Python Data Science

Fee :  ₹25,000 + GST  ₹4500, Duration : 5 months,  Contact : patkar@rajeshpatkar.com

1. Overview of Data Science

   1. Importance and Applications

   2. Data Science vs. Data Analytics vs. Machine Learning

2. Data Science Lifecycle

   1. Data Collection, Cleaning, Exploration, Modeling, Deployment, and Maintenance

3. Tools and Environment Setup

   1. Python Ecosystem for Data Science

   2. Introduction to VSCode

   3. Introduction to Jupyter Notebooks

   4. Version Control with Git and GitHub

1. NumPy Fundamentals

   1. Arrays and Array Operations

   2. Mathematical Functions and Vectorization

   3. Indexing, Slicing, and Reshaping Arrays

2. Advanced NumPy Techniques

   1. Broadcasting and Vectorization

   2. Performance Optimization

3. pandas for Data Manipulation

   1. Series and DataFrames

   2. Importing and Exporting Data (CSV, Excel, JSON, SQL)

4. Data Cleaning and Preparation

   1. Handling Missing Data

   2. Dropping Duplicates

   3. Filtering and Sorting

1. Descriptive Statistics

   1. Measures of Central Tendency and Dispersion

   2. Probability Distributions

2. Data Visualization with matplotlib

   1. Basic Plotting: Line, Scatter, Bar, Histogram

   2. Customizing Plots: Colors, Labels, Legends, Titles

3. Advanced Visualization with seaborn

   1. Statistical Plots: Boxplots, Violin Plots, Pair Plots

   2. Heatmaps and Correlation Matrices

4. Interactive Visualizations

   1. Introduction to Plotly and Bokeh

   2. Creating Interactive Dashboards

1. Probability Theory

   1. Basic Probability Concepts

   2. Conditional Probability and Bayes’ Theorem

2. Inferential Statistics

   1. Sampling Methods

   2. Confidence Intervals and Margin of Error

3. Hypothesis Testing

   1. Null and Alternative Hypotheses

   2. t-Tests, Chi-Square Tests, p-Values

4. Regression Analysis

   1. Simple and Multiple Linear Regression

   2. Logistic Regression for Classification

1. Machine Learning Fundamentals

   1. Supervised vs. Unsupervised Learning

   2. Overview of scikit-learn

2. Supervised Learning - Regression

   1. Linear Regression Concepts and Implementation

   2. Evaluating Regression Models: MSE, RMSE, R²

3. Supervised Learning - Classification

   1. Logistic Regression: Binary Classification

   2. Performance Metrics: Accuracy, Precision, Recall, F1-Score

1. Decision Trees and Ensemble Methods

   1. Decision Trees: Structure, Splitting Criteria, Pruning

   2. Random Forests: Ensemble Learning, Feature Importance

2. Support Vector Machines (SVM)

   1. Margin Maximization

   2. Kernel Functions: Linear, Polynomial, RBF

   3. SVM Implementation for Classification

3. Unsupervised Learning - Clustering

   1. k-Means Clustering: Algorithm Steps, Applications

   2. Hierarchical Clustering: Agglomerative and Divisive Methods

4. Dimensionality Reduction Techniques

   1. Principal Component Analysis (PCA)

   2. t-Distributed Stochastic Neighbor Embedding (t-SNE)

1. Cross-Validation Techniques

   1. k-Fold Cross-Validation

   2. Stratified Sampling

2. Hyperparameter Tuning

   1. Grid Search and Randomized Search

   2. Using scikit-learn’s GridSearchCV and RandomizedSearchCV

3. Handling Imbalanced Data

   1. Challenges with Imbalanced Datasets

   2. Techniques: Resampling, SMOTE, Adjusting Class Weights

4. Model Deployment and Monitoring

   1. Saving and Loading Models with joblib and pickle

   2. Introduction to ML Model Monitoring and Maintenance

1. Introduction to Data Engineering

   1. Role of Data Engineering in Data Science

   2. Overview of Data Pipelines and ETL Processes

2. Data Pipeline Tools

   1. Introduction to Apache Airflow

   2. Designing and Orchestrating Workflows

3. Big Data Technologies

   1. Introduction to Apache Hadoop and HDFS

   2. Basics of Apache Spark for Large-Scale Data Processing

4. Cloud Data Engineering

   1. Overview of Cloud Platforms (AWS, GCP, Azure)

   2. Introduction to AWS S3, Redshift, and Glue

5. NoSQL Databases

   1. Introduction to MongoDB and Cassandra

   2. Use-Cases for NoSQL vs. SQL

6. Data Warehousing Concepts

   1. Designing Data Warehouses

   2. Star and Snowflake Schemas

1. Introduction to Relational Databases and SQL

   1. Understanding Databases and SQL Syntax

   2. Data Definition and Manipulation (CREATE, SELECT, INSERT, UPDATE, DELETE)

2. Advanced SQL Concepts

   1. Joins, Subqueries, and Set Operations

   2. Aggregate Functions and Grouping

3. Interacting with Databases in Python

   1. Using SQLite and PostgreSQL with Python

   2. Database Connections and Transactions

4. Introduction to ORMs

   1. Using SQLAlchemy for Database Operations

   2. Mapping Classes to Database Tables

1. Introduction to Web Development and Flask

   1. Understanding HTTP and Client-Server Architecture

   2. Setting Up a Flask Application

2. Routing and View Functions

   1. Defining Routes and Handling Requests

   2. Templates and Rendering HTML Pages

3. Forms and User Input

   1. Handling Form Submissions

   2. Validating User Input

4. Building RESTful APIs

   1. Creating APIs with Flask

   2. Consuming and Serving JSON Data

Exam System