Discussion with Alexander Schacht and Benjamin Piske how it relates to your goals, what it takes to think strategically, which role innovation has here, what practical steps to take to drive teams forward, which knowledge to acquire to lead teams successfully, how this relates to influencing, and how your attitude will play a big role in this.…

Interview with Shafi ChowdhuryIn this episode, we’ll cover an amazing story by one of the best programmers and mentors I ever worked with - Shafi Chowdhury (www.shaficonsultancy.com).We’ll explore how it changed from being a freelance programmer only to building his company on the side. He had a great vision in mind, that drove him forward.You’ll a…

Discussion with Alexander Schacht and Paolo Eusebi 000000E2 000000E0 00002DDD 00002879 000099FA 0000A568 00007FBA 00006CBA 00004C39 0000A224על ידי Alexander Schacht and Paolo Eusebi

Discussion with Alexander Schacht and Paolo Eusebi 0000016B 0000015F 000045CA 00003CF4 0013891F 000E045E 00007FBA 00006E82 00004C39 001366D6על ידי Alexander Schacht and Paolo Eusebi

Do you have a lot of email ping-pong, where emails go back and forth many times – too many times?Are you aware about the brand of you, that you communicate with your email style?Is email your default communication tool?Then this episode is for you. We have researched various articles on good email writing copies and distilled the best for you in th…

Discussion with Alexander Schacht and Benjamin Piske By listening to this episode, you’ll learn about these topics: What are helpful mindsets about emails Five step approach to managing emails Good habits to establish like Reply in a timely manner Send and respond less to receive less. Tips on how to set up filters Smartphone vs desktop email check…

Discussion with Alexander Schacht and Paolo EusebiIn this episode, we share out ideas and experiences, which mindset sets up statisticians for success. We cover topics around:על ידי Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry

על ידי Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry

Discussion with Alexander Schacht and Benjamin Piske In this episode, we share our ideas and experiences, which mindset sets up for success. We cover topics around: Leading people Convincing business partners Delivering value and selling it–and what does selling mean Thinking outside the status quo to improve things in the long run Always learning …

על ידי Alexander Schacht and Paolo Eusebi

על ידי Gyom

Discussion with Paolo and ThomasCommunicating data is so important! Quarto is a fantastic tool for writing reproducible reportsusing literate programming. Literate programming allows us to incorporate documentation andcode in the same program. The data science community has embraced this idea by adoptingRmarkdown and Jupyter Notebooks. Using Quarto…

Resources: R Packages (2e)על ידי Alexander Schacht and Paolo Eusebi

In this episode, Paolo and Thomas dive into the fundamental principles for a well-structured data science project. These include practical advice on: • organizing files into folders, • documenting and commenting code, • using version control systems and much more. Although the episode focuses on applying these fundamental principles in R projects, …

In this episode, we move from the logistic regression model to proportional odds model, with emphasis oninterpretation and the checking of assumptions (visually and analytically). We also speak about theopportunities and challenges of dealing with the dichotomization of ordinal or continuous variables. Resources: ● McCullagh, Peter, and John A. Nel…

Logistic regression is a beautiful tool for modeling a binary dependent variable, although many morecomplex extensions exist. In the show, we will speak about the generalized linear model family, logit andprobit functions, interpretations, and practicalities. Resources: ● McCullagh, Peter, and John A. Nelder. Generalized linear models. Routledge, 1…

Creating reproducible research is crucial for data scientists as it ensures transparency, understanding, and accuracy in the research process. Not only does it help others understand your work, but it also allows for the reproduction and verification of your results in the future. Heidi Seibold, an expert in reproducible research, suggests three st…

Alexander interviewed Hana Khan about her path from being a data analyst to a data visualizer. Hana runs Hanalytx, her own company, which is specialized in helping others in presenting and visualizing data. Hana also runs the Art of Communicating Data podcast. In this episode, Hana and Alexander discussed super interesting topics like sources of in…

Interview with Alex Andorra Interviewing Alex Andorra about bayesian inference, probabilistic programming, and more was a pleasure. Alex is a data scientist and modeler at the PyMC Labs consultancy. He's also an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. Alex is also a contributor and instructor in the "Intui…

Interview with Shafi Chowdhury Click here to get the quick guide! Shafi ChowdhuryThis image has an empty alt attribute; its file name is shaffi.webpHe has have over 20 years of experience as a statistical programmer in the Pharma industry. He worked for Pharma companies and CROs across Europe in many different therapeutic areas and in all phases of…

Interpretable Machine Learning with Python - Second Ed Interpretable Machine Learning with Python - Second Edition [link] Explainable Boosting Machine [link] How Interpretable and Trustworthy are GAMs? [link]SHAP (SHapley Additive exPlanations) [link]על ידי Gyom

Other resources: Linear Models with R by Julian J. Faraway, 2nd EditionRegression Modeling Strategies by Frank E. Harrellעל ידי Gyom

Chanin Nantasenamat is behind the very successful YouTube channel Data Professor. He is an actual professor with a background in working in academia in the field of bioinformatics. Since the beginning of the pandemic he has started his brand Data Professor and has been taking the data science scene by storm. We talked about his background, how he d…

על ידי Introduction to Linear Regression - Part 1

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Amazon has many services and one of the biggest products they have is AWS. It stands for Amazon Web Services and is a cloud services platform. As part of their cloud services platform, Amazon also has skilled data scientists helping their clients implement data science solutions effectively and efficiently. In this episode, I talk to one such data …

There are many ways to get a data science job and on this episode we hear a very interesting one! Khuyen Tran is a student and a data scientist who got her first job before even graduating. On top of other things, we learn how this was possible. She shares her tips on how to stay engaged in the data science field and how to improve oneself by doing…

Communities all over the world are on the rise. People want to connect with others that they have shared values with. People in these communities help their fellow members with hard times, follow each other's journeys and are there for each other through challenges. One such great community is Women in Data. Its goal is to increase diversity in dat…

Yes, government agencies use AI too! When you think about it, with the gigantic amount of data they have, no wonder they need machine learning solutions to deal with their problems. And if you're passionate about working for the government or at least supporting it, this could be a great alternative for you! This week on the show I have Nicole Jane…

This week on the show I have Mikiko Bazeley. She is currently busy co-founding Sidewalk.ai, an AI based solution for residential real-estate. Her path to founding a company hasn't been very straightforward though. She has worked in many different companies, in many different domains before she got to where she is today. Her story is a great example…

Data Scientists are in demand and that's why becoming a data scientist is also in demand. Though many times what people have in mind when they think of becoming a data scientist is working at a company, with a boss. This doesn't have to be the case for everyone though. Ways you can do data science and in general, careers in data are too many to cou…

A consultant’s life is an exciting one. You get to experience many different industries, domains and technologies. It tends to be high-paced and at times stressful but if you like the kind of work you do, it is rewarding. Rossy is one such consultant. She works for a very big multinational tech consultancy firm. She specializes in finance and banki…

As a person who looked for jobs in the remote workspace before the pandemic, I can say, it is not easy. Fully remote companies tend to be, simply, super cool and have great culture. That’s why they get a lot of applications. This in turn makes the space super competitive. When I, “re-met” Ceren 6-7 months ago, she was also looking for remote work. …

This week I welcome Jayeeta Putatunda on the show! Jayeeta is a senior data scientist specialising in Natural Language Processing and Machine Learning solutions at Indellient Inc. in New York. She is equipped with all the skills a data scientist can have and works on very interesting projects with Fortune 100 companies. But of course, this doesn't …

One of the biggest worries before changing to data science is inexperience. It's natural to think that what you're doing right now is not relevant and that it's too late for you to switch to data science. But what you work on now does not stop you from pursuing a career in data science. In this episode, we learn about Meg's transition into data sci…

Doing a PhD is a great way to dive very deeply into a topic you're passionate about. When it comes to AI there is no shortage of interesting topics. If you don't want to have the limitation of a company that needs to make profits and want to satisfy your curiosity about your passion, doing a PhD might be just the thing for you. This week I'm interv…

Curious about how data science and machine learning contributes to renewable energy research? In this episode, Sakshi Mishra from the National Renewable Energy Laboratory (NREL) joins me to talk about her journey and her work. NREL is part of the United States Department of Energy and its main goal is to innovate on renewable energy and energy effi…

Sometimes it’s hard to imagine a vast variety of jobs where data science skills and machine learning are used. So, we end up defaulting to a data scientist position. Shivali Goel’s job is an excellent example of a non-data scientist title where data science skills are highly used. Shivali is a globalisation engineer at Adobe. She works on making su…

Yaakov is a data scientist and theatre producer. He has experience working in start-ups and more recently as a free data scientist ready to make trouble. He has worked on some very interesting projects bringing art and data science together. We talk about his experience working in start-ups, how he comes up with his projects now and what he works o…

Nikola and Petr both work for the same company in Prague. DataSentics, where they work, has a wide selection of projects and a very interesting portfolio. As part of their jobs as data scientists, Nikola and Petr not only implement machine learning models for their clients but also act as consultants and help them from start to end of creating a wh…

On this week's episode, I welcome Katia Stambolieva, proud co-founder of the space tech company NinaSpace and a freelance data scientist. In NinaSpace, Katia and the team work on detecting wildfires from space and warn governments on time to mitigate the negative effect of these unexpected events. Apart from her work at her company, she also does f…

In this week's episode, Jigyasa Grover of Twitter joins me to talk about her career and machine learning engineering. She shares her journey of becoming a machine learning engineer and the steps she took throughout the years. We dive deeper into the types of projects she works on, how she works with her team at Twitter and what she loves about her …

In this episode, we look into one of the many data-related titles: product analyst. Kasia Rachuta is a product analyst at Square, a financial services company in San Francisco. She also held data science positions before and is able to compare the two easily. We talk about how being a product analyst differs from being a data scientist, what her da…

Samantha joins me to talk about her career in the fourth episode of So you want to be a data scientist. She is a senior data scientist at Sentry.io in San Francisco. We talk about her journey from academia to business. We also discuss her daily responsibilities and what being a senior data scientist changed for her. Samantha has really helpful insi…

Melissa is a technical writer at Uber working specifically for the machine learning team. In this episode we get to learn more about technical writing and what this job entails. Melissa talks about her journey of becoming a technical writer, shares the kind of tasks she performs day-to-day and informs us about the career path of a technical writer.…

Data science can take many shapes and forms. It is important to get insights from people working in different industries, different sized companies and different roles. In this episode, Víctor García Cazorla joins me to share his data science story. He works at ABN AMRO, one of the major banks in the Netherlands. He shares his experience of working…

Madli is a former colleague of mine who is currently working for a new exciting start-up called Pactum. She is an entrepreneur at heart and a badass data scientist with a background in social sciences. She worked in many cool projects during her time at IBM and has 3 years of first hand experience of working with clients. In this episode, she will …