Join Lillian Pierson, P.E. for an in-depth discussion in this video What you should know, part of Python for Data Science Essential Training Part 1.
- [Instructor] As far as what you should know for this course, you want to have Python 3.6 or above installed on your machine, and you also want to make sure that you have done an Anaconda install. That's just because you're going to need to use the libraries that come with that installation. We are going to be working with Jupyter notebooks, and so in case you have never worked with Jupyter notebooks before, then you can go check out the Jupyter noteboooks video in Python Programming Efficiently. As far as math requirements, there is no real math requirement for this course. I'm going to take you through what you should know during the course.
AuthorLillian Pierson, P.E.
- Why use Python for working with data
- Filtering and selecting data
- Concatenating and transforming data
- Data visualization best practices
- Visualizing data
- Creating a plot
- Creating statistical data graphics
- Performing basic math and linear algebra
- Correlation analysis
- Multivariate analysis
- Data sourcing via web scraping
- Introduction to natural language processing
- Collaborative analytics with Plotly
Skill Level Intermediate
Python Statistics Essential Trainingwith Michele Vallisneri2h 58m Intermediate
1. Introduction to the Data Professions
High-level course road map1m 28s
2. Data Preparation Basics
3. Data Visualization 101
4. Practical Data Visualization
5. Basic Math and Statistics
6. Data Sourcing via Web Scraping
7. Collaborative Analytics with Plotly
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