Data Science Training
Learn data analysis, visualization, statistics, and machine learning for real-world insights. Extract actionable knowledge from complex datasets.
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Data Science Curriculum
- Introduction to Python & Installation (Jupyter, VS Code, Anaconda)
- Variables, Data Types, Loops, Functions & Lambda Expressions
- File Handling, Exception Handling & Working with JSON/CSV
- NumPy: Arrays, Broadcasting, and Vectorized Operations
- Pandas: DataFrames, Groupby, Merging, Pivot Tables & Multi-indexing
- Data Visualization with Matplotlib & Seaborn
- Setting up Virtual Environments with Conda & Pip
- Project: Automated Data Cleaning Script for a Real-world CSV Dataset
- Descriptive Statistics: Mean, Median, Mode, Variance & Standard Deviation
- Probability Theory: Conditional Probability & Bayes' Theorem
- Statistical Distributions: Normal, Binomial, Poisson & Exponential
- Inferential Statistics: Central Limit Theorem & Confidence Intervals
- Hypothesis Testing: t-tests, Chi-square Tests & ANOVA
- Correlation & Regression Analysis Fundamentals
- A/B Testing: Designing Experiments & Interpreting Results
- Project: A/B Testing Analysis for an E-commerce Website Conversion Rate
- SQL for Data Science: SELECT, JOINs, Window Functions & Subqueries
- Connecting to Databases: PostgreSQL, MySQL & SQLite with Python
- Web Scraping: Extracting Data with BeautifulSoup & Selenium
- Working with APIs: RESTful Services, JSON Parsing & Authentication
- Data Cleaning: Handling Null Values, Duplicates & Type Casting
- Data Validation: Schema Consistency & Data Integrity Checks
- Working with Large Files: Chunking, Dask & Memory Optimization
- Project: Building a Web Scraper to Collect & Clean Job Listings Data
- Univariate Analysis: Histograms, Box Plots & Density Plots
- Bivariate Analysis: Scatter Plots, Heatmaps & Pair Plots
- Interactive Visualizations with Plotly & Dash Dashboards
- Geospatial Visualization with Folium & Plotly Mapbox
- Feature Correlation Analysis: Pearson, Spearman & Kendall Coefficients
- Storytelling with Data: Designing Executive-ready Visual Reports
- Project: Interactive EDA Dashboard for COVID-19 Data using Plotly Dash
- House Price Prediction using Linear Regression
- Customer Churn Prediction using Logistic Regression
- Classification with Decision Trees & Random Forests
- Gradient Boosting: XGBoost & LightGBM for Tabular Data
- Support Vector Machines for Text Classification
- K-Nearest Neighbors (k-NN) for Recommendation Systems
- Model Evaluation: Confusion Matrix, ROC-AUC & Cross-validation
- Hyperparameter Tuning with Grid Search & Random Search
- Project: Predicting Loan Default Risk using Ensemble Methods
- K-Means Clustering for Customer Segmentation
- DBSCAN for Anomaly Detection in Transactions
- Hierarchical Clustering with Dendrograms
- Dimensionality Reduction: PCA & t-SNE for Data Exploration
- Time Series Analysis: Trend, Seasonality & ARIMA Forecasting
- Natural Language Processing: Tokenization & Sentiment Analysis
- Recommender Systems: Collaborative vs Content-based Filtering
- Project: Building a Movie Recommendation Engine with Collaborative Filtering
- Feature Creation: Polynomial Features & Interaction Terms
- Categorical Encoding: One-Hot, Label & Target Encoding
- Feature Scaling: Standardization vs Normalization
- Feature Selection: RFE, Boruta & Mutual Information
- Handling Imbalanced Data: SMOTE & Undersampling Techniques
- Cross-validation Strategies: K-Fold, Stratified & Time Series Split
- Automated ML: Introduction to Auto-sklearn & TPOT
- Project: End-to-End Feature Engineering Pipeline for Insurance Claim Prediction
- Model Deployment: Serving Predictions with Flask & FastAPI
- Building Interactive Dashboards with Streamlit
- Containerizing Data Science Apps with Docker
- Cloud Deployment: AWS SageMaker & Azure ML Studio
- Experiment Tracking & Model Versioning with MLflow
- Creating Automated Data Pipelines with Airflow
- Communicating Insights: Translating Metrics into Business Value
- Project: Deploying a Sales Forecasting Dashboard to the Cloud