🚀 Data Science Mastery Program – From Scratch to Advanced ML

✅ Program Overview:

This Data Science program is a comprehensive, beginner-friendly journey designed to take learners from zero programming knowledge to mastering key concepts in exploratory data analysis, machine learning, and feature engineering. The course focuses on applied learning through mini case studies, hands-on exercises, and practical examples.

Whether you are a student, working professional, or career switcher, this program is ideal for building real-world skills in Python programming, data analysis, and machine learning.

🧑‍💻 Module 1: Python Programming for Data Science

Keywords: Learn Python for Data Science, Python Basics, Data Types, Loops, Functions, Pandas, NumPy

What You Will Learn:

• Python fundamentals with hands-on exercises

• Working with lists, dictionaries, functions, loops

• Data wrangling with pandas and numpy

• File handling and basic data parsing

📌 Outcome: Ability to manipulate and analyze structured data using Python.

📊 Module 2: Exploratory Data Analysis (EDA) and Statistics

Keywords: Data Visualization, EDA, Matplotlib, Seaborn, Descriptive Statistics, Linear Algebra for Data Science

What You Will Learn:

• Data cleaning and preparation techniques

• Visualizing data using Matplotlib and Seaborn

• Applying statistical summaries: mean, median, mode, variance, standard deviation

• Basic linear algebra: vectors, matrices, and dot product

• Correlation, covariance, and data distribution insights

📌 Outcome: Proficiency in understanding data patterns and communicating insights visually.

📚 Module 3: Case Study-Based Data Storytelling

Keywords: Real-World Data Science Projects, Problem Framing, Data Insights, Mini Case Studies

What You Will Learn:

• Framing a business/data problem

• Building a structured case study around a small dataset

• Extracting insights and defining objectives

• Preparing the data for model building

📌 Outcome: Ability to connect business context with data science methodology.

🧠 Module 4: Feature Engineering and Data Preparation

Keywords: Feature Engineering Techniques, Categorical Encoding, Outlier Detection, Feature Scaling

What You Will Learn:

• Creating new features from raw data

• Encoding categorical variables (Label Encoding, One-Hot Encoding)

• Handling outliers and missing values

• Normalization and standardization

📌 Outcome: Readiness to build robust machine learning models with cleaned, transformed data.

📈 Module 5: Supervised Machine Learning

Keywords: Regression Algorithms, Classification Models, Linear Regression, Logistic Regression, KNN, Decision Trees, Random Forest, Ensemble Learning

What You Will Learn:

• Linear and logistic regression from scratch

• K-Nearest Neighbors (KNN) for classification

• Decision Trees and Random Forests

• Homogeneous vs Heterogeneous Ensembles (Bagging, Boosting)

• Performance metrics: Accuracy, Precision, Recall, F1 Score

📌 Outcome: Ability to model, train, and evaluate predictive models.

🧪 Module 6: Unsupervised Learning and Dimensionality Reduction

Keywords: Clustering, K-Means, Principal Component Analysis (PCA), SVM, Pattern Recognition

What You Will Learn:

• K-Means Clustering for pattern recognition

• Support Vector Machines (SVM) for classification tasks

• Principal Component Analysis for dimensionality reduction

• Applying ML techniques to identify hidden structures in data

📌 Outcome: Understand complex data distributions and apply unsupervised learning methods.

💼 Capstone Case Studies and Project

Keywords: Data Science Projects, End-to-End ML Pipeline, Business Problem Solving

What You Will Build:

• End-to-end ML pipeline project

• Data to insight storytelling using all modules

• Business case framing, solution design, and model evaluation

📌 Outcome: Portfolio-ready project that demonstrates real-world problem-solving.

🎯 Key Takeaways

• Build a strong foundation in Python programming for data analysis

• Perform exploratory data analysis with statistical insights

• Engineer features that enhance model performance

• Apply both supervised and unsupervised machine learning algorithms

• Solve real-world problems with data-driven decision making

• Create portfolio-ready case studies to showcase your skills

📌 Who Is This Program For?

• Aspiring Data Scientists

• Data Analysts and Business Analysts

• Career Switchers to Data Science

• Working Professionals seeking upskilling

• Final-year Students looking to build practical skills