Course Overview
Start your data science journey from scratch. Learn data wrangling with Pandas, numerical computing with NumPy, statistical visualizations with Matplotlib & Seaborn, and supervised machine learning models with Scikit-Learn.
What You'll Learn
- Clean, transform, and analyze messy real-world datasets in Python
- Build predictive classification and regression machine learning models
- Create executive-ready data visual dashboards
- Understand statistical inference, hypothesis testing, and model evaluation metrics
Key Skills Covered
Structured Learning Roadmap
Step-by-step milestone progression for Data Science Course
Establish prerequisite knowledge in Data Science, setup local development environments, and master fundamental syntax and concepts.
Variables, loops, functions, lists, dictionaries, and Jupyter notebook workflows. Array operations, DataFrame filtering, handling missing values, and group-by aggregations.
Creating histograms, scatter plots, heatmaps, and trendlines with Seaborn. Linear Regression, Logistic Regression, Decision Trees, Random Forests, and cross-validation.
Synthesize all course modules into a production-grade portfolio project, undergo self-assessment quizzes, and earn your official Money Mitra Network certificate.
Course Curriculum
Module 1: Python Fundamentals for Data Science
Module 2: Data Wrangling with NumPy & Pandas
Module 3: Data Visualization & Exploratory Analysis
Module 4: Supervised Machine Learning Models
Instructor Profile
Dr. Meera Nair
Ph.D. in Computer Science with 14+ years designing scalable machine learning algorithms, database engines, and analytics pipelines.
Prerequisites & Audience
No prior programming experience required. Basic high-school mathematics.
Beginners, career switchers, and analysts wanting to learn Python-based data science.