2019 is The Year of 01. Artificial Intelligence. This is The Year That We Replace One Buzzword with Another.
Feb 2019 | LinkedIn
If you're looking for a job, look for companies that have been investing in data science for years and building over time... not constantly rebranding their failing departments and products to look like they have something "fresh."
Why There Will Be No 02. Data Science Job Titles By 2029
Feb 2019 | Forbes
There is evidence to suggest the rise of the data scientist is a temporary phenomenon, though, which is a normal part of the technology hype cycle. The coming disillusionment with data science job titles will be the following: Many data science teams have not delivered results that can be measured in ROI by executives. The excitement of AI and ML has temporary led people to ignore the basic question: What does a data scientist actually do? For complex data engineering tasks, you need five data engineers for every one data scientist. Automation is coming for many tasks data scientists perform, including machine learning. Every major cloud vendor has heavily invested in some type of AutoML initiative. Succeeding as a data 03. scientist in small companies/startups
Feb 2019 | Medium - Towards Data Science
Small companies don’t need a data scientist, but they need a “data person”. They might call the job “data scientist/engineer/analyst/ninja”, whatever.
How new grads can 04. profit from a Data Science Mentor
Nov 2018 | Medium - Towards Data Science
University grads feel overwhelmed by the many options that there are after university. In Data Science specifically there are many different areas to get started in or focus on, which doesn’t make it easier. This is where a career mentor could help. Why Data Scientists Are 05. Crucial For AI Transformation
Sep 2018 | Forbes
No industry today can say that it is not data driven. With vast volume, speed and variety of data coming from external and internal sources, the need to scientifically approach data is paramount for competitive intelligence of organisations. That is what is keeping CxOs awake at odd hours.
Top 10 roles in AI and 06. data science
Jul 2018 | Medium - Hakernoon
Cassie Kozyrkov (Chief Decision Intelligence Engineer, Google) says that applied data science is highly interdisciplinary. Perspective and attitude matter at least as much as education and experience. In which order to hire to grow a data team: data engineer, decision-maker, analyst, expert analyst... data scientist is the 6th in the list. An Ode to the Type A 07. Data Scientist
Jun 2018 | Medium - Towards Data Science
Type A = analysis, type B = build.
Data Science Leaders: 08. There are too many of you
Jun 2018 | Medium - Towards Data Science
The leadership talent shortage in data science is far, far worse then the technical skills shortage itself. Cassie Kozyrkov says: data science leaders today are what I like to call “transcended data scientists.” People who pursued formal training in science, engineering, or statistics and then, by some miracle, woke up one day to realize that they were more interested in making data useful than chasing mathematical complexity for its own sake. Radical Change Is 09. Coming To Data Science Jobs
Mar 2019 | Forbes
The job of data scientist as we know it today will be barely recognizable in five to 10 years. Instead, end users in all manner of economic sectors will work with data science software the way non-technical people work with Excel today. In fact, those data science tools might be just another tab in Excel 2029. Presents 5 different types of professionals appearing: generalists, specialists, developers and engineers.
Data scientists should 10. not write production code
Feb 2019 | LinkedIn
The code to do data science and the one written for production are fundamentally different because they serve different purposes. How Top-Performing 11. College Grads Fall Into the ‘Prestige Career’ Trap
Jan 2019 | Medium - Business
Business schools funnels the highest achievers into consulting and finance. Not related to data science directly, but relevant to understand one of the personas switching from finance and consulting to data-related professions.
The Data Science Gold 12. Rush: Top Jobs in Data Science and How to Secure Them
Jan 2019 | KDNuggets
"The future of work is through data. Those who aren’t already working in data and analytics will soon be utilizing the technology for its undeniable business benefits. CEOs all over the world are looking for candidates who possess specific knowledge and skills, such as: Education in business data analytics, working knowledge of coding languages and programs, such as PYTHON or R. Ability and propensity to learn new coding languages and programs. Ability to work well with others as well as individually. Critical thinking and problem-solving skills. A college minor or working experience in other, tangentially related fields such as marketing, HR, cyber security, transportation, or customer service." LinkedIn 2018 Emerging 13. Jobs Report
Dec 2018 | LinkedIn
6 out of the 15 emerging jobs are related to AI. AI skills infiltrating every industry and among the fastest-growing skills on LinkedIn (globally saw a 190% increase from 2015 to 2017).
The Data Science Gap 14. Jun 2018 | Medium - Towards Data Science
The people responsible for this “Gap” aren’t really the data scientists; rather the directors, HR departments and even the recruiters in different companies. The Kinds of Data 15. Scientist
Nov 2018 | Harvard Business Review
To hire the right people for the right roles, it’s important to distinguish between different types of data scientist. Science for humans vs data science for machines. One type of data scientist creates output for humans to consume, in the form of product and strategy recommendations. They are decision scientists. The other creates output for machines to consume like models, training data, and algorithms. They are modeling scientists.
What on earth is data 16. science? The quest for a useful definition
Aug 2018 | Medium - Hackernoon
Data science is the discipline of making data useful. Statistics, machine learning, data mining, analytics. Anatomy of a data 17. scientist - infographic
Jun 2018 | Pinterest
Distinguishes between distinctive IT careers and professional fields you may now be aware of.
Demand and Salaries for 18. Data Scientists Continue to Climb
Jan 2018 | Spectrum
The USA is facing a significant shortage of data scientists, a big change from a surplus in 2015. This week, job-search firm Indeed reported that its data indicates the shortage is getting worse: While more job seekers are interested in data-science jobs, the number of job postings from employers has been rising faster than the number of interested applicants. The 20 data science 19. projects at the core of every successful business
April 2017 | Medium - Applied Data Science
Lists the Data Science Business Map and presents the core 20 data science projects emerge from a diagram. Behavioural Segmentation of customers, Recommendation Engine suggesting products to customers, Market Basket analysis suggesting new packages to sell, Funnel Analysis / Lead Prioritisation of potential customers, Attribution Modelling of marketing campaigns, Text Mining of customer feedback, Bots for customer services, Churn Predictio, Fraud Detection, Financial Forecasting, Visualization of key metrics.
Battle of the Data 20. Science Venn Diagrams
Oct 2016 | KDNuggets
Classic post about the historical definitions of data science until 2016. The industry is reviewing this in 2019. The AI Roles Some 21. Companies Forget to Fill
Mar 2019 | Harvard Business Review
Another framework for data roles: translator, business leader, engineer.
How To Write For 22. Plumbers Of Data Science
Feb 2019 | Medium - Plumber of Data Science
Defines the scope of the data engineer profession: Data Processing (e.g. Spark, Kakfa, Flink…), Big Data Platforms (e.g. Hadoop, Google Cloud, AWS, Azure), Data Storage (SQL & NoSQL Databases) ,Data Visualization (BI tools, web apps, mobile apps). Don’t accept articles for pure data scientist topics like: machine learning, deep learning, AI, algorithms, statistics and so on. These articles are far better suited for the Towardsdatascience publication. How It Feels to Learn 23. Data Science in 2019
Feb 2019 | Medium - Towards Data Science
Funny interview-like article listing the main skills an aspiring data scientist needs to acquire in 2019.
Explaining Data 24. Science/Artificial Intelligence
Feb 2019 | Medium - Towards Data Science
A Data Scientist’s answer to: “What do you do ?” The job of a Data Scientist is hard to pinpoint as it is rather new. It revolves around data, statistics, programming and communication. What No One Will Tell 25. You About Data Science Job Applications
Feb 2019 | Medium - Towards Data Science
How to play the game of getting interviews and applications.
Data Scientists: Why are 26. They So Expensive to Hire?
Feb 2019 | KDNuggets
We provide some reasoning behind the high cost factor of hiring a data scientist, including the increasing amount of data ready to be analyzed, the structural shortage of people with the appropriate skills, and more. This is why AI has yet to 27. reshape most businesses
Feb 2019 | MIT Technology Review
For many companies, deploying AI is slower and more expensive than it might seem.
3 Steps To Build A Data 28. Science Portfolio
Jan 2019 | Medium - Towards Data Science
DS candidates need to generate a portfolio public evidence of their data science skills. Internship + Personal Side Projects (Kaggle, Hackathon) + Social Media branding (Medium, KDNuggets, Twitter, Linkedin). What are the Skills 29. Needed to Become a Data Scientist in 2019?
Jan 2019 | Medium - Towards Data Science
Level of education and work experience necessary for a data scientist in 2019.
Implementing a 30. Corporate AI Strategy
Jan 2019 | Medium - Towards Data Science
Nothing new here, but a good summary. Machine learning needs to learn from data, and data comes from records, and records come from processes. a) AI should be boring. b) It directly impacts KPIs, c) It will take time to implement, d) It starts small. e) Perfect never gets delivered, because there’s going to be a better research paper tomorrow anyways. Prioritize Which Data 31. Skills Your Company Needs with This 2×2 Matrix
Oct 2018 | Harvard Business Review
We need to think about transitioning to a more data-skewed skillset. But which skills should you focus on? They present a matrix/canvas to map them.
Data-Driven? Think again 32. Jul 2019 | Medium - Towards Data Science
Without decision-making fundamentals, your decision will be at best inspired by data, but not driven by it. Businesses are hiring data scientists in droves to make rigorous, scientific, unbiased, data-driven decisions. And now, the bad news: those decisions usually aren’t. For a decision to be data-driven, it has to be the data — as opposed to something else entirely — that drive it. A New, Faster Approach 33. To Data Science And Machine Learning
Jan 2019 | Medium - Towards Data Science
For every business, data science is the foundation of enabling a successful transformation into an AI-powered enterprise. Data analytics and machine learning drive smarter business decisions, greater operational efficiency, and higher levels of customer satisfaction.
Why you shouldn’t be a 34. data science generalist
Jan 2018 | Medium - Hackernoon
DS isn’t a single, well-defined field, and companies don’t hire generic, jack-of-all-trades, but rather individuals with very specialized skill sets. The Top 3 Skills That 35. Aspiring Data Scientists are Missing - Part 1: The Engineering Mindset
Jan 2018 | LinkedIn
Engineering is critical part of being a data scientist. It's not about hacking together a one-off solution. It's about building systems to solve problems. Systems that are testable, robust, efficient, scalable, and portable. Systems that you can resuse. Systems that can accommodate new data sources and challenges in the future.
Data Science Project 36. Flow for Startups
Jan 2018 | Medium - Towards Data Science
Presents a small startup's AI project flow, where a small team of data scientists (usually one to four) run short and mid-sized projects led by a single person at a time. There might not be a data engineer to perform these duties. In this case the data scientist is usually in charge of working with developers to help with these aspects. Alternatively, the data scientist might do these preparations, if they happen to be the rarest of all of God’s beasts: the Full Stack Data Scientist! ✨ ✨. You can thus replace data engineer with data scientist whenever it is mentioned, depending on your environment. Data Scientist vs Data 37. Engineer
Feb 2017 | DataCamp
Explains the difference between data engineers and data scientists: responsibilities, tools, languages, job outlook, salary, etc. Lots of interesting dashboards comparing the two.
Data Scientist vs Data 38. Engineer, What’s the difference?
Jun 2018 | CogntiveClass.ai
Classic post from 2016. The emergence of big data, new roles began popping up in corporations and research centers — namely, Data Scientists and Data Engineers. The quick article is an overview of the roles of the Data Analyst, BI Developer, Data Scientist and Data Engineer. How to Hire for 39. Technical, Data-Centric Roles
Jan 2019 | Dataiku
Who to target (also passive candidates who already have a job, not only graduates form top colleges), how to interview (present a problem, not only technical tricky questions only).
Four routes to becoming 40. a data scientist
Feb 2019 | Big Cloud
a. Undergraduate Degree + Experience (Standard); b. MOOC Data Science Courses: 6-18 months part time, Free – $1k; c. Data Science Bootcamp: 2-3 months, $1k-$14k; d. Master’s Degree: 9-20 months, $20k-$70k. It is important to remember that the current leaders within Data Science have “learnt on the job.” They have created an entirely new industry and although “Data Scientist” is a rather vague title, to work in an ever technologically led industry does not require years of scientific research under your belt.
A good theoretical background from a quality MOOC, Bootcamp or ideally Master’s degree, plus a few years of practical experience will set you well on your way. The absolute beginner’s 41. guide for data science rookies
Mar 2019 | Towards Data Science
List of foundations, libraries, and where to learn.
Six Recommendations for 42. Aspiring Data Scientists
Mar 2019 | Medium - Towards Data Science
How to build experience before landing a job. Get hands-on with cloud computing. Create a new data set. Glue things together (multiple tools). Stand up a service. Create a stunning visualization. Write a white paper To get hired as a data 43. scientist, don’t follow the herd
Oct 2018 | Medium - Towards Data Science
Replicate papers, get out of the comfortable zone doing different projects in different subjects and different libraries, learn boring things, do annoying things like send messages to people, comment posts, and attend meetups, do things that seem crazy.