Becoming a Data Scientist - Curriculum via Metromap ← Pragmatic Perspectives. Data Science, Machine Learning, Big Data Analytics, Cognitive Computing …. well all of us have been avalanched with articles, skills demand info graph’s and point of views on these topics (yawn!).
One thing is for sure; you cannot become a data scientist overnight. Its a journey, for sure a challenging one. But how do you go about becoming one? Where to start? When do you start seeing light at the end of the tunnel? Given how critical visualization is for data science, ironically I was not able to find (except for a few), pragmatic and yet visual representation of what it takes to become a data scientist.
FundamentalsStatisticsProgrammingMachine LearningText Mining / Natural Language ProcessingData VisualizationBig DataData IngestionData MungingToolbox Each area / domain is represented as a “metro line”, with the stations depicting the topics you must learn / master / understand in a progressive fashion. PS: I did not want to impose the use of any commercial tools in this plan. 7 Steps for Learning Data Mining and Data Science. How to learn data mining and data science?
I outline seven steps and point you to resources for becoming a data scientist. By Gregory Piatetsky, Oct 10, 2013. comments I am frequently asked - how to learn Data Mining and Data Science? Here is my summary. You can best learn data mining and data science by doing, so start analyzing data as soon as you can! Here are 7 steps for learning data mining and data science. Languages: Learn R, Python, and SQLTools: Learn how to use data mining and visualization toolsTextbooks: Read introductory textbooks to understand the fundamentalsEducation: watch webinars, take courses, and consider a certificate or a degree in data scienceData: Check available data resources and find something thereCompetitions: Participate in data mining competitionsInteract with other data scientists, via social networks, groups, and meetings 1.
Recent KDnuggets Poll found that the most popular languages for data mining are R, Python, and SQL. 2. 3. 4. 5. 6. How to Get Started in Data Science. A lot of people ask me: how do I become a data scientist?
I think the short answer is: as with any technical role, it isn’t necessarily easy or quick, but if you’re smart, committed and willing to invest in learning and experimentation, then of course you can do it. In a previous post, I described my view on “What is a data scientist?” : it’s a hybrid role that combines the “applied scientist” with the “data engineer”. Many developers, statisticians, analysts and IT professionals have some partial background and are looking to make the transition into data science. And so, how does one go about that? Java Developers If you’re a Java developer, you are familiar with software engineering principles and thrive on crafting software systems that perform complex tasks.
A good first step is to understand the various algorithms in machine learning: which algorithms exist, which problems they solve and how they are implemented. Python Developers Statisticians and applied scientists Business analysts.