Data Science
Data Science + AI/ML
Master Data Science, Machine Learning, and AI in 6 months. Learn Python, Statistics, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and basics of Deep Learning. Real datasets, real projects, real career outcomes. Offline in Mughalsarai + live online batches available.
5 (12 reviews)
• 6 months • beginner • 78% placement Skills You'll Learn
Python Pandas NumPy Matplotlib Seaborn Scikit-learn Machine Learning Data Science Statistics Deep Learning TensorFlow AI Data Analysis EDA
Course Curriculum
Week 3 weeks: Python for Data Science
- Python setup
- VS Code
- Jupyter Notebook
- Variables
- Data types
- Strings
- Lists
- Tuples
- Dictionaries
- Sets
- Loops
- Functions
- File handling
- CSV and JSON
- pip install
- Virtual environments
- List comprehension
Week 2 weeks: NumPy — Numerical Computing
- What is NumPy
- ndarray
- Creating arrays
- Shape and reshape
- Indexing and slicing
- Broadcasting
- Mathematical operations
- Statistical functions (mean median std)
- Random module
- Array performance vs lists
- Matrix operations basics
Week 3 weeks: Pandas — Data Manipulation
- Series and DataFrame
- read_csv read_excel
- head() tail() info() describe()
- Selecting columns rows
- Filtering (boolean indexing)
- Sorting
- Groupby and aggregation
- Merge join concat
- Pivot tables
- Handling missing values (fillna dropna)
- Data type conversion
- apply() and lambda
- String operations in Pandas
- Date and time handling
Week 3 weeks: Statistics & Probability
- Mean Median Mode
- Variance and Standard Deviation
- Percentiles and Quartiles
- Normal distribution
- Skewness and Kurtosis
- Probability basics
- Bayes theorem (intuition)
- Correlation (Pearson Spearman)
- Hypothesis testing (t-test chi-square)
- p-value and significance
- Central Limit Theorem
- All calculations done in Python
Week 2 weeks: Data Visualisation — Matplotlib & Seaborn
- Matplotlib basics (line bar scatter hist)
- Subplots and figure size
- Labels titles legends
- Seaborn (countplot barplot boxplot heatmap violinplot pairplot)
- Customising colour palettes
- Visualisation best practices
- Telling a story with charts
- EDA visualisation workflow
Week 2 weeks: Exploratory Data Analysis (EDA) Project
- Choosing a real dataset (Kaggle)
- Data loading and inspection
- Cleaning and preprocessing
- Univariate and bivariate analysis
- Correlation heatmap
- Outlier detection (IQR Z-score)
- Feature insights and summary
- EDA report writing
- Presenting findings
Week 3 weeks: Machine Learning — Regression
- What is Machine Learning
- Supervised vs Unsupervised
- Train test split
- Linear Regression (theory + code)
- Multiple Linear Regression
- Polynomial Regression
- Evaluation metrics (MAE MSE RMSE R2)
- Feature scaling (StandardScaler MinMaxScaler)
- Handling categorical variables (LabelEncoder OneHotEncoder)
- Overfitting and underfitting
- Project: House Price Prediction
Week 3 weeks: Machine Learning — Classification
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier
- K-Nearest Neighbours (KNN)
- Support Vector Machine (SVM)
- Evaluation metrics (accuracy precision recall F1 confusion matrix ROC-AUC)
- Cross-validation
- Hyperparameter tuning (GridSearchCV)
- Project: Customer Churn Prediction
- Project: Loan Approval Classifier
Week 2 weeks: Unsupervised Learning & Feature Engineering
- K-Means Clustering
- Elbow method
- Silhouette score
- PCA (Principal Component Analysis)
- Dimensionality reduction
- Feature engineering techniques
- Feature selection (correlation variance importance)
- Handling imbalanced datasets (SMOTE overview)
- Pipeline concept in Scikit-learn
Week 2 weeks: Deep Learning Basics
- What are Neural Networks
- Perceptron concept
- Activation functions (ReLU Sigmoid Softmax)
- TensorFlow and Keras setup
- Building a simple neural network
- Training and evaluation
- Image classification (MNIST handwritten digits)
- NLP basics (text preprocessing tokenisation)
- Sentiment analysis with simple model
- GPU vs CPU (awareness)
- What to learn next in deep learning
Week 1 week: Git, Deployment & Tools
- Git and GitHub for data projects
- Jupyter Notebook best practices
- Saving and loading models (pickle joblib)
- Streamlit basics (build a simple ML web app)
- Deploying a Streamlit app (Streamlit Cloud)
- Kaggle account and competitions overview
- Google Colab for heavy computation
Week 3 weeks: Capstone Project & Career Preparation
- Capstone project (end-to-end ML pipeline)
- Dataset selection
- EDA and preprocessing
- Model building and comparison
- Model evaluation and selection
- Documentation and README
- GitHub upload
- Streamlit demo app
- Resume building for data roles
- LinkedIn optimisation
- Portfolio presentation
- Mock interviews (technical + HR)
- Freelance data analysis guide
- Career roadmap (analyst to scientist to engineer)
Your Instructor
TI
TechPath Instructor
Senior Data Scientist & ML Trainer
10+ years experience
Frequently Asked Questions
₹20,000 ₹30,000 33% OFF
EMI from ₹3,333/month
This course includes:
- 6 months of live training
- Max 25 students/batch
- Certificate of completion
- Lifetime access to content
- Placement assistance
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