🎓 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)