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Development Data Science

Data Science Approach from Scratch: An Easy Explanation

Understand the Complete Data Science use through a Real Life problem - The Easiest Way to Understand Data Science
Rating: 4.1 out of 54.1 (46 ratings)
4,643 students
Created by Nizamuddin Siddiqui
Last updated 11/2019
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Data Science explanation in layman language and it's need
  • 8 Mistakes made by people who want to start their career in Data Science
  • 3 Myths about Data Science
  • Data Types and Variables
  • Definition of Data Cleaning and the errors that results in the need of Data Cleaning
  • Feature Engineering
  • Data Thinking Development
  • Use of many Algorithms
  • Example to select the best model in case of a classification problem
  • Types of Learning Methods

Course content

4 sections • 15 lectures • 57m total length

  • Preview02:30
  • Data Understanding Test
    10 questions
  • Preview04:47
  • Need of Data Science
    02:01
  • Preview08:20
  • Preview02:46

  • Data Types and Variables
    04:11
  • Descriptive Analysis
    01:37
  • Data Cleaning
    01:32
  • Feature Engineering
    01:44
  • Data Thinking Development
    02:37

  • Problem Definition
    04:42
  • Algorithms
    13:38
  • Prediction
    02:36
  • Learning Methods
    04:33

  • BONUS LECTURE
    00:13

Requirements

  • You are a beginner in Data Science
  • You are thinking about moving into Data Science
  • You want to know "What Data Science is all about?"
  • You are a business person and want to understand the use of Data Science

Description

Welcome to the ultimate course on Data Science Approach from Scratch!!!

This course is your Best Resource for learning the use of Data Science.

We will be understanding the use of:

1. Data Science from a layman's perspective

2. Regression Algorithms

3. Classification Algorithms

4. Clustering Algorithms

5. Boosting Algorithms

6. Dimensionality Reduction Algorithms

Specifically, we will cover:

  • Linear Regression

  • Stepwise Regression

  • Polynomial Regression

  • Logistic Regression

  • Naive Bayes

  • Decision Trees

  • Random Forest

  • Support Vector Machine

  • Gradient Boosting

  • Principal Component Analysis and Linear Discriminant Analysis

  • K-means Clustering and Hierarchical Clustering

  • and much more!

Feel free to message me on Udemy if you have any questions about the course!

Thanks for checking out the course page, and I hope to see you inside the course!

Nizamuddin

Course Instructor

Who this course is for:

  • Aspiring Data Scientists
  • Data Science Enthusiasts
  • Students and professionals curious about Data Science

Instructor

Nizamuddin Siddiqui
Edupreneur | Data Science Author | Career Mentor
Nizamuddin Siddiqui
  • 4.0 Instructor Rating
  • 467 Reviews
  • 37,475 Students
  • 5 Courses

Nizamuddin Siddiqui is a Data Science Author and Career Mentor. He is helping students to understand Data Science concepts for more than three years, solved thousands of problems for students, and has many overseas clients from the education industry. Most of his work is into academics especially offering help to Statistics students in the USA, UK, Bangladesh through different clients.


He is also an Edupreneur who likes to provide applicable knowledge to people who are keen to know things.


In terms of education, he holds a post-graduate degree in Statistics and certified in R programming and MY SQL.

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