Python, used extensively to analyze data, was key to Google’s overnight success as a search engine in the late nineties, and is widely used in database operations and analytics. Python is also user-friendly. Get up to speed with Python so that you can use it with confidence in data analysis. Start by learning how Python works with numbers and some fundamentals in logic and conditions. Then, learn how to control your programs with for and while loops, how to contain data in lists, tuples, and dictionaries, and how to read, write and work with files. After you become familiar with data and Python, put your newfound knowledge to work. Literally, Python will do the statistics, predictions and visualizations for you. Practice using linear and logistic regressions to discover relationships among data, predict future outcomes and test new data. If you have never taken a course in statistics or just need a refresher, take this chance to learn and review the basics in this course before applying them to analytics. Practice all these skills with realistic datasets. Data analytics is used to make key, real-time decisions every day in finance and business, and for longer term planning and review in nearly all fields. It also underlies machine learning, which is used to automate everything from personal assistance to mining.