Foundations in Python for Scaling Data Analysis

This course is designed to introduce data analysts with little to no prior Python experience to programming in Python. It covers Python installation, basic programming concepts, data exploration and manipulation using pandas, and data visualization in Matplotlib and seaborn. The course is designed to provide a foundational understanding of Python in the context of data analysis and visualization. Upon completion of this course, learners will have the Python programming skills to do data analysis and the foundational skills needed to learn how to do machine learning in Python. 

Key Skills Covered in this Course: 

• Data Analysis
  • Exploratory Data Analysis
  • Descriptive Statistics
• Data Visualization
  • Best Practices
  • Visualization with matplotlib, seaborn, and plotly
• Jupyter Notebooks
  • Develop code in an interactive, shareable notebook environment
• Python Programming
  • Variables, data types, and data structures
  • Data Manipulation with pandas

Prerequisites: 

• Knowledge of descriptive statistics 
• Experience with Exploratory Data Analysis

Data Requirements:

• Provide the instructor with two datasets for an exploratory data analysis at least 4 weeks before the course start date. 
• The primary dataset should include:
  • At least 5000 observations (rows)
  • 2 or more categorical variables (columns)
  • 3 or more numeric variables (at least one should be continuous)
  • 1 or more columns with dates
  • A column with messy string data (long course)
• The second dataset should include:
  • a column that can be used as a join key to the first dataset
  • and at least one other column

Session length: 

Medium Course: 9 hours

Long Course:  12 hours

*Session time breakdown is flexible, with the caveat that no session is longer than 2 hours.