Data Science

Data Science – Complete Course Content (Beginner to Advanced)

Introduction to Data Science

  • What is Data Science?
  • Data Science lifecycle
  • Roles: Data Analyst, Data Scientist, ML Engineer
  • Real-time industry use casesTools overview: Python, Jupyter, SQL, Power BI

Python for Data Science

Python Basics

  • Variables, data types
  • Operators
  • Conditional statements
  • Loops
  • Functions
  • Lambda functions
  • List, Tuple, Set, Dictionary
  • Exception handling

Python for Data Handling

  • NumPy (arrays, operations, reshaping, broadcasting)
  • Pandas (Series, DataFrames, indexing, merging, grouping, missing values)
  • Data cleaning & preprocessing

Data Visualization

Using Matplotlib & Seaborn

  • Line, bar, scatter, histogram, pie charts
  • Heatmaps
  • Pairplots
  • Distribution plots
  • Customizing plots

Dashboards

  • Power BI / Tableau basics
  • Creating interactive dashboards
  • DAX basics (if Power BI chosen)

Statistics & Probability for Data Science

  • Types of data
  • Measure of central tendency (Mean, Median, Mode)
  • Dispersion (Variance, Std. Dev, Range, IQR)
  • Probability basics
  • Bayes theorem
  • Hypothesis testing (Z-test, T-test, Chi-square, ANOVA)
  • Correlation & Covariance
  • Normal distribution
  • Statistical inference

Exploratory Data Analysis (EDA)

  • Data inspection
  • Outlier detection & treatment
  • Handling missing values
  • Feature engineering
  • Encoding techniques
  • Scaling & normalization
  • EDA report preparation

Machine Learning Basics

  • ML workflow
  • Types of ML: Supervised, Unsupervised, Reinforcement
  • Bias-variance concept
  • Cross-validatio
  • Overfitting & underfitting

Supervised Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naïve Bayes
  • Gradient Boosting: XGBoost / LightGBM basics

Unsupervised Learning Algorithms

  • K-Means Clustering
  • Hierarchical Clustering
  • PCA (Dimensionality Reduction)
  • Association Rules
  • Anomaly Detection basics

Model Evaluation & Optimization

  • Train-test split
  • Evaluation metrics: accuracy, precision, recall, F1
  • Confusion matrix
  • ROC & AUC
  • Hyperparameter tuning
  • GridSearchCV / RandomizedSearchCV
  • Feature importance

Deep Learning (Introduction)

  • Neural network basics
  • Activation functions
  • Forward & backward propagation
  • Loss functions
  • Intro to TensorFlow / Keras
  • Building a basic neural network

SQL for Data Science

  • SQL basics
  • Joins, subqueries
  • Group By, Having
  • Window functions
  • Writing analytical queries
  • Real-time project datasets

Deployment & Real-time Concepts

  • Model saving (pickle, joblib)
  • Streamlit basics
  • Integrating ML model with UI
  • Cloud basics (AWS/S3/EC2 intro)

Mini Projects + Capstone

  • EDA project
  • Classification project (banking/HR/healthcare)
  • Clustering project
  • Preparing project report & PPT
  • Mock interviews