Advancing into Data Analytics from Excel to Python

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This course is provided by Big Data Trunk for Stanford Technology Training Program but a limited few seats available to the public.

Students of this class may have opportunity to be considered for Internship with Big Data Trunk.

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

• Foundations of Analytics in Excel
• Variables in Excel
• Exploratory Data Analysis in Excel
• Data Visualization in Excel

3. Introduction to the Python Programming Language

• Installing Anaconda

4. Milestone 1: How to use Jupyter Notebooks

Python Essentials

• Introduction to Pandas
• Using Pandas to access Excel files
• Data Analysis with Pandas

5. Milestone 2: Perform exploratory data analysis using Pandas

• Using Python for data wrangling
• Using Python to manipulate Excel files
• Data Visualization in Python: Matplotlib, Pandas, Seaborn

6. Milestone 3: Perform data visualization using Python

• Inferential Statistics and Hypothesis Testing in Python
• Correlation and Linear Regression using Excel and Python
• 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.