The Effective Data Scientist

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 Chowdhury In 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 also hear about his approach to teaching and mentoring – or in general helping people do their job better. His abundance mindset inspires me a lot. Shafi explains, why and how he made his own job redundant in his own company. Retention is a major cost driver and disrupts some companies a lot. You’ll learn how he manages to have nearly no turnover in his company and how is approach to recruiting and training new employees fits in to this. Further, we’ll cover how and why Shafi and his team regularly presenting at different conferences. We’ll especially go deeper on this presentation at the PSI conference in 2017 about an amazing tool to analyse and visualize data at the same time. His approach to delivering all this innovation is very unique. Finally, you’ll learn a lot about the leadership attitude that helped him grow his business fast into a medium-sized CRO with a very stable client base.

Discussion with Alexander Schacht and Paolo Eusebi 000000E2 000000E0 00002DDD 00002879 000099FA 0000A568 00007FBA 00006CBA 00004C39 0000A224

Discussion with Alexander Schacht and Paolo Eusebi 0000016B 0000015F 000045CA 00003CF4 0013891F 000E045E 00007FBA 00006E82 00004C39 001366D6

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 this episode. By listening to this episode, you will learn about: When is email the best way to communicate? Approaches to attachments. How to take care of your style? How to structure your emails? About different email cultures Simple actionable tricks to improve your effectiveness directly

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 checking
• When and when not to work on emails
• And the option of last resort

Discussion with Alexander Schacht and Paolo Eusebi In this episode, we share out ideas and experiences, which mindset sets up statisticians for success. We cover topics around:

Discussion with Alexander Schacht and Paolo Eusebi

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 about the business and the people in the business

Learning about statistical innovation

Doing things more effectively

Becoming more impact-fully

Raising your business acumen–internally and externally

Having quality in mind

Quote from the Episode by Bill Gates “Most people overestimate what they can do in one year and underestimate what they can do in ten years.”

Discussion with Paolo Eusebi and Alexander Schacht

Interview with Zachary Skrivanek

Discussion with Paolo and Thomas Communicating data is so important! Quarto is a fantastic tool for writing reproducible reports using literate programming. Literate programming allows us to incorporate documentation and code in the same program. The data science community has embraced this idea by adopting Rmarkdown and Jupyter Notebooks. Using Quarto efficiently, you can create parametrized reports, write scientific publications, and build data-driven slides. Enjoy this super-interesting conversation with Thomas Neitmann, and be an effective data scientist!

Resources:

R Packages (2e)

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, you want to apply the same principles to any Data Science project, regardless of the language used.

Further resources:

Advanced R 2nd Ed. (http://adv-r.hadley.nz)

R for Data Science 2nd Ed. (http://(r4ds.hadley.nz)

In this episode, we move from the logistic regression model to proportional odds model, with emphasis on interpretation and the checking of assumptions (visually and analytically). We also speak about the opportunities and challenges of dealing with the dichotomization of ordinal or continuous variables.

Resources:

● McCullagh, Peter, and John A. Nelder. Generalized linear models. Routledge, 1983.

● Agresti, Alan. Categorical data analysis. John Wiley & Sons, 2003.

● Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC, 2016. (http://https://julianfaraway.github.io/faraway/ELM/)

Logistic regression is a beautiful tool for modeling a binary dependent variable, although many more complex extensions exist. In the show, we will speak about the generalized linear model family, logit and probit functions, interpretations, and practicalities.

Resources:

● McCullagh, Peter, and John A. Nelder. Generalized linear models. Routledge, 1983.

● Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC, 2016. (http://https://julianfaraway.github.io/faraway/ELM/)

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 steps for achieving reproducibility:

Document everything

Develop reusable code, and

Share results with others.

By following these steps, you can ensure that your research is reproducible and accessible to anyone who needs it. Share this resource with your colleagues who want to enhance the quality of their research!

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 inspiration, optimal workflow, and appropriate tools for producing great data visualizations.

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 "Intuitive Bayes Introductory Course". This self-paced course is designed for data scientists and developers, where you'll learn Bayesian modeling with code, not math.
Alex also runs the amazing "Learning Bayesian Statistics" podcast!
If you love Python and Bayesian inference, catch this episode.

How Alex began his path on statistical modeling and how he finds this a challenging, versatile, and creative

How Alex and his agency do with clients using multilevel regression and post-stratification and tracking opinion through time

How the community around PMYC is growing and contributing to development in an open source format

Alex hosts a podcast that covers a wide variety of topics, including political elections, healthcare, neuroscience, and the market

Alex's suggestions of what books to read and how to effectively contribute to open source projects, and stay active in the community

What are the benefits of joining online communities for learning

and more..

Listen to this episode now and share this with your friends and colleagues!

Interview with Shafi Chowdhury

Click here to get the quick guide!

Shafi Chowdhury This image has an empty alt attribute; its file name is shaffi.webp He 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 clinical trials before setting up his own consultancy firm. He believes knowledge should be shared and therefore he is a regular presenter at PhUSE conferences and regularly attend many other conferences including PSI conferences for Statisticians in the Pharmaceutical Industry. He also provides bespoke training and have a website to allow users to learn just the module they need at that time.

He specialise in reviewing processes and developing standards, tools, templates and macros to improve the expertise of individuals and efficiency of processes. As an independent consultant with all the proven experience behind him, he offers unbiased expert opinions which can be used by management to make their decisions. His aim is always to drive up Quality by Design.

Specialties: Writing SAS programs to check, modify, analyse and report any kind of data. Developing client specific template programs and generic macros. Developing bespoke training programs to produce well rounded programmers within weeks.