What is Data Science?

Data Science is a collection of processes, algorithms and tools to extract knowledge and insights from various sources of structured and unstructured data. Data Science is widely used in organizations to gain understanding related to markets, competitors and products to lift business performance.


In the recent past, there has been an explosion in data. It is estimated that currently over 2.5 quintillion bytes of data is being generated every day. With the advent of big data technologies like Hadoop and Spark, the storage and processing of large volumes and variety of data is much easier now. Such huge volume of data contains infinite knowledge and wisdom, if properly mined. Over the past few years, the demand for Data Scientists, who are adept at using statistical techniques for data mining, has skyrocketed across all domains. This course will help you understand the fundamentals of Data Science and help you transform your career into a successful Data Scientist.

Course Structure:

  • The course is divided into 5 modules with 50% theory and 50% hands on in the form of assignments and mini projects
  • Students will be given a real time Data Science project to work on as a part of the course

After the course, you will:

  • Be able to understand the elements of Data Science and how it is being used currently in the industry to lift business performance
  • Be proficient in Statistical Analysis and Data Mining
  • Be proficient in Machine Learning algorithms
  • Be able to handle a Data Science project end to end
  • Understand what the industry currently requires from a Data Scientist and be ready to switch your career as a Data Scientist

Course overview:

  • Module 1 – Data Science Toolkit
  • Module 2 – Statistics and Exploratory Data Analysis
  • Module 3 – Machine Learning
  • Module 4 – Advanced Machine Learning
  • Module 5 – Hands on Project

Module 1 – Data Science Toolkit

  • Introduction to Data Science
  • Introduction to Python
  • Data Visualization

Module 2 – Statistics and Exploratory Data Analysis

  • Introduction to Statistics
  • Hypothesis Testing
  • Inferential Statistics
  • Exploratory Data Analysis

Module 3 – Machine Learning

  • Unsupervised Learning
    • Clustering
    • Principal Component Analysis
  • Supervised Learning
    • Regression

Module 4 – Advanced Machine Learning

  • Support Vector Machine
  • KNN modelling
  • Naïve Bayes
  • Decision Tree
  • Model selection and Ensemble modelling

Module 5 – Hands on Project

  • A real-life Data Science project

Who can take this course?

  • Professionals who are interested to learn about Data Science techniques
  • Professionals who want to switch their careers into Data Science
  • Professionals who want to complete Data Science certifications
  • Data Science enthusiasts who want an in-depth knowledge in the subject
  • Data Analyst
  • Data Scientist
  • Business Intelligence Developer
  • Data architect
  • Data Engineer



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