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Advancing into Data Analytics from Excel to Python

Before each live online session, Tech Training will provide a Zoom link for live online classes, along with any required class materials.

This six-hour session will review the foundations of data analytics using Excel and then transfer and advance that knowledge to perform a complete data analysis using the Python programming language.

Prerequisite: Learners should have an understanding of Basic Programming and Excel.

You will have the opportunity to learn how to conduct exploratory data analysis, data visualization and hypothesis testing, and how to use Python to access and manipulate Excel files. At the end of the course, you will be able to perform a complete data analysis using Python. 

Learning Objectives:

During this course, you will have the opportunity to learn how to:

  • Understand the Foundations of Analytics in Excel
  • Explore Variables in Excel
  • Understand Exploratory Data Analysis
  • Understand the Foundations of Inferential Statistics and Hypothesis Testing
  • Use the Python Programming Language for Data Analysis
  • Access Excel Files Using Python
  • Perform Data Visualization and Exploration in Python
  • Perform More Efficient and Deeper Data Analyses using Python
  • Explore Correlation and Linear Regression in Excel and Python
  • Use Python to Manipulate Excel Files and to perform Machine Learning

Topic Outline:

  1. Overview of Data Analytics
  2. Excel Review
    1. Foundations of Analytics in Excel
    2. Variables in Excel
    3. Exploratory Data Analysis in Excel
    4. Data Visualization in Excel 
  3. Introduction to the Python Programming Language
    1. Installing Anaconda 
  4. Milestone 1: How to use Jupyter Notebooks
    1. Python Essentials
    2. Introduction to Pandas
    3. Using Pandas to access Excel files
    4. Data Analysis with Pandas
  5. Milestone 2: Perform exploratory data analysis using Pandas
    1. Using Python for data wrangling
    2. Using Python to manipulate Excel files
    3. Data Visualization in Python: Matplotlib, Pandas, Seaborn
  6. Milestone 3: Perform data visualization using Python
    1. Inferential Statistics and Hypothesis Testing in Python
    2. Correlation and Linear Regression using Excel and Python
    3. Using Python to perform machine learning
  7. Milestone 4: Perform complete Python data analysis
  8. Conclusion: Data Analytics in the real world, and next steps.
Antony Ross

Antony Ross

Antony originally attained a degree in psychology with an emphasis in sport psychology. He began working with athletes and eventually chose to pursue a graduate degree in exercise physiology. He conducted research in muscle physiology while teaching at USC and, subsequently, UCLA.

Custom training workshops are available for this program

Technology training sessions structured around individual or group learning objectives. Learn more about custom training.

Special Group Rates

For groups of 5 or more, special rates are available. Please contact [email protected] for more details.

University IT Technology Training sessions are available to a wide range of participants, including Stanford University staff, faculty, students, and employees of Stanford Hospitals & Clinics, such as Stanford Health Care, Stanford Health Care Tri-Valley, Stanford Medicine Partners, and Stanford Medicine Children's Health.

Additionally, some of these programs are open to interested individuals not affiliated with Stanford, allowing for broader community engagement and learning opportunities.