Data Science

Join our Beginner’s Data Science Course and gain essential skills in data analysis. Start your journey towards a rewarding career in data science today!

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Course Registration

45

Classes

3 Times

Weeks

All

Skill Level

English

Language

Overview

In today’s data-driven world, the ability to extract meaningful insights from vast datasets is a skill in high demand. Our Data Science Essentials course is designed to equip you with the fundamental knowledge and practical skills needed to embark on a rewarding journey in the field of data science. Upon successful completion of the course and the capstone project, you will receive a Data Science Essentials Certification, demonstrating your proficiency in data science.

 

 

Join us on this exciting journey into the world of data science and gain the skills needed to unlock the power of data for insightful decision-making and innovation. Enroll today and start your path to becoming a proficient data scientist!

Completing this course will help you:

Who is the course for?

Data Science Essentials course is designed to cater to a wide range of individuals. Whether you are an aspiring data scientist looking to build a career in this thriving field, an analyst seeking to enhance your data analysis skills, a business professional aiming to make more data-informed decisions, or an IT professional considering a transition into data science roles, this course is tailored to meet your needs.

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Learning Path

  • Basic of Python Programming
  • Advanced Data Structures (List, Tuple, Dictionary, Set)
  • Functions and Recursion
  • Advanced Packages:  Pandas, Numpy, Matplotlib, Seaborn
  • Univariate Analysis
  • Bivariate Analysis
  • Multivariate Analysis
  • Project
  • Descriptive statistics
  • Probability and Probability Distribution Functions
  • Hypothesis testing
  • Project
  • Linear Regression and Its Type
  • Logistics Regression
  • Naïve Baye Model
  • K- Nearest Neighbour 
  • Support Vector Machine
  • Decision Tree
  • Ensemble Techniques (Bagging and Boosting)
  • Project-1
  • Project -2
  • Super vised vs Unsupervised Learning
  • Clustering (K-means and Hierarchical Clustering)
  • Principle Component Analysis
  • Association Rule Mining
  • Project

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