Data Science Training in Bangalore: Complete Course with 30 Modules

Introduction to Data Science

• What is Data Science?
• Data Science vs Data Analytics vs Machine Learning vs AI
• Data Science lifecycle
• Types of data — structured, semi-structured and unstructured
• Business problems and data-driven decision making
• Data Science roles and career opportunities
• Real-world applications of Data Science

Python Programming for Data Science

• Python installation and environment setup
• Variables, data types and operators
• Strings and string operations
• Lists, tuples, sets and dictionaries
• Conditional statements
• Loops and iterations
• Functions and arguments
• Lambda functions
• List/dictionary comprehensions
• Exception handling
• File handling
• Modules and packages
• Object-oriented programming fundamentals
• Regular expressions
• Working with JSON and CSV files
• Virtual environments
• Jupyter Notebook and Google Colab

Python Libraries for Data Science

• NumPy fundamentals
• NumPy arrays and dimensions
• Array indexing and slicing
• Broadcasting
• Mathematical and statistical operations
• Pandas Series and DataFrames
• DataFrame indexing and filtering
• Sorting and grouping
• Merging, joining and concatenation
• Handling missing values
• Duplicate-data handling
• Data type conversion
• Apply, map and lambda operations
• Reading CSV, Excel, JSON and database data

Data Cleaning and Preprocessing

• Understanding raw datasets
• Data quality assessment
• Missing-value treatment
• Duplicate detection
• Outlier detection and treatment
• Inconsistent data handling
• Data transformation
• Encoding categorical variables
• Feature scaling
• Normalization and standardization
• Feature selection
• Data leakage
• Train/test data preparation
• Building reusable preprocessing pipelines

Statistics for Data Science

• Descriptive statistics — mean, median and mode
• Range, variance and standard deviation
• Percentiles and quartiles
• Probability fundamentals
• Conditional probability
• Bayes theorem
• Probability distributions — normal, binomial, Poisson
• Sampling techniques
• Central Limit Theorem
• Confidence intervals
• Hypothesis testing
• Null and alternative hypotheses
• p-value
• Type I and Type II errors
• t-test, chi-square test, ANOVA
• Correlation and covariance

SQL for Data Science

• Database fundamentals
• Relational databases
• Tables, rows and columns
• Primary and foreign keys
• SELECT statements
• WHERE, ORDER BY and GROUP BY
• Aggregate functions
• HAVING clause
• CASE statements
• String and date functions
• Joins — inner, left, right and full
• Subqueries
• Common Table Expressions
• Views
• Window functions
• Ranking functions and partitioning
• Advanced SQL queries
• Data extraction for analytics
• SQL interview exercises

Excel for Data Science and Analytics

• Excel fundamentals
• Advanced formulas — IF, SUMIF, COUNTIF and lookup functions
• XLOOKUP/VLOOKUP
• Conditional formatting
• Data validation
• Pivot tables
• Pivot charts
• Data cleaning
• Advanced filtering
• Charts and dashboards
• Power Query fundamentals
• Business reporting

Exploratory Data Analysis

• Understanding datasets
• Univariate, bivariate and multivariate analysis
• Distribution analysis
• Correlation analysis
• Outlier analysis
• Pattern identification
• Feature relationships
• Statistical summaries
• Business-question-driven analysis
• EDA using Pandas
• EDA using Matplotlib
• EDA using Seaborn
• Building an end-to-end EDA project

Data Visualization

• Principles of effective visualization
• Matplotlib
• Seaborn
• Line charts, bar charts and histograms
• Box plots, scatter plots and heatmaps
• Pair plots and distribution plots
• Interactive visualization fundamentals
• Choosing the correct visualization
• Data storytelling
• Executive dashboards

Machine Learning Fundamentals

• What is Machine Learning?
• Supervised, unsupervised and semi-supervised learning
• Reinforcement learning overview
• Machine Learning workflow
• Features and target variables
• Training, testing and validation datasets
• Bias and variance
• Underfitting and overfitting
• Model selection
• Cross-validation
• Model evaluation

Regression Algorithms

• Linear regression
• Multiple linear regression
• Polynomial regression
• Ridge regression
• Lasso regression
• Elastic Net
• Regression assumptions
• Residual analysis
• MAE, MSE, RMSE
• R-squared and adjusted R-squared
• Regression project

Classification Algorithms

• Logistic regression
• K-Nearest Neighbors
• Naive Bayes
• Decision trees
• Random Forest
• Support Vector Machines
• Gradient Boosting
• XGBoost fundamentals
• Accuracy, precision, recall and F1-score
• ROC-AUC
• Confusion matrix
• Classification project

Unsupervised Learning

• Clustering fundamentals
• K-Means clustering
• Hierarchical clustering
• DBSCAN
• Cluster evaluation — elbow method and silhouette score
• Customer segmentation
• Market segmentation
• Anomaly detection
• Clustering project

Feature Engineering

• Feature creation
• Feature transformation
• Feature scaling
• Encoding techniques
• Binning
• Log transformation
• Date/time features
• Interaction features
• Feature selection — correlation-based, recursive elimination, model-based

Advanced Machine Learning

• Ensemble learning — bagging and boosting
• Random Forest
• Gradient Boosting
• XGBoost
• LightGBM overview
• Hyperparameter tuning — Grid Search and Random Search
• Cross-validation
• Handling imbalanced datasets — SMOTE
• Model pipelines
• Explainable AI fundamentals
• SHAP fundamentals

Time Series Analysis

• Time-series fundamentals
• Components of time series
• Trend and seasonality
• Stationarity
• Time-series decomposition
• Moving averages
• Exponential smoothing
• Autocorrelation
• Forecasting fundamentals
• ARIMA overview
• SARIMA overview
• Time-series machine learning
• Sales forecasting project

Deep Learning

• Introduction to Deep Learning
• Neural networks
• Neurons and activation functions
• Forward propagation
• Backpropagation
• Loss functions
• Optimizers and gradient descent
• Learning rate, epochs and batch size
• TensorFlow fundamentals
• Keras fundamentals
• Building neural networks
• Model training and evaluation

Computer Vision Fundamentals

• Image data fundamentals
• Image preprocessing
• CNN architecture
• Convolution and pooling
• Filters and feature maps
• Image classification
• Transfer learning
• Object detection overview
• Computer vision project

Natural Language Processing

• Introduction to NLP
• Text preprocessing
• Tokenization
• Stop-word removal
• Stemming and lemmatization
• Bag of Words
• TF-IDF
• N-grams
• Text classification
• Sentiment analysis
• Named Entity Recognition overview
• Word embeddings
• NLP project

Generative AI for Data Scientists

• Introduction to Generative AI
• LLM fundamentals
• Generative AI vs traditional ML
• Prompt engineering
• Effective prompts for data analysis
• LLM APIs
• AI-assisted Python programming
• AI-assisted SQL analysis
• Text generation and summarization
• Data interpretation
• Embeddings
• Vector databases overview
• Retrieval-Augmented Generation fundamentals
• RAG architecture
• LLM-based analytics applications

Power BI for Data Science & Analytics

• Power BI fundamentals
• Connecting data sources
• Data transformation and Power Query
• Data modeling and relationships
• DAX fundamentals
• Calculated columns and measures
• KPIs
• Charts and visualizations
• Interactive dashboards
• Filters and slicers
• Drill-down and drill-through
• Dashboard publishing
• Business intelligence project

Tableau Fundamentals

• Tableau interface
• Connecting datasets
• Data preparation
• Dimensions and measures
• Calculated fields
• Charts
• Filters and parameters
• Dashboards
• Interactive reports
• Data storytelling
• Tableau project

Git and GitHub

• Git fundamentals
• Repository creation
• Clone, add, commit and push
• Branches
• Merge and pull requests
• GitHub repositories
• README creation
• Managing Data Science projects
• Building a professional GitHub portfolio

APIs and Data Collection

• What are APIs?
• REST API fundamentals
• HTTP methods
• JSON data
• API authentication
• Consuming APIs using Python
• Extracting data from APIs
• Web scraping fundamentals
• Data collection best practices
• Building an automated data collection project

Cloud and Deployment Fundamentals

• Cloud computing overview
• AWS/Azure/GCP fundamentals
• Cloud storage
• Cloud databases overview
• Deploying ML models
• Flask/FastAPI fundamentals
• Streamlit applications
• Creating prediction APIs
• Docker fundamentals
• Containerizing ML applications
• Basic CI/CD concepts

MLOps Fundamentals

• Introduction to MLOps
• ML lifecycle
• Experiment tracking
• Model versioning
• Data versioning
• Model deployment
• Model monitoring
• Model retraining
• ML pipelines
• Production ML concepts

Real-World Data Science Projects

• House Price Prediction
• Customer Churn Prediction
• E-Commerce Customer Segmentation
• Sales Forecasting
• Credit Risk Prediction
• Loan Approval Prediction
• Fraud Detection
• Employee Attrition Prediction
• Customer Lifetime Value Analysis
• Marketing Campaign Analysis
• Retail Demand Forecasting
• Healthcare Data Analysis
• Sentiment Analysis
• Recommendation System
• Predictive Maintenance
• Stock/Financial Data Analysis
• Business Intelligence Dashboard
• End-to-End Machine Learning Deployment Project

Capstone Project

• Business problem identification
• Dataset collection
• Data cleaning
• Exploratory Data Analysis
• Statistical analysis
• Feature engineering
• Machine Learning model development
• Model comparison and hyperparameter tuning
• Model evaluation
• Dashboard creation
• Model deployment
• GitHub documentation
• Final project presentation

Interview Preparation

• Data Science interview questions
• Python interview questions
• SQL interview questions
• Statistics interview questions
• Machine Learning interview questions
• Deep Learning questions
• NLP questions
• Power BI questions
• Scenario-based questions
• Case-study interviews
• Coding exercises
• SQL exercises
• Machine Learning problem-solving
• Mock interviews
• HR interview preparation

Career & Portfolio Development

• Data Scientist role
• Data Analyst role
• Machine Learning Engineer role
• Business Analyst role
• AI/ML Engineer role
• Building a Data Science resume
• LinkedIn profile optimization
• GitHub portfolio development
• Project documentation
• Interview portfolio
• Job search strategy
• Technical assessment preparation
• Industry-readiness guidance

House Price Prediction

Build a regression model to predict house prices based on features like location, size and amenities using scikit-learn.

Customer Churn Prediction

Develop a classification model to identify customers likely to churn, enabling proactive retention strategies.

E-Commerce Customer Segmentation

Use K-Means clustering to segment e-commerce customers by purchasing behaviour for targeted marketing.

Sales Forecasting

Apply time-series analysis with ARIMA to forecast product sales and optimise inventory planning.

Credit Risk Prediction

Build a classification model to assess credit risk and predict loan default probability for financial applications.

Fraud Detection

Develop a machine learning pipeline to detect fraudulent transactions in real-time using imbalanced data techniques.

Sentiment Analysis

Use NLP techniques to classify customer review sentiment and visualise sentiment trends over time.

Business Intelligence Dashboard

Create an interactive Power BI dashboard connecting to a real dataset with KPIs, filters and drill-through reports.