with heavily pretreated HR+/HER2- breast cancer. These early results highlight the potential of Pfizer's strategy to advance atirmociclib as a future CDK inhibitor backbone therapy that may address treatment resistance with first generation CDK4/6i, subject to clinical success and regulatory approval. The CDK4i+2i combination is continuing to be explored in an ongoing Phase 1b/2 dose escalation and dose expansion study (NCT05262400).

Additional information on the Pfizer-sponsored abstracts, including date and time of presentation, follow in the chart below.

Pfizer is continuing its commitment to help non-scientists understand the latest findings with the development of abstract plain language summaries (APLS) for company-sponsored research being presented at ESMO, which are written in non-technical language. Those interested in learning more can visit www.Pfizer.com/apls to access the summaries starting September 16, 2024.

*The PHAROS trial is conducted with support from Pierre Fabre.

**Pfizer and Astellas have a clinical collaboration agreement with Merck to evaluate the combination of PADCEV ® and KEYTRUDA ® in patients with previously untreated metastatic urothelial cancer. 

Prescribing Information for Pfizer Medicines

Please see full Prescribing Information for PADCEV.

Please see full Prescribing Information for BRAFTOVI and full Prescribing Information for MEKTOVI.

About Pfizer Oncology

At Pfizer Oncology, we are at the forefront of a new era in cancer care. Our industry-leading portfolio and extensive pipeline includes three core mechanisms of action to attack cancer from multiple angles, including antibody-drug conjugates (ADCs), small molecules, bispecific antibodies and other immunotherapy biologics. We are focused on delivering transformative therapies in some of the world’s most common cancers, including breast cancer, genitourinary cancer, hematology-oncology, and thoracic cancers, which includes lung cancer. Driven by science, we are committed to accelerating breakthroughs that help people with cancer globally live better and longer lives.

About Pfizer: Breakthroughs That Change Patients’ Lives

At Pfizer, we apply science and our global resources to bring therapies to people that extend and significantly improve their lives. We strive to set the standard for quality, safety and value in the discovery, development and manufacture of health care products, including innovative medicines and vaccines. Every day, Pfizer colleagues work across developed and emerging markets to advance wellness, prevention, treatments and cures that challenge the most feared diseases of our time. Consistent with our responsibility as one of the world's premier innovative biopharmaceutical companies, we collaborate with health care providers, governments and local communities to support and expand access to reliable, affordable health care around the world. For 175 years, we have worked to make a difference for all who rely on us. We routinely post information that may be important to investors on our website at www.Pfizer.com . In addition, to learn more, please visit us on www.Pfizer.com and follow us on X at @Pfizer and @Pfizer News , LinkedIn , YouTube and like us on Facebook at Facebook.com/Pfizer .

DISCLOSURE NOTICE:

The information contained in this release is as of September 11, 2024. The Company assumes no obligation to update forward-looking statements contained in this release as the result of new information or future events or developments.

This release contains forward-looking information about Pfizer Oncology, Pfizer’s Oncology portfolio of marketed and investigational therapies, including combinations, and an investigational therapy for a cancer-related condition; expectations for our product pipeline, in-line products and product candidates, including their potential benefits, clinical trial results and other developing data; potential breakthrough, best- or first-in-class or blockbuster status or expected market entry of our medicines; and other statements about our business, operations and financial results that involves substantial risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Risk and uncertainties include, among other things, uncertainties regarding the commercial success of Pfizer’s oncology portfolio; the uncertainties inherent in research and development, including the ability to meet anticipated clinical endpoints, commencement and/or completion dates for our clinical trials, regulatory submission dates, regulatory approval dates and/or launch dates, as well as the possibility of unfavorable new clinical data and further analyses of existing clinical data; risks associated with interim and preliminary data; the risk that clinical trial data are subject to differing interpretations and assessments by regulatory authorities; whether regulatory authorities will be satisfied with the design of and results from our clinical studies; whether and when any drug applications, biologics license applications and/or emergency use authorization applications may be filed in any jurisdictions for any potential indication for Pfizer’s product candidates; whether and when any such applications that may be pending or filed for any of Pfizer’s product candidates may be approved by regulatory authorities, which will depend on myriad factors, including making a determination as to whether the product's benefits outweigh its known risks and determination of the product's efficacy and, if approved, whether any such product candidates will be commercially successful; decisions by regulatory authorities impacting labeling, manufacturing processes, safety and/or other matters that could affect the availability or commercial potential of Pfizer’s products or product candidates, including development of products or therapies by other companies; manufacturing capabilities or capacity; uncertainties regarding the impact of COVID-19 on Pfizer’s business, operations and financial results; and competitive developments.

A further description of risks and uncertainties can be found in Pfizer’s Annual Report on Form 10-K for the fiscal year ended December 31, 2023 and in its subsequent reports on Form 10-Q, including in the sections thereof captioned “Risk Factors” and “Forward-Looking Information and Factors That May Affect Future Results”, as well as in its subsequent reports on Form 8-K, all of which are filed with the U.S. Securities and Exchange Commission and available at www.sec.gov and www.pfizer.com .

i Cleveland Clinic. Cachexia (Wasting Syndrome). Cachexia (Wasting Syndrome): Symptoms & Treatment (clevelandclinic.org) . Accessed September 3, 2024.  ii Lisa Martin, Michael B. Sawyer, Cancer Cachexia: Emerging Preclinical Evidence and the Pathway Forward to Clinical Trials, JNCI: Journal of the National Cancer Institute , Volume 107, Issue 12, December 2015, djv322, https://doi.org/10.1093/jnci/djv322

Category: Pipeline

Media Contact:  +1 (212) 733-1226  [email protected]   Investor Contact:  +1 (212) 733-4848  [email protected]  

Source: Pfizer Inc.

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Zevra Therapeutics Presented New Data for Arimoclomol and OLPRUVA® (Sodium Phenylbutyrate) at the Society for the Study of Inborn Errors of Metabolism (SSIEM) 2024 Annual Symposium

New clinical efficacy and safety data for arimoclomol as a possible treatment for Niemann-Pick disease type C, including from long-term and real-world settings, demonstrate clinically meaningful reduction in disease progression  

Arimoclomol was well tolerated during the Open Label Extension trial and Early Access Program with no safety signals identified

PK modeling studies showed that administration of OLPRUVA while fasting vs fed results in higher drug exposure which may allow for lower effective dosages when taken without food

CELEBRATION, Fla., Sept. 06, 2024 (GLOBE NEWSWIRE) -- Zevra Therapeutics, Inc. (NasdaqGS: ZVRA) (Zevra, or the Company), a rare disease therapeutics company, today announced the presentation of five posters at the Society for the Study of Inborn Errors of Metabolism (SSIEM) 2024 Annual Symposium. Four posters focused on data from multiple studies showing the efficacy and safety of arimoclomol as a treatment for people living with Niemann-Pick disease type C (NPC), and one poster highlighted data from pharmacokinetic modeling studies of OLPRUVA ® , a therapy for the long-term management of certain adult and pediatric patients with urea cycle disorders (UCDs) involving deficiencies of carbamylphosphate synthetase (CPS), ornithine transcarbamylase (OTC) or argininosuccinic acid synthetase (AS).

“The data collected during the Phase 2/3 study of arimoclomol, including long-term data from Open Label Extension (OLE) and Early Access Program (EAP) participants, add to the large body of evidence that demonstrates arimoclomol’s clinical efficacy and safety as a treatment for people living with NPC,” said Adrian Quartel, M.D., FFPM, Chief Medical Officer of Zevra. “Additionally, we presented PK modeling data from OLPRUVA in both adult and pediatric virtual patients, showing an increase in drug exposure under fasting conditions.”

Presentation Details

The data for arimoclomol presented at SSIEM is summarized below:

The data for OLPRUVA presented at SSIEM is summarized below:

About the SSIEM 2024 Annual Symposium

The Society for the Study of Inborn Errors of Metabolism (SSIEM) 2024 Annual Symposium took place September 3-6, 2024, in Porto, Portugal. The annual symposium is intended to foster the study of inherited metabolic disorders and promote the exchange of ideas between professionals in different disciplines who are researching inborn errors of metabolism (IEM).

About Niemann-Pick Disease Type C (NPC)

Niemann-Pick disease type C (NPC) is an ultra-rare, progressive, and neurodegenerative lysosomal storage disorder characterized by an inability of the body to transport cholesterol and other lipids within the cell, leading to an accumulation of these substances in various tissue areas, including brain tissue. The disease is caused by mutations in the NPC1 or NPC2 genes, which are responsible for making lysosomal proteins. Both children and adults can be affected by NPC with varying clinical presentations. Those living with NPC lose independence due to physical and cognitive limitations, with key neurological impairments presenting in speech, cognition, swallowing, ambulation, and fine motor skills. Disease progression is irreversible and can be fatal within months or take years to be diagnosed and advance in severity.

About Arimoclomol

Arimoclomol, Zevra’s orally-delivered, investigational drug product candidate for the treatment of NPC, has been granted Orphan Drug designation, Fast Track designation, Breakthrough Therapy designation, and Rare Pediatric Disease designation by the FDA, and Orphan Medicinal Product designation for the treatment of NPC by the European Medicines Agency (EMA). The FDA has accepted the resubmission of the NDA for arimoclomol and has set a user fee action date (PDUFA date) of September 21, 2024.

About Urea Cycle Disorders

UCDs are a group of rare, genetic disorders that can cause harmful ammonia to build up in the blood, potentially resulting in brain damage and neurocognitive impairments if ammonia levels are not controlled. Any increase in ammonia over time is serious. Therefore, it is important to adhere to any dietary protein restrictions and have alternative medication options to help control ammonia levels.

About OLPRUVA ®

OLPRUVA (sodium phenylbutyrate) was approved for the treatment of certain UCDs in December 2022 and has recently been marketed under the brand name, OLPRUVA ® . OLPRUVA (sodium phenylbutyrate) for oral suspension is a prescription medicine used along with certain therapies, including changes in diet, for the long-term management of adults and children weighing 44 pounds (20 kg) or greater and with a body surface area (BSA) of 1.2 m 2 or greater, with UCDs, involving deficiencies of carbamylphosphate synthetase (CPS), ornithine transcarbamylase (OTC), or argininosuccinic acid synthetase (AS). OLPRUVA is not used to treat rapid increase of ammonia in the blood (acute hyperammonemia), which can be life-threatening and requires emergency medical treatment. For more information, please visit www.OLPRUVA.com.

Important Safety Information

Certain medicines may increase the level of ammonia in your blood or cause serious side effects when taken during treatment with OLPRUVA. Tell your doctor about all the medicines you or your child take, especially if you or your child take corticosteroids, valproic acid, haloperidol, and/or probenecid.

OLPRUVA can cause serious side effects, including: 1) nervous system problems (neurotoxicity). Symptoms include sleepiness, tiredness, lightheadedness, vomiting, nausea, headache, confusion, 2) low potassium levels in your blood (hypokalemia) and 3) conditions related to swelling (edema). OLPRUVA contains salt (sodium), which can cause swelling from salt and water retention. Tell your doctor right away if you or your child get any of these symptoms. Your doctor may do certain blood tests to check for side effects during treatment with OLPRUVA. If you have certain medical conditions such as heart, liver or kidney problems, are pregnant/planning to get pregnant or breast-feeding, your doctor will decide if OLPRUVA is right for you.

The most common side effects of OLPRUVA include absent or irregular menstrual periods, decreased appetite, body odor, bad taste or avoiding foods you ate prior to getting sick (taste aversion). These are not all of the possible side effects of OLPRUVA. Call your doctor for medical advice about side effects. You may report side effects to FDA at 1-800-FDA-1088.

About Zevra Therapeutics, Inc.

Zevra Therapeutics, Inc. is a rare disease company combining science, data, and patient needs to create transformational therapies for diseases with limited or no treatment options. Our mission is to bring life-changing therapeutics to people living with rare diseases. With unique, data-driven development and commercialization strategies, the Company is overcoming complex drug development challenges to make new therapies available to the rare disease community.

Expanded access programs are made available by Zevra and its affiliates and are subject to the Company's Expanded Access Program (EAP) policy as published on its website at  www.zevra.com . Participation in these programs is subject to the laws and regulations of each jurisdiction under which each respective program is operated. Eligibility for participation in any such program is at the treating physician's discretion.

For more information, please visit  www.zevra.com  or follow us on  X  (formerly Twitter) and  LinkedIn .

Cautionary Note Concerning Forward-Looking Statements

This press release may contain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements include all statements that do not relate solely to historical or current facts, including without limitation statements regarding the promise and zpotential impact of our preclinical or clinical trial data; the initiation, timing and results of any clinical trials or readouts, the content, information used for, timing or results of any NDA submissions or resubmissions for arimoclomol or any other product candidates for any specific disease indication or at any dosage; the potential benefits of any of our products or product candidates for any specific disease or at any dosage; our strategic and product development objectives, including with respect to becoming a leading, commercially focused rare disease company; potential revenues from our arimoclomol expanded access program; the potential for royalty and milestone contributions, the presentation of data at conferences; and the timing of any of the foregoing. Forward-looking statements are based on information currently available to Zevra and its current plans or expectations. They are subject to several known and unknown uncertainties, risks, and other important factors that may cause our actual results, performance, or achievements to be materially different from any future results, performance, or achievements expressed or implied by the forward-looking statements. These and other important factors are described in detail in the “Risk Factors” section of Zevra’s Annual Report on Form 10-K for the year ended December 31, 2023, Zevra’s Quarterly Report on Form 10-Q for the three months ended June 30, 2024, and Zevra’s other filings with the Securities and Exchange Commission. While we may elect to update such forward-looking statements at some point in the future, except as required by law, we disclaim any obligation to do so, even if subsequent events cause our views to change. Although we believe the expectations reflected in such forward-looking statements are reasonable, we cannot assure that such expectations will prove correct. These forward-looking statements should not be relied upon as representing our views as of any date after the date of this press release.