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- 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.
- Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
- Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
- Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
- Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
- Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
- Gain a 360° overview of how to explore and use Power BI to build impactful reports.
- Discover how data engineers lay the groundwork that makes data science possible. No coding involved!
- Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.
