Designed for Working Professionals
Placement Assistance in MNC's
Vital Programming Tools & Languages Covered
Seamless Student Support
Career Mentorship by Industry Experts
Data-Driven Business Decisions
Covering both curriculum and advanced tools such as PySpark, SQL, GIT, Data Wrangling, and MLOps, these sessions will ensure that you have all the knowledge and skills you need to be successful.
Career Advancement
The program is curated with a team of Expert Data Scientists to guide your journey.
Live Virtual Session
Data science face-to-face training customized to live online classes aligned with your schedule.
Hands-on Experience
Take advantage of a data science course for beginners and Practice on Industry projects.
Placement Assistance
One of the best Data Science Courses with Placement consists of top hiring companies.
Learning Support
In our Online Data Science Bootcamp, Get all the learning materials & recording in one single place.
Real World Approach
We take an ideal approach toward real-world challenges and assist candidates with a mindset.
Chapter 1 - Python Basics
Chapter 2 - Python Data Structures
Chapter 3 - Python Programming Fundamentals
Chapter 4 - Working with Data in Python
Chapter 5 - Working with NumPy Arrays
Chapter 1 - Data Science Overview
Chapter 2 - Data Analytics Overview
Chapter 3 - Statistical Analysis and Business Applications
Chapter 4 - Python Environment Setup and Essentials
Chapter 5 - Mathematical Computing with Python (NumPy)
Chapter 6 - Scientific computing with Python (Scipy)
Chapter 7 - Data Manipulation with Pandas
Chapter 8 - Machine Learning with Scikit–Learn
Chapter 9 - Natural Language Processing with Scikit Learn
Chapter 10 - Data Visualization in Python using matplotlib
Chapter 11 - Web Scraping with BeautifulSoup
Chapter 12 - Python integration with Hadoop MapReduce and Spark
Chapter 1 - Introduction to Artificial Intelligence and Machine Learning
Chapter 2 - Data Wrangling and Manipulation
Chapter 3 - Supervised Learning
Chapter 4 - Feature Engineering
Chapter 5 - Supervised Learning-Classification
Chapter 6 - Unsupervised learning
Chapter 7 - Time Series Modelling
Chapter 8 - Ensemble Learning
Chapter 9 - Recommender Systems
Chapter 10 - Text Mining
Chapter 1 - Getting Started with Tableau
Chapter 2 - Core Tableau in Topics
Chapter 3 - Creating Charts in Tableau
Chapter 4 - Working with Metadata
Chapter 5 - Filters in Tableau
Chapter 6 - Applying Analytics to the worksheet
Chapter 7 - Dashboard in Tableau
Chapter 8 - Modifications to Data Connections
Chapter 9 - Introduction to Level of Details in Tableau (LODS)
Chapter 1 - Fundamental SQL Statements
Chapter 2 - Restore and Back-up
Chapter 3 - Selection Commands: Filtering
Chapter 4 - Selection Commands: Ordering
Chapter 5 - Alias
Chapter 6 - Aggregate Commands
Chapter 7 - Group By Commands
Chapter 8 - Conditional Statement
Chapter 9 - Joins
Chapter 10 - Subqueries
Chapter 11 - Views and Index
Chapter 12 - String Functions
Chapter 13 - Mathematical Functions
Chapter 14 - Date - Time Functions
Chapter 15 - Pattern (String) Matching
Chapter 16 - User Access Control Functions
Chapter 1 - Introduction to Business Analytics
Chapter 2 - Introduction to R Programming
Chapter 3 - Data Structures
Chapter 4 - Data Visualization
Chapter 5 - Statistics for Data Science I
Chapter 6 - Statistics for Data Science II
Chapter 7 - Regression Analysis
Chapter 8 - Classification
Chapter 9 - Clustering
Chapter 10 - Association