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.