Description

Course Highlights
Foundational Concepts

•  Introduction to the fundamentals of data analysis.
•  Understanding the importance of data in decision-making processes.
• Data Collection and

Data Collection and Preprocessing

• Techniques for acquiring and cleaning data.
• Exploring methods to handle missing or inconsistent data.

Statistical Analysis

•  Statistical Learning both descriptive and inferential statistical methods.
•  Applying statistical concepts to draw meaningful insights from data.

Data Visualization

• Using visualization tools to represent complex data.
•  Interpreting visualizations to communicate findings effectively.

Programming For Data Analysis

•  Introduction to programming languages such as Python or R.
•  Hands-on coding exercises for practical data analysis.

Machine Learning Basics

• Overview of machine learning concepts and algorithms.
• Understanding how machine learning can be applied to data analysis.

Database Manangement

• Basics of database systems and SQL for data manipulation
•  Understanding the role of databases in efficient data handling.

Big Data and Data Warehousing

•  Exploring technologies related to big data.
•  Understanding data warehousing principles for large-scale data management.

Case Studies and Real-World Applications

• Analyzing real-world data analysis projects.
• Developing problem-solving skills through case studies.

Ethical Considerations

• Discussing ethical considerations in data analysis.
•  Promoting responsible and transparent data practices.

Final Project
Integrating knowledge and skills acquired throughout the program.
Presenting a comprehensive data analysis project.

Training Format:
 All materials are made available through our Online Learning Platform
 Students should commit approximately 5-6 hours of their time per week

Materials Provided:
Online delivery of curriculum materials, exercises and templates.

Assignments:
In order to demonstrate their understanding of the course content, students will be required to
submit assignments at the end of every month.