And one of the major reasons why I use it for data science projects especially machine learning is that it is very light. Python is one of the languages that is really self describing. "For data science it is most important to understand and implement algorithms. Learn Haskell, it'll improve all your other programming skills even if you never use it in a project." - Gordon
For longevity in marketable skills, C and C++ are essential base skills. In simple terms today I like Python and JavaScript. If you're into software development, keep active in several, and strive to learn a new and different culture language every year or two. "I've been in software development for 50 years (now retired) and languages come and go. Skills you'll gain: Python Programming, Data Science, Data Analysis, Data Structures, Statistical Programming, Machine Learning, Data Mining, Regression, Machine Learning Algorithms, Data Visualization, General Statistics, Basic Descriptive Statistics, SQL, Applied Machine Learning, Statistical Analysis, Computer Programming Tools, Data Analysis Software, Machine Learning Software, Software Visualization, Databases, Programming Principles, Exploratory Data Analysis, Computer Programming, Statistical Visualization, Algebra, Data Management, Database Theory, Data Visualization Software, R Programming, Statistical Machine Learning, Statistical Tests, Deep Learning, Probability & Statistics, Database Application, Extract, Transform, Load, Plot (Graphics), Devops Tools, SPSS, Estimation, Interactive Data Visualization, Algorithms, Database Administration, Geovisualization, Reinforcement Learning, Theoretical Computer Science, Big Data, Business Analysis, Computational Logic, Correlation And Dependence, Econometrics, Entrepreneurship, Marketing, Mathematical Theory & Analysis, Mathematics, Spreadsheet Software, Storytelling, Supply Chain Systems, Supply Chain and Logistics, Writingįollowing is an excerpt from a Coursera Community forum topic about what programming languages our Community members use.
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