Data Science, AI & Predictive Analytics

Course Overview

Transform raw datasets into actionable predictive intelligence. This course equips analytical professionals and data enthusiasts with practical skills in Python data manipulation, statistical hypothesis testing, core & advanced Machine Learning algorithms, Time Series forecasting, and interactive dashboard deployment using Streamlit.

Who Is This For?

Data analysts, BI professionals, business managers, software engineers, and analytical enthusiasts aiming for AI & ML mastery.

Core Objective

Architect end-to-end data pipelines, train high-accuracy predictive ML models, and deploy live data apps with Streamlit.

What You Will Learn

Data Wrangling & Manipulation

Master Pandas and NumPy to clean messy datasets, handle missing values, transform types, and compute aggregations.

Statistical Analysis & Storytelling

Conduct hypothesis testing, understand statistical distributions, correlation matrices, and create Seaborn visual stories.

Machine Learning Algorithms

Implement Linear/Logistic Regression, Decision Trees, Random Forests, SVM, KNN, K-Means clustering, and PCA.

Time Series Forecasting & NLP

Build ARIMA time-series models for trend prediction and parse customer sentiment using Natural Language Processing.

Interactive Web App Deployment

Deploy predictive models as live web applications using Streamlit and containerized microservices endpoints.

Tools & Technologies Mastered

Python 3.x Pandas & NumPy Scikit-Learn Seaborn & Matplotlib Streamlit Apps SQL Analytics

Detailed Curriculum Breakdown

Week 1-2 8 Hours

Data Foundations (Pandas & NumPy)

Jupyter notebook workflows, Series and DataFrames, indexing, filtering, group-by aggregations, merge/join datasets, and handling missing data.

Week 3-4 8 Hours

Stats & Visualization Storytelling

Descriptive statistics, probability distributions, p-values, hypothesis testing (t-tests, chi-square), correlation heatmaps, and Seaborn plots.

Week 5-6 8 Hours

Core Machine Learning Models

Supervised learning workflows, Linear & Logistic Regression, model evaluation metrics (RMSE, Precision, Recall, F1-Score), Decision Trees, and Random Forests.

Week 7-8 8 Hours

Advanced ML & Unsupervised Learning

Support Vector Machines (SVM), K-Nearest Neighbors (KNN), K-Means Clustering, Principal Component Analysis (PCA), and Hyperparameter Tuning via GridSearchCV.

Week 9-10 8 Hours

Time Series Forecasting & NLP

Stationarity testing, ARIMA and SARIMA forecasting models, text tokenization, TF-IDF vectorization, and sentiment analysis models.

Week 11-12 8 Hours

Streamlit App Deployment & Capstone

Building interactive web UIs with Streamlit, wrapping ML models into API endpoints, cloud hosting, and presenting final capstone dashboards.

Real-World Capstone Projects

Project 1

Real Estate Market Forecaster

Develop a machine learning regression model predicting property prices based on geospatial features and economic indicators.

Project 2

Financial Fraud Detection System

Train a high-precision classification model detecting anomalous credit card transactions on live streaming datasets.

Frequently Asked Questions

Do I need an advanced math background?

Basic high-school math is sufficient. We teach all necessary statistical concepts from scratch in a practical, hands-on manner.

What tools will I use during the course?

You will work with Python, Jupyter Notebooks, Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn, SQL, and Streamlit.

Are real datasets included?

Yes! You will work with real financial, real estate, e-commerce, and healthcare datasets throughout the program.

Founding Batch Live Classes

Total Course Fee

₹12,999.00

Includes 50% OFF Promo Code Discount

12 Weeks (48 Hours) Live Interactive Batch

1-on-1 Mentorship & Code Reviews

Live Real-World Capstone Projects

Verified Government Reg. Certificate

Paid Internship & Job Assistance

100% Secure Checkout via Razorpay. Instantly unlock student portal dashboard.

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