Course Outline for Data Analysis & Governance using python

Course Outline for Data Analysis & Governance using python

This course is designed for busy business professionals who want to have a grasp of the leading field in Information Technology - which is Data Science and Analytics. Data analytic techniques covered here shall enable you to take raw data and uncover patterns to extract valuable insights from it while remaining within a Data Governance framework.

Therefore this course shall give a high level understanding of the process and techniques of extracting information from data, ascertain patterns, and analyze the data to make predictions and classifications while adhering to data governance guidelines.

Duration

3 days

Learning Outcome

This course will use python as the tool of choice for performing data analytics. By the end of the course, the learner shall be able to:

• Perform basic programming tasks on python.
• Use python’s Pandas library for data extraction and cleaning.
• Use matplotlib for data visualization.
• Use seaborn for data visualization.
• Understand the process and lifecycle of Data Analytics.
• Perform basic analytics on data.
• How to structure your data governance program
• How to maintain a data governance program
• Have enough knowledge to learn more as needed.

Requirements:

• Internet connection.
• Google account (so that I can share resources at real time in the cloud).
• Personal Computer.
• Ability to share documents to and from Google Drive.

Course Content

The content of the course shall cover:

1. Programming in python.
   1. Syntax.
   2. Strings.
   3. Numbers.
   4. Lists.
   5. Dictionaries.
   6. Conditions.
   7. Loops.
   8. Functions.
   9. Lambda.
   10. Libraries.
2. Pandas.
   1. Sources
   2. Connections
   3. Scraping
3. Visualizations.
   1. Matplotlib.
   2. Seaborn.
4. Data Analysis and Governance.
   1. Basics of data governance
   2. Cleaning Data.
   3. Splitting Data.
   4. Modeling Data.
   5. Train-Test Split.
   6. K-Means.
   7. KNN.
   8. Decision Trees.
   9. Dimensionality Reduction.

And IF time permits:

1. Recommender Systems:
   1. User Based Collaborative Systems.
   2. Item Based Collaborative Systems.