About This Course
The Data Science & Data Analytics course is designed to help learners develop the skills required to analyze data, extract meaningful insights, and build predictive models using Python and industry-standard tools. This course covers the complete data science workflow, including data collection, data cleaning, data analysis, visualization, statistics, and machine learning.
Students will learn how to work with real-world datasets using powerful Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. The course also introduces predictive modeling techniques like regression, classification, clustering, and forecasting.
Through hands-on exercises, projects, and practical examples, learners will gain real-world experience in solving business problems using data. By the end of this course, students will be equipped with job-ready skills for careers such as Data Analyst, Data Scientist, and Business Analyst in today’s data-driven industry.
Learning Objectives
Material Includes
- Structured video lessons with practical demonstrations
- Real-world data science and analytics projects
- Practice exercises and assignments
- Downloadable datasets and code files
- Quizzes to test your understanding
- Hands-on implementation using Python and data science tools
- Lifetime access to course materials
- Course completion certificate
Requirements
- Basic computer knowledge
- No prior Data Science or Python experience required (beginner-friendly)
- A computer (Windows, Mac, or Linux)
- Internet connection to access course materials
- Python and Anaconda installation (setup guidance provided)
- Willingness to practice and work on real-world projects
Target Audience
- Beginners who want to start a career in Data Science or Data Analytics
- Students pursuing Computer Science, IT, Statistics, or related fields
- Job seekers preparing for Data Analyst or Data Scientist roles
- Software developers and IT professionals upgrading to data science skills
- Business professionals who want to make data-driven decisions
- Career switchers entering high-demand analytics and AI fields
Curriculum
Module 1: Introduction to Data Science (HTML)
Introduction to Data Science
Python for Data Science
Industry Applications and Use Cases
Data Science Project Lifecycle
Python Environment Setup
Module 2: Python Essentials (Core)
Module 3: Scientific Libraries for Data Science
Module 4: Data Importing and Exporting
Module 5: Data Manipulation and Cleaning
Module 6: Data Analysis and Visualization
Module 7: Statistics for Data Science
Module 8: Machine Learning Basics
Module 9: Machine Learning Algorithms and Applications
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