Course Description
The Data Analytics course provides a comprehensive introduction to the field of data analysis. Students will learn how to collect, process, and analyze data using various tools and techniques. The course covers fundamental concepts, practical applications, and advanced analytical methods, ensuring that participants are well-prepared for careers in data analytics.
Course Objectives
Understand the fundamentals of data analytics and its importance in decision-making.
Gain proficiency in data collection, cleaning, and preprocessing techniques.
Learn to use popular data analytics tools such as Excel, Python, and R.
Develop skills in statistical analysis and data visualization.
Apply analytical techniques to real-world problems across different sectors.
Target Audience
This course is ideal for:
Aspiring data analysts looking to start their careers.
Professionals seeking to enhance their data analysis skills.
Students from various disciplines interested in leveraging data for insights.
Course Content
Module 1: Introduction to Data Analytics
Overview of Data Analytics
Importance of Data in Business
Types of Data Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
Module 2: Data Collection and Preparation
Data Sources: Primary vs. Secondary Data
Data Collection Methods
Data Cleaning Techniques
Module 3: Data Analysis Tools
Introduction to Excel for Data Analysis
Basics of Python for Data Analytics
Using R for Statistical Analysis
Module 4: Statistical Analysis
Descriptive Statistics
Inferential Statistics
Hypothesis Testing
Module 5: Data Visualization
Importance of Data Visualization
Tools for Data Visualization: Tableau, Matplotlib, Seaborn
Creating Effective Visualizations
Module 6: Real-World Applications
Case Studies in Various Industries
Hands-on Projects
Building a Portfolio of Data Analytics Projects
