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Course Details:
Concepts Covered:
- Understand the fundamentals of statistics
- Learn how to work with different types of data
- How to plot different types of data
- Calculate the measures of central tendency, asymmetry, and variability
- Calculate correlation and covariance
- Distinguish and work with different types of distributions
- Estimate confidence intervals
- Perform hypothesis testing
- Make data driven decisions
- Understand the mechanics of regression analysis
- Carry out regression analysis
- Use and understand dummy variables
- Understand the concepts needed for data science even with Python and R!
Course Details:
Concepts Covered:
- Design for an Audience
- Storytelling Data Visualization
- Gestalt Principles and Pre-Attentive Attributes
- Matplotlib
Course Details:
Concepts Covered:
- Learn how to use data visualization to explore data
- Learn how and when to use the most common plots
Course Details:
Concepts Covered:
- Dictionaries and Frequency Tables
- Functions: Fundamentals
- Functions: Intermediate
- Jupyter Notebook
Course Details:
Concepts Covered:
- Do a data cleaning project from start to finish
- Hone your data cleaning skills
Course Details:
Concepts Covered:
- Query external data sources using an API
- Scrape data from the web
Course Details:
Concepts Covered:
Course Details:
Concepts Covered:
- Joining Data in SQL
- Intermediate Joins in SQL
- Building and Organizing Complex Queries
- Table Relations and Normalization
- Querying SQLite from Python
Course Details:
Concepts Covered:
- Read and explore documentation
- Inspect files
- Perform basic text processing
- Define different kinds of output
- Redirect and pipe output
- Employ streams and file descriptors
Course Details:
Concepts Covered:
- Employ the command line for Data Science
- Define Important command line concepts
- Modify the behavior of commands with options
- Navigate the filesystem
- Employ glob patterns and wildcards
- Manage users and permissions