Course Overview

🎓 **Course Title:** Data Analytics & Data Science Mastery — From Zero to Professional

🧠 **Level:** Beginner to Advanced

🕒 **Duration:** 3 Months

🎯 **Goal:** Become a job-ready Data Analyst & Data Scientist skilled in Python, SQL, Machine Learning, Data Visualization & Real Business Problem Solving.

🔹 **Month 1: Foundations of Data Analytics & Python**

📘 **Week 1: Introduction to Data Analytics**
• What is Data Analytics? (Descriptive, Diagnostic, Predictive, Prescriptive)
• Data Analyst vs Data Scientist
• Data lifecycle: Collection → Cleaning → Analysis → Insights
• Essential tools: Excel, Python, SQL, Power BI/Tableau
• Understanding structured vs unstructured data
• Business use cases: Finance, Healthcare, Retail, AI, Marketing

🧪 **Exercise:** Analyze a sample sales CSV file & extract top 5 insights manually.

📘 **Week 2: Python for Data Analysis**
• Python basics: variables, loops, functions
• Data structures: lists, tuples, dicts
• Libraries: NumPy, Pandas, Matplotlib, Seaborn
• Reading/writing CSV, Excel, JSON
• DataFrames: indexing, slicing, filtering, merging

🧪 **Exercise:** Use Pandas to clean a dataset & compute summary statistics.

📘 **Week 3: Data Cleaning & EDA (Exploratory Data Analysis)**
• Handling missing values, duplicates, inconsistencies
• Outlier detection (IQR, Z-score)
• Data types & conversion
• Visual EDA (pair plots, correlation heatmaps)
• Using Jupyter Notebook effectively
• Feature engineering basics

🧪 **Mini Project:** Clean a real-world dataset (e.g., Titanic, Netflix, Zomato) & create a full EDA report.

📘 **Week 4: SQL for Data Analytics**
• Databases: relational vs non-relational
• CRUD operations
• SQL queries: SELECT, WHERE, GROUP BY, HAVING
• Joins, subqueries, window functions
• Creating reports from SQL data

🧪 **Exercise:** Solve 25 SQL queries from a business analytics dataset.

🔹 **Month 2: Visualisation + Statistics + Machine Learning**

🎨 **Week 5: Data Visualization (BI Tools & Python)**
• Using Matplotlib & Seaborn for charts
• Dashboards with Power BI or Tableau
• KPI design & storytelling with data
• Choosing the right chart for the right insight
• Building professional dashboards

🧪 **Project:** Build a sales dashboard with 10+ charts & KPIs.

📊 **Week 6: Statistics & Probability for Data Science**
• Types of distributions
• Mean, median, mode, variance, skewness
• Hypothesis testing (t-test, chi-square)
• Probability concepts
• Correlation vs causation
• Confidence intervals, p-values

🧪 **Exercise:** Analyze a dataset and determine statistically significant insights.

🤖 **Week 7: Introduction to Machine Learning**
• What is ML? Types: Supervised, Unsupervised
• Regression models (Linear, Polynomial)
• Classification models (Logistic Regression, Decision Trees)
• Clustering: K-means, Hierarchical
• Train-test split, cross-validation
• Model evaluation: accuracy, precision, recall, F1-score

🧪 **Mini Project:** Build a model to predict house prices or customer churn.

🧠 **Week 8: Intermediate ML + Feature Engineering**
• Random Forest, Gradient Boosting
• Feature Scaling: Standardization, Normalization
• Handling imbalanced data (SMOTE, Random Under-sampling)
• Hyperparameter tuning (GridSearch, RandomSearch)
• Model pipelines in scikit-learn

🧪 **Project:** Build a classification model & improve accuracy to 85%+ using feature engineering.

🔹 **Month 3: Advanced ML, Real Projects, Portfolio, Deployment**

💡 **Week 9: Deep Learning & NLP (Basics)**
• Neural Networks: Perceptron, activation functions
• Introduction to TensorFlow/PyTorch
• Text data preprocessing
• Sentiment analysis
• Tokenization, n-grams, TF-IDF
• Intro to word embeddings

🧪 **Mini Project:** Build a sentiment classifier for Amazon/Flipkart reviews.

🧠 **Week 10: End-to-End Data Science Projects**
• Full pipeline: Data → Cleaning → EDA → ML
• Business problem framing
• Feature selection
• Creating reusable ML scripts
• Model interpretability (SHAP, LIME)
• Version control (Git basics)

🧪 **Project:** Predict credit card fraud, diabetes, or loan approval with ML.

🚀 **Week 11: Deployment & Cloud**
• Deploy ML models using: Flask, Streamlit, FastAPI
• Cloud intro: AWS, Google Cloud, Azure
• Using APIs to serve predictions
• Building interactive dashboards with ML integrations

🧪 **Exercise:** Deploy a working ML model using Streamlit or Flask.

🏆 **Week 12: Career Building + Capstone Projects**
• Resume building for Data roles
• LinkedIn profile optimization
• Interview prep (SQL, Python, Case Studies)
• Freelancing platforms: Upwork, Fiverr, Kaggle
• Building a professional portfolio with 5+ projects

🧪 **Capstone Project (choose one):**
• Customer Churn Prediction
• Sales Forecasting Model
• Movie Recommendation System
• Fraud Detection System
• Real-time Dashboard + ML Model

📦 **Tools Covered:**
• Programming: Python, Jupyter, VS Code
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow
• Databases: MySQL, PostgreSQL
• Visualization: Power BI, Tableau
• Deployment: Flask, Streamlit, Git
• Cloud: AWS/GCP/Azure
• Version control: Git/GitHub

🏁 **Final Outcome:**
By the end of this course, you will be able to:
✔ Perform advanced data analytics using Python & SQL
✔ Build dashboards & BI reports
✔ Apply machine learning algorithms to real data
✔ Deploy models as web apps
✔ Build a professional data science portfolio
✔ Become ready for roles like:
• Data Analyst
• Business Analyst
• Data Scientist (Junior)
• ML Engineer (Entry-level)