1. GETTING STARTED
a) Self-learning (2 - 4 months)
Explore if data science is for you
This is the key to getting started. Two years ago some of us at work formed a study group to review Stats 202 class material. This is what got me excited and started with data analytics. Only 2 of the 5 members of our study group chose to dive deeper into this field (data science is not for everyone).
- Learn basic statistics: Stats 202 coursework is perfect for this
- Learn a statistical tool: I spent 3 months heads-down learning R as a new-bee and had the most fun doing so. Why learn R?
- Solve toy problems: Curiosity is key to data science. If you've questions about your country's economy, crime stats, sports performance, get the data and start answering your questions
- Learn Unix tools: I picked O'Reilly's Data Analysis with Open Source Tools (A hands-on guide for programmers and data scientists) book to read.
- Learn SQL and scripting languages: I know Java, Ruby and SQL. Python is on my list.
There's a lot of training material available online
- Stats 202
- Caltech Data Science course
- Coursera: Introduction to Data Science, Machine learning, Data Analysis, Computing for Data Analysis
- University of California Berkeley - Introduction to Data Science
- Knight Center for Journalism's course on Introduction to Infographics and Data Visualization
- Learn R
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