In today’s data-driven world, the ability to derive meaningful insights from vast amounts of information is a skill in high demand. Data Science has emerged as a crucial discipline that combines statistical analysis, machine learning, and domain expertise to uncover valuable patterns and trends. Our comprehensive Data Science course is designed to equip individuals with the knowledge and skills needed to excel in this dynamic field.
Module 1 – Fundamentals of Programming
- Introduction to Anaconda and Jupyter Notebook
- Introduction to Git and Github
- Python Fundamentals
Module 2 – Fundamentals of Statistics
- Mean, Median and mode
- Standard Deviation, Average and Probability
- Permutation combination & Linear Algebra
Module 3 – Basic and Advanced Python
- Data Types, Strings, List
- Numpy Pandas, matplotlib, Seaborn
- Data visualization using above libraries
- Data Pre processing and Exploratory Data Analysis
Module 4 – Machine Learning
- Introduction to Machine Learning
- Regression and Classification Models
- Evaluation Metrics for Classification Models
- K-Nearest Neighbour Model
- Decision Tree model
- Random Forest Model
- Bagging and Boosting Techniques
- K-means and Hierarchical Clustering
- Naive Bayés Model
- Support Vector Machine
- Principal Component Analysis
Module 5 – Big Data Analytics and Visualization
- SQL Programming and MongoDB Basics
- Tableau Dashboard and Power BI
- Time Series Analysis and Forecasting
- Understanding Multivariate Time Series and their Structure.
Project Hands – On
- Project 1 → Data Science Project
- Project 2 → Machine Learning Project
- Project 3 → Deep Learning Project
- Project 4 → NLP Computer Vision Project