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- Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
- Learn to retrieve and parse information from the internet using the Python library scrapy.
- Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
- Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
- Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
- Learn how to create, customize, and share data visualizations using Matplotlib.
- Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
- Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.
- Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
- Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.
- Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.
