DataFramed

de DataCamp

Data science and machine learning are making the impossible possible—whether it is awe-inspiring results from language models, or huge gains in computational biology. However, as data science is increasingly democratized, it’s also making the possible widespread and there is still so much to uncover. In this podcast by DataCamp, Adel Nehme will interview data leaders from industry and academia on the latest thinking on all things data. From how to lead data teams to the importance of improving data literacy, we’ll cover all the latest trends and insights on how to scale the impact of data science in organizations.

Episodios

#72 Building High Performing Data Teams with Syafri Bahar, VP of Data Science at Gojek

por DataCamp

In this episode of DataFramed, we speak with Syafri Bahar, VP of Data Science at Gojek about building high-performing data teams, and how data science is central to Gojek’s success. 

Throughout the episode, Syafri discusses his background, the hallmarks of a high-performance data team, how he measures the ROI on data activities, the skills needed in every successful data team, what is the best organizational model for data mature organizations, how Covid-19 affected Gojek’s data teams, his thoughts on data literacy and governance, future trends in data science and AI, and why data scientists should sharpen their maths and machine learning skills in an age of increasing automation. 

Relevant links from the interview:

#71 Scaling Machine Learning Adoption: A Pragmatic Approach

por DataCamp

In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs. 

Throughout the episode, Noah discusses his background, his philosophy around pragmatic AI, the differences between data science in academia and the real world, how data scientists can become more action-oriented by creating solutions that solve real-world problems, the importance of dev-ops, his most recent book on the practical guide to MLOps, how data science can be compared to Brazilian jiu-jitsu, what data scientists should learn to scale the amount of value they deliver, his thoughts on auto-ml and automation, and more. 

Relevant links from the interview:

#70 Beyond the Language Wars: R & Python for the Modern Data Scientist

por DataCamp

In this episode of DataFramed, we speak with Rick Scavetta and Boyan Angelov about their new book, Python and R for the Modern Data Scientist: The Best of Both Worlds, and how it dawns the start of a new bilingual data science community.  

Throughout the episode, Rick and Boyan discuss the history of Python and R, what led them to write the book, how Python and R can be interoperable, the advantages of each language and where to use it, how beginner data scientists should think about learning programming languages, how experienced data scientists can take it to the next level by learning a language they’re not necessarily comfortable with, and more. 

Relevant links from the interview:

#69 Effective Data Storytelling: How to Turn Insights into Action

por DataCamp

In this episode of DataFramed, we speak with Brent Dykes, Senior Director of Insights & Data Storytelling at Blast Analytics and author of Effective Data Storytelling: How to Turn Insights into Action on how data storytelling is shaping the analytics space. 

Throughout the episode, Brent talks about his background, what made him write a book on effective data storytelling, how data storytelling is often misinterpreted and misused, the psychology of storytelling and how humans are shaped to resonate with it, the role of empathy when creating data stories, the blueprint of a successful data story, what data scientists can do to become better data storytellers, the future of augmented analytics and data storytelling, and more. 

Relevant links from the interview:

#68 The Future of Responsible AI

por DataCamp

In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.

Throughout the episode, Maria talks about her background, the differences & intersections between "AI ethics" and "Responsible AI", the state of responsible AI adoption within organizations, the link between responsible AI and organizational culture, what data scientists can do today to ensure they're part of their organization's responsible AI journey, and more.

Relevant links from the interview:

#67 Operationalizing Machine Learning with MLOps

por DataCamp

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs,  an AI Observability company on a mission to build the interface between AI and human operators. 

Throughout the episode, Alessya talks about the unique challenges data teams face when operationalizing machine learning that spurred the need for MLOps, how MLOps intersects and diverges with different terms such as DataOps, ModelOps, and AIOps, how and when organizations should get started on their MLOps journey, the most important components of a successful MLOps practice, and more. 

Relevant links from the interview:

#66 The Path to Building Data Cultures

por DataCamp

In this episode of DataFramed, Adel speaks with Sudaman Thoppan Mohanchandralal, Regional Chief Data, and Analytics Officer at Allianz Benelux, on the importance of building data cultures and his experiences operationalizing data culture transformation programs.

Throughout the episode, Sudaman talks about his background, the Chief Data Officer’s mandate and how it has evolved over the years, how organizations should prioritize building data cultures, the science behind culture change, the importance of executive data literacy when scaling value from data, and more.

Relevant links from the interview:

#65 Preventing Fraud in eCommerce with Data Science

por DataCamp

In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.

Throughout the episode, Elad talks about his background, the plethora of data science use-cases in eCommerce, how Riskified builds state-of-the-art fraud detection models, common pitfalls data teams face, his best practices gaining organizational buy-in for data projects, how data scientists should focus on value, whether they should have engineering skills, and more.

Relevant links from the interview:

#64 Creating Trust in Data with Data Observabilty

por DataCamp

In this episode of DataFramed, Adel speaks with Barr Moses, CEO, and co-founder of Monte Carlo on the importance of data quality and how data observability creates trust in data throughout the organization. 

Throughout the episode, Barr talks about her background, the state of data-driven organizations and what it means to be data-driven, the data maturity of organizations, the importance of data quality, what data observability is, and why we’ll hear about it more often in the future. She also covers the state of data infrastructure, data meshes, and more. 

Relevant links from the interview:


#63 The Past and Present of Data Science

por DataCamp

In this episode of DataFramed, Adel speaks with Sergey Fogelson, Vice President of Data Science and Modeling at Viacom on how data science has evolved over the past decade, and the remaining large-scale challenges facing data teams today.

Throughout the episode, Sergey deep-dives into his background, the various projects he’s been involved with throughout his career, the most exciting advances he’s seen in the data science space, the largest challenges facing data teams today, best practices democratizing data, the importance of learning SQL, and more. 

Relevant links from the interview:

More resources from DataCamp


#62 From Predictions to Decisions

por DataCamp

In this episode of DataFramed, Adel speaks with Dan Becker, CEO of decision.ai and founder of Kaggle Learn on the intersection of decision sciences and AI, and best practices when aligning machine learning to business value.

Throughout the episode, Dan deep-dives into his background, how he reached the top of a Kaggle competition, the difference between machine learning in a Kaggle competition and the real world, the role of empathy when aligning machine learning to business value, the importance of decisions sciences when maximizing the value of machine learning in production, and more. 

Links:

#61 Creating Smart Cities with Data Science

por DataCamp

In this episode of DataFramed, Adel speaks with Amen Ra Mashariki, principal scientist at Nvidia and the former Chief Analytics Officer of the City of New York on how data science is done in government agencies, and how it's driving smarter cities all around us. 

Throughout the episode, Amen deep-dives into the use-cases he worked on to make the city of New York smarter, how data science allows cities to become more reactive and proactive, the unique challenges of scaling data science in a government setting, the friction between providing value and data privacy and ethics, the state of data literacy in government, and more. 

Links from the interview:


New DataFramed Episodes

por DataCamp

We are super excited to be relaunching the DataFramed podcast. In this iteration of DataFramed, Adel Nehme, a data science educator at DataCamp, will uncover the latest thinking on all things data and how it’s impacting organizations through biweekly (once every two weeks) interviews and conversations with data experts from across the world. 

Check out this snippet for a preview of what’s to come and for a short chat with DataCamp’s CEO Jonathan Cornelissen on where he thinks data science is headed and the major challenges facing data teams today. 

Links:


#60 Data Privacy in the Age of COVID-19

por DataCamp

Before the COVID-19 crisis, we were already acutely aware of the need for a broader conversation around data privacy: look no further than the Snowden revelations, Cambridge Analytica, the New York Times Privacy Project, the General Data Protection Regulation (GDPR) in Europe, and the California Consumer Privacy Act (CCPA). In the age of COVID-19, these issues are far more acute. We also know that governments and businesses exploit crises to consolidate and rearrange power, claiming that citizens need to give up privacy for the sake of security. But is this tradeoff a false dichotomy? And what type of tools are being developed to help us through this crisis? In this episode, Katharine Jarmul, Head of Product at Cape Privacy, a company building systems to leverage secure, privacy-preserving machine learning and collaborative data science, will discuss all this and more, in conversation with Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp.

Links from the show

FROM THE INTERVIEW

#59 Data Science R&D at TD Ameritrade

por DataCamp

This week, Hugo speaks with Sean Law about data science research and development at TD Ameritrade. Sean’s work on the Exploration team uses cutting edge theories and tools to build proofs of concept. At TD Ameritrade they think about a wide array of questions from conversational agents that can help customers quickly get to information that they need and going beyond chatbots. They use modern time series analysis and more advanced techniques like recurrent neural networks to predict the next time a customer might call and what they might be calling about, as well as helping investors leverage alternative data sets and make more informed decisions.

What does this proof of concept work on the edge of data science look like at TD Ameritrade and how does it differ from building prototypes and products? And How does exploration differ from production? Stick around to find out.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~19:20)

Data Science Best Practices (with Ben Skrainka ~34:50)


Original music and sounds by The Sticks.

#58 Critical Thinking in Data Science

por DataCamp

This week, Hugo speaks with Debbie Berebichez about the importance of critical thinking in data science. Debbie is a physicist, TV host and data scientist and is currently the Chief Data Scientist at Metis in NY.

In a world and a professional space plagued by buzz terms like AI, big data, deep learning, and neural networks, conversations around skill sets and less than productive programming language wars, what has happened to critical thinking in data science and data thinking in general?

What type of critical thinking skills are even necessary as data science, AI and machine learning become even more present in all of our lives and how spread out do they need to be across organizations and society? Listen to find out!


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

Data Science tools for getting stuff done and giving it to the world (with Jared Lander ~21:55)

Statistical Distributions and their Stories (with Justin Bois ~39:30)


Original music and sounds by The Sticks.

#57 The Credibility Crisis in Data Science

por DataCamp

This week, Hugo will be speaking with Skipper Seabold about the current and looming credibility crisis in data science. Skipper is Director of Data Science at Civis Analytics, a data science technology and solutions company, and also the creator of the statsmodels package for statistical modeling and computing in python. Skipper is also a data scientist with a beard bigger than Hugo's.

They’re going to be talking about how data science is facing a credibility crisis that is manifesting itself in different ways in different industries, how and why expectations aren’t met and many stakeholders are disillusioned. You’ll see that if the crisis isn’t prevented, the data science labor market may cease to be a seller’s market and we’ll have big missed opportunities. But this isn’t an episode of Black Mirror so they’ll also discuss how to avoid the crisis, taking detours through the role of randomized control trials in data science, the rise of methods borrowed from econometrics and how to set realistic expectations around what data science can and can’t do.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~15:48 & ~35:20)


Original music and sounds by The Sticks.

#56 Data Science at AT&T Labs Research

por DataCamp

This week, Hugo speaks with Noemi Derzsy, a Senior Inventive Scientist at AT&T Labs within the Data Science and AI Research organization, where she does lots of science with lots of data.

They’ll be talking about her work at AT&T Labs Research, the mission of which is to look beyond today’s technology solutions to invent disruptive technologies that meet future needs. AT&T Labs works on a multitude of projects, from product development at AT&T, to how to combat bias and fairness issues in targeted advertising and creating drones for cell tower inspection research that leverages AI, ML and video analytics. They’ll be talking about some of the work Noemi does, from characterizing human mobility from cellular network data to characterizing their mobile network to analyze how its topology compares to other real social networks reported to understanding tv viewership, and how engaged people are in different shows. They’ll discuss what the future of data science looks like, whether it will even be around in 2029 and what types of skills would help you land a job in a place like AT&T Labs.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~18:23 & ~36:38)


Original music and sounds by The Sticks.

#55 Getting Your First Data Science Job

por DataCamp

This week, Hugo speaks with Chris Albon about getting your first data science job. Chris is a Data Scientist at Devoted Health, where he uses data science and machine learning to help fix America's healthcare system. Chris is also doing a lot of hiring at Devoted and that’s why he’s so excited today to talk about how to get your first data science job. You may know Chris as co-host of the podcast Partially Derivative, from his educational resources such as his blog and machine learning flashcards or as one of the funniest data scientists on Twitter.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

Guidelines for A/B Testing (with Emily Robinson ~26:50)


Original music and sounds by The Sticks.

#54 Women in Data Science

por DataCamp

This week, Hugo speaks with Reshama Shaikh, about women in machine learning and data science, inclusivity and diversity more generally and how being intentional in what you do is essential. Reshama, a freelance data scientist and statistician, is also an organizer of the meetup groups Women in Machine Learning & Data Science (otherwise known  as WiMLDS) and PyLadies. She has organized WiMLDS for 4 years and is a Board Member. They’ll discuss her work at WiMLDS and what you can do to support and promote women and gender minorities in data science. They’ll also delve into why women are flourishing in the R community but lagging in Python and discuss more generally how NUMFOCUS thinks about diversity and inclusion, including their code of conduct. All this and more.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

WiMLDS (Women in Machine Learning and Data Science)

FROM THE SEGMENTS

DataCamp User Stories (with David Sudolsky ~17:27 & ~31:50)


Original music and sounds by The Sticks.

#53 Data Science, Gambling and Bookmaking

por DataCamp

This week, Hugo speaks with Marco Blume, Trading Director at Pinnacle Sports. Marco and Hugo will talk about the role of data science in large-scale bets and bookmaking, how Marco is training an army of data scientists and much more. At Pinnacle, Marco uses tight risk-management built on cutting-edge models to provide bets not only on sports but on questions such as who will be the next pope? Who will be the world hot dog eating champion, who will land on mars first and who will be on the iron throne at the end of game of thrones. They’ll discuss the relations between risk management and uncertainty, how great forecasters are necessarily good at updating their predictions in the light of new data and evidence, how you can model this using Bayesian inference and the future of biometric sensing in sports betting. And, as always, much, much more.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

Data Science Best Practices (with Ben Skrainka ~16:40)

Statistical Distributions and their Stories (with Justin Bois at ~36:00)


Original music and sounds by The Sticks.

#52 Data Science at the BBC

por DataCamp

This week on DataFramed, the DataCamp podcast, Hugo speaks with Gabriel Straub, the Head of Data Science and Architecture at the BBC, where his role is to help make the organization more data informed and to make it easier for product teams to build data and machine learning powered products. They’ll be talking about data science and machine learning at the BBC and how they can impact content discoverability, understanding content, putting the right stuff in front of people, how Gabriel and his team develop broader data science & machine learning architecture to make sure best practices are adopted and what it means to apply machine learning in a sensible way. How does the BBC think about incorporating data science into its business, which has been around since 1922 and historically been at the forefront of technological innovation such as in radio and television? Listen to find out!

LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

DataCamp User Stories (with Krittika Patil ~16:10 & ~38:12)


Original music and sounds by The Sticks.

#51 Inclusivity and Data Science

por DataCamp

This week Hugo speaks with Dr. Brandeis Marshall, about people of color and under-represented groups in data science. They’ll talk about the biggest barriers to entry for people of color, initiatives that currently exist and what we as a community can do to be as diverse and inclusive as possible.

Brandeis is an Associate Professor of Computer Science at Spelman College. Her interdisciplinary research lies in the areas of information retrieval, data science, and social media. Other research includes the BlackTwitter Project, which blends data analytics, social impact and race as a lens to understanding cultural sentiments. Brandeis is involved in a number of projects, workshops, and organizations that support data literacy and understanding, share best data practices and broaden participation in data science.


LINKS FROM THE SHOW

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

What Data Scientists Really Do (with Hugo Bowne-Anderson & Emily Robinson ~21:30 & ~41:40)


Original music and sounds by The Sticks.

#50 Weapons of Math Destruction

por DataCamp

In episode 50, our Season 1, 2018 finale of DataFramed, the DataCamp podcast, Hugo speaks with Cathy O’Neil, data scientist, investigative journalist, consultant, algorithmic auditor and author of the critically acclaimed book Weapons of Math Destruction. Cathy and Hugo discuss the ingredients that make up weapons of math destruction, which are algorithms and models that are important in society, secret and harmful, from models that decide whether you keep your job, a credit card or insurance to algorithms that decide how we’re policed, sentenced to prison or given parole? Cathy and Hugo discuss the current lack of fairness in artificial intelligence, how societal biases are perpetuated by algorithms and how both transparency and auditability of algorithms will be necessary for a fairer future. What does this mean in practice? Tune in to find out. As Cathy says, “Fairness is a statistical concept. It's a notion that we need to understand at an aggregate level.” And, moreover, “data science doesn't just predict the future. It causes the future.”


LINKS FROM THE SHOW

DATAFRAMED SURVEY

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

Data Science Best Practices (with Heather Nolis ~20:30)

Data Science Best Practices (with Ben Skrainka ~39:35)


Original music and sounds by The Sticks.

#49 Data Science Tool Building

por DataCamp

Hugo speaks with Wes McKinney, creator of the pandas project for data analysis tools in Python and author of Python for Data Analysis, among many other things. Wes and Hugo talk about data science tool building, what it took to get pandas off the ground and how he approaches building “human interfaces to data” to make individuals more productive. On top of this, they’ll talk about the future of data science tooling, including the Apache arrow project and how it can facilitate this future, the importance of DataFrames that are portable between programming languages and building tools that facilitate data analysis work in the big data limit. Pandas initially arose from Wes noticing that people were nowhere near as productive as they could be due to lack of tooling & the projects he’s working on today, which they’ll discuss, arise from the same place and present a bold vision for the future.

LINKS FROM THE SHOW

DATAFRAMED SURVEY

DATAFRAMED GUEST SUGGESTIONS

FROM THE INTERVIEW

FROM THE SEGMENTS

Data Science Best Practices (with Ben Skrainka ~17:10)

Studies in Interpretability (with Peadar Coyle at ~39:00)



Original music and sounds by The Sticks.

#48 Managing Data Science Teams

por DataCamp

In this episode of DataFramed, the DataCamp podcast, Hugo speaks with Angela Bassa about managing data science teams. Angela is Director of Data Science at iRobot, where she leads the team through development of machine learning algorithms, sentiment analysis, and anomaly detection processes. iRobot are the makers of consumer robots that we all know and love, like the Roomba, and the Braava which are, respectively, a robotic vacuum cleaner and a robotic mop. Angela will talk about how to get into data science management, the most important strategies to ensure that your data science team delivers value to the organization, how to hire data scientists and key points to consider as your data science team grows over time, in addition to the types of trade-offs you need to make as a data science manager and how you make the right ones. Along the way, you’ll see why a former marine biologist has the skills and ways of thinking to be a super data scientist at a company like iRobot and you’ll also see the importance of throwing data analysis parties.

LINKS FROM THE SHOW

FROM THE INTERVIEW

FROM THE SEGMENTS

Correcting Data Science Misconceptions (w/ Heather Nolis ~18:45)

Project of the Month (w/ David Venturi ~38:45)


Original music and sounds by The Sticks.

#47 Human-centered Design in Data Science

por DataCamp

Hugo speaks with Peter Bull about the importance of human-centered design in data science. Peter is a data scientist for social good and co-founder of Driven Data, a company that brings cutting-edge practices in data science and crowdsourcing to some of the world's biggest social challenges and the organizations taking them on, including machine learning competitions for social good. They’ll speak about the practice of considering how humans interact with data and data products and how important it is to consider them while designing your data projects. They’ll see how human-centered design provides a robust and reproducible framework for involving the end-user all through the data work, illuminated by examples such as DrivenData’s work in financial services and Mobile Money in Tanzania. Along the way, they’ll discuss the role of empathy in data science, the increasingly important conversation around data ethics and much, much more.

LINKS FROM THE SHOW

FROM THE INTERVIEW

FROM THE SEGMENTS

Probability Distributions and their Stories (with Justin Bois at ~24:00)

Studies in Interpretability (with Peadar Coyle at ~38:10)


Original music and sounds by The Sticks.

#46 AI in Healthcare, an Insider's Account

por DataCamp

In this episode of DataFramed, a DataCamp podcast, Hugo speaks with Arnaub Chatterjee. Arnaub is a Senior Expert and Associate Partner in the Pharmaceutical and Medical Products group at McKinsey & Company. They’ll discuss cutting through the hype about artificial intelligence (AI) and machine learning (ML) in healthcare by looking at practical applications and how McKinsey & Company is helping the industry evolve.

Tune in for an insider’s account into what has worked in healthcare, from ML models being used to predict nearly everything in clinical settings, to imaging analytics for disease diagnosis, to wound therapeutics. Will robots and AI replace disciplines such as radiology, ophthalmology, and dermatology? How have the moving parts of data science work evolved in healthcare? What does the future of data science, ML and AI in healthcare hold? Stick around to find out.

LINKS FROM THE SHOW

FROM THE INTERVIEW

FROM THE SEGMENTS

Machines that Multi-task (with Manny Moss)

Part 1 at ~21:05

Part 2 at ~40:00


Original music and sounds by The Sticks.


#45 Decision Intelligence and Data Science

por DataCamp

In this episode of DataFramed, Hugo speaks with Cassie Kozyrkov, Chief Decision Scientist at Google Cloud. Cassie and Hugo will be talking about data science, decision making and decision intelligence, which Cassie thinks of as data science plus plus, augmented with the social and managerial sciences. They’ll talk about the different and evolving models for how the fruits of data science work can be used to inform robust decision making, along with pros and cons of all the models for embedding data scientists in organizations relative to the decision function. They’ll tackle head on why so many organizations fail at using data to robustly inform decision making, along with best practices for working with data, such as not verifying your results on the data that inspired your models. As Cassie says, “Split your damn data”.

Links from the show

FROM THE INTERVIEW

FROM THE SEGMENTS

Probability Distributions and their Stories (with Justin Bois at ~19:45)

Machines that Multi-Task (with Friederike Schüür of Fast Forward Labs ~43:45)


Original music and sounds by The Sticks.

#44 Project Jupyter and Interactive Computing

por DataCamp

In this episode of DataFramed, Hugo speaks with Brian Granger, co-founder and co-lead of Project Jupyter, physicist and co-creator of the Altair package for statistical visualization in Python.

They’ll speak about data science, interactive computing, open source software and Project Jupyter. With over 2.5 million public Jupyter notebooks on github alone, Project Jupyter is a force to be reckoned with. What is interactive computing and why is it important for data science work? What are all the the moving parts of the Jupyter ecosystem, from notebooks to JupyterLab to JupyterHub and binder and why are they so relevant as more and more institutions adopt open source software for interactive computing and data science? From Netflix running around 100,000 Jupyter notebook batch jobs a day to LIGO’s Nobel prize winning discovery of gravitational waves publishing all their results reproducibly using Notebooks, Project Jupyter is everywhere. 


Links from the show 

FROM THE INTERVIEW

FROM THE SEGMENTS

Machines that Multi-Task (with Friederike Schüür of Fast Forward Labs)

Part 1 at ~24:40

Part 2 at ~44:00


 Original music and sounds by The Sticks.

#43 Election Forecasting and Polling

por DataCamp

Hugo speaks with Andrew Gelman about statistics, data science, polling, and election forecasting. Andy is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University and this week we’ll be talking the ins and outs of general polling and election forecasting, the biggest challenges in gauging public opinion, the ever-present challenge of getting representative samples in order to model the world and the types of corrections statisticians can and do perform. 

"Chatting with Andy was an absolute delight and I cannot wait to share it with you!"-Hugo 

 Links from the show 

FROM THE INTERVIEW

FROM THE SEGMENTS

Statistical Lesson of the Week (with Emily Robinson at ~13:30)

Data Science Best Practices (with Ben Skrainka~40:40)


 Original music and sounds by The Sticks.

#42 Full Stack Data Science

por DataCamp

Hugo speaks with Vicki Boykis about what full-stack end-to-end data science actually is, how it works in a consulting setting across various industries and why it’s so important in developing modern data-driven solutions to business problems. Vicki is a full-stack data scientist and senior manager at CapTech Consulting, working on projects in machine learning and data engineering. They'll also discuss the increasing adoption of data science in the cloud technologies and associated pitfalls, along with how to equip businesses with the skills to maintain the data products you developed for them. All this and more: Hugo is pumped!

Links from the show

#41 Uncertainty in Data Science

por DataCamp

Hugo speaks with Allen Downey about uncertainty in data science. Allen is a professor of Computer Science at Olin College and the author of a series of free, open-source textbooks related to software and data science. Allen and Hugo speak about uncertainty in data science and how we, as humans, are not always good at thinking about uncertainty, which we need be to in such an uncertain world. Should we have been surprised at the outcome of the 2016 election? What approaches can we, as a data reporting community, take to communicate around uncertainty better in the future? From election forecasting to health and safety, thinking about uncertainty and using data & data-oriented tools to communicate around uncertainty are essential.

Links from the show

#40 Becoming a Data Scientist

por DataCamp

Hugo speaks with Renee Teate about the many paths to becoming a data scientist. Renee is a Data Scientist at higher ed analytics start-up HelioCampus, and creator and host of the Becoming a Data Scientist Podcast. In addition to discussing the many possible ways to become becoming a data scientist, they will discuss the common data scientist profiles and how to figure out which ones may be a fit for you. They’ll also dive into the fact that you need to figure out both where you are in terms of skills and knowledge and where you want to go in terms of your career. Renee has a bunch of great suggestions for aspiring data scientists and also flags several important pitfalls and warnings. On top of this, they'll dive into how much statistics, linear algebra and calculus you need to know in order to become an effective data scientist and/or data analyst.

Links from the show

#39 Data Science at Stitch Fix

por DataCamp

Hugo speaks with Eric Colson, Chief Algorithms Officer at Stitch Fix, an online personal styling service reinventing the shopping experience by delivering one-to-one personalization to their clients through the combination of data science and human judgment. Eric is responsible for the creation of dozens of algorithms at Stitch Fix that are pervasive to nearly every function of the company, from merchandise, inventory, and marketing to forecasting and demand, operations, and the styling recommender system. Join for all of this and more.

Links from the show