Data Science Master Class

Training and Certification

The Data Science masterclass is a comprehensive program designed to equip participants with the theoretical knowledge and practical skills required to excel in data science. This course covers essential concepts such as data analysis, machine learning, data visualization, and real-world applications across multiple domains, making it ideal for aspiring data scientists and professionals aiming to upskill.

Course Objectives

Understand the fundamentals of data science and its practical applications.
Master the tools and techniques for data collection, preprocessing, analysis, and visualization.
Build predictive models using machine learning algorithms.
Develop hands-on expertise with Python, R, SQL, and data visualization platforms.
Apply data science solutions to solve real-world problems across various industries.

Pre-requisites

Basic knowledge of programming and mathematics.
Familiarity with statistics is recommended but not mandatory.

Learning Mode

Physical Class
Live Virtual Class

Certification Exam

Participants will be guided to prepare for industry-recognized certifications such as:

- PCEP-30-02: PCEP™ – Certified Entry-Level Python Programmer
- DP-100: Microsoft Certified: Azure Data Scientist Associate

Data Science Course Module

Introduction to Data Science

- Overview of Data Science and its Applications
- Data Science Workflow
- Career Paths in Data Science
- Tools for Data Science

Data Science Foundations

- Descriptive and Inferential Statistics
- Probability Distributions
- Linear Algebra and Calculus
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)

Programming for Data Science

- Python and R Basics
- Essential Libraries (NumPy, Pandas, ggplot2)
- SQL for Data Manipulation

Version Control - Git

- Git Basics and Commands
- Collaboration on GitHub
- Best Practices for Version Control

Data Visualization and Storytelling

- Visualization Principles
- Python Libraries (Matplotlib, Seaborn, Plotly)
- Dashboards with Tableau and Power BI

Machine Learning Fundamentals

- Supervised and Unsupervised Learning
- Regression, Classification, and Clustering Models
- Model Evaluation Metrics

Advanced Machine Learning

- Deep Learning with Neural Networks
- Natural Language Processing (NLP)
- Reinforcement Learning
- Advanced Topics: GANs and Ensemble Methods

Domain Applications and Case Studies

Applications in Healthcare, Finance, Retail, Marketing, Sports, and more.

Capstone Project

- Real-World Problem Identification
- Data Analysis, Model Building, and Evaluation
- Final Report and Presentation