Data Science Links

Links to great data science articles or videos. Feel free to add stuff via pull request here.

Contents

General Advice on Data Science

What They Don’t Tell You About Data Science 1: You Are a Software Engineer First - Highlighting the importance of software engineering skills for data scientists.
Why Data Science Teams Need Generalists, Not Specialists - As data science is mostly about prototyping, iterating and learning on-the-fly, generalists are better suited than specialists.
Bridging the Gap: from Data Science to Production - Advice on how to best bridge the “deployment gap” in data science.
Compilation of Advice for New and Aspiring Data Scientists - Another great collection of advice: building human capital, choosing a company, acquiring new skills
How can Data Scientists survive layoffs? – How to make data science achieve a real business impact
How do we deliver Data Science in the Enterprise – How to overcome the challenges data science teams face often in an established enterprise

Don’t Call Yourself A Programmer, And Other Career Advice - Real-world career advice for engineers and IT professionals. Highlights the importance of communication, negotiating and self-marketing.
Miscellaneous unsolicited (and possibly biased) career advice - another great collection of advice pieces
So Good They Can’t Ignore You - This article is actually a summary of Cal Newport’s book of the same name. In it, he argues that “following your passion” rarely leads to job satisfaction. Instead, mastering a rare and valuable skill will build ‘career capital’: If you’re really good at what you do, you’ll feel competent and you’re typically granted autonomy, both key factors for occupational happiness. Therefore, pick a career path where you can deliberately improve your skills and aim to be the world-best at what you do.

Evolution from #NoProjects to Continuous Digital - One of many great talks by Allan Kelly, one of the figureheads of the #beyondprojects movement. He explains why the project model leads to countless problems in today’s fast-paced digital economy and suggests a better solution.
A manifesto for Agile data science - Seven principles to help adopt agile practices in data science.
Datenprodukte bauen mit Agile Data Science - “Organisatorische Rahmenbedingungen für Datenprodukte auf Basis von künstlicher Intelligenz”.
What is Minimum Viable (Data) Product? - Adapting the agile concept of a Minimum Viable Product (MVP) for data science.
Agiles Management: 7 Prinzipien, die man kennen sollte - Sieben Prinzipien für agiles Arbeiten.
8 “Simple” Guidelines For Data Projects - Best practices for building impactful data products.

The most difficult thing in data science: politics - How to navigate corporate politics as a data scientist.
How To Survive Corporate Politics as A Data Scientist - 30 tips.
Your Data Science Project Will Fail Unless It Meets These 3 Conditions - Rules for successful data science projects in a corporate environment.
How to Increase Your Influence at Work
Eiffel’s Tower – At the example of the Eiffel Tower’s construction, Nickolas Means illustrates the importance of politics in engineering. But “Every organization is political” doesn’t mean you have to participate in scheming and backstabbing. For healthy politics, reframe Networking as Making Friends, Self-Promotion as Telling Stories and Negotiation as Cooperation. As an engineering manager, rather than assuming the role of shit umbrella and protecting your team from all politics, be a heat shield that takes the pressure off your team if it becomes overwhelming.
Failed “Squad Goals” - Spotify doesn’t use “the Spotify model” and neither should you.
The Hierarchy Is Bullshit (And Bad For Business) - About perceiving the corporate hierarchy as a neutral data structure instead of a dominance ladder

To Improve Your Team, First Work on Yourself - How internal self-awareness, external self-awareness, and personal accountability can improve team dynamics.
How to Develop the Five Soft Skills That Will Make You a Great Analyst - 5 points with advice on self-evaluation and improvement.
How to Work With Stakeholders as a Data Scientist - On communication, expectation management and other important topics when working with stakeholders.
How I increased my impact as a data scientist with one question - Ask stakeholders: “Imagine we’ve done all the hard work and developed a perfect solution. What will you do with it?”

Presentation Tips for Data Professionals - Some general advice on presentation content, design and delivery (not necessarily restricted to data science).
On Presentations – Practice your storytelling skills to engage your audience.
Fix Your Really Bad PowerPoint and 7 tips to create visual presentations - About slide design
What makes a good talk? – A one-page compilation of presentation advice