01. Theory:
Use Coursera and EdX for theory, concepts, and applications of probability, statistics, linear algebra, calculus, and machine learning.
2. Data Visualisation:
Tableau and PowerBI are easy-to-use tools with GUI facilities for data visualization and business intelligence. Matplotlib in Python and ggplot in R are good packages for visualization.
3. Programming:
Python and R are great free tools that you can download from the internet. RStudio for R and Anaconda for Python are two good tools to write and test codes. You can use Datacamp for learning and hands-on practice of Python and R. Then practice machine learning algorithms using Python or R.
4. Big Data:
For Big Data, AWS or Azure can be used for storage and Spark can be used for processing. You need to pull the data from the database, process or filter the data and bring it to a usable format. For getting and processing data SQL is a very good tool. You can use the dplyr package in R or pandas in Python for data processing tasks.
