Data Science (Advanced)

About Course

This Advanced Data Science program is designed to equip learners with in-depth knowledge of data analysis, machine learning, and real-world data-driven decision making. The first month focuses on strong theoretical foundations and practical tools, while the second month focuses on industry-level projects guided by experienced mentors to provide real-world exposure.

What Will You Learn?

✅ Advanced Data Analysis Techniques
✅ Python for Data Science & Automation
✅ Machine Learning Algorithms
✅ Data Visualization & Storytelling
✅ Deep Learning Fundamentals
✅ Industry Tools: Python, Pandas, NumPy, Scikit-Learn, TensorFlow, Power BI
✅ Hands-on Industry Projects

Material Includes

📚 LMS Access for Self-paced Learning
📜 2 Industry-recognized Certificates
📂 Real-world Data Science Projects
📝 Weekly Assignments & Case Studies

Course Curriculum

Month 1: Theory & Concepts (Monday – Friday Classes)

Module 1: Introduction to Advanced Data Science

Module 2: Python for Data Science

Module 3: Exploratory Data Analysis (EDA)

Module 4: Machine Learning Fundamentals

Module 5: Advanced Machine Learning

Module 6: Deep Learning & Model Deployment

Month 2: Industry-Level Projects

Project 1: Predictive Analytics for Business

Project 2: Customer Segmentation using Machine Learning

Your Instructors

mojeshbondu1@gmail.com

0 Rating 29 Courses 0 Students

This course includes:

Hello There!

Information

Follow Us

Data Science (Advanced)

mojeshbondu1@gmail.com

March 4, 2026

About Course

What Will You Learn?

Material Includes

Course Curriculum

Month 1: Theory & Concepts (Monday – Friday Classes)

Module 1: Introduction to Advanced Data Science

Understanding the Data Science Lifecycle

Role of Data Scientists in Industry

Applications of Data Science in Business

AICTE & NSDC Guidelines in Data Science

Module 2: Python for Data Science

Python Programming for Data Analysis

Libraries: NumPy, Pandas, Matplotlib

Data Cleaning & Preprocessing

Working with Large Datasets

Module 3: Exploratory Data Analysis (EDA)

Understanding Data Patterns

Statistical Analysis & Probability

Data Visualization Techniques

Tools: Matplotlib, Seaborn, Power BI

Module 4: Machine Learning Fundamentals

Supervised vs Unsupervised Learning

Regression & Classification Algorithms

Decision Trees & Random Forest

Model Evaluation Techniques

Module 5: Advanced Machine Learning

Support Vector Machines (SVM)

Clustering Algorithms (K-Means, DBSCAN)

Feature Engineering Techniques

Hyperparameter Tuning

Module 6: Deep Learning & Model Deployment

Introduction to Neural Networks

Deep Learning with TensorFlow/Keras

Model Optimization Techniques

Deploying ML Models in Real Applications

Month 2: Industry-Level Projects

Project 1: Predictive Analytics for Business

Data collection & preprocessing

Building predictive models

Performance evaluation

Business insights & reporting

Project 2: Customer Segmentation using Machine Learning

Data clustering and segmentation

Implementing ML algorithms

Data visualization dashboards

Real-world case study presentation

Your Instructors

mojeshbondu1@gmail.com

This course includes:

Menu

Quick Links

Visit our office HQ

2nd branch