Introduction to Data Science using Python (Module 13)

Introduction to Data Science using Python (Module 1/3), available at Free, has an average rating of 4.25, with 12 lectures, based on 7208 reviews, and has 160451 subscribers.

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You will learn about Understand the basics of Data Science and Analytics Understand how to use Python and Scikit learn Get a good understanding of all buzz words like Data Science, Machine learning, Data Scientist etc. This course is ideal for individuals who are Anyone who wants to learn about Data Science from absolute scratch. or Anyone who wants to switch or make a career in Data Science and Analytics or Anyone who is curious to know what is Data Science and what does a Data Scientist do in his/her day job. It is particularly useful for Anyone who wants to learn about Data Science from absolute scratch. or Anyone who wants to switch or make a career in Data Science and Analytics or Anyone who is curious to know what is Data Science and what does a Data Scientist do in his/her day job.

Enroll now: Introduction to Data Science using Python (Module 1/3)

Summary

Title: Introduction to Data Science using Python (Module 1/3)

Price: Free

Average Rating: 4.25

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Understand the basics of Data Science and Analytics

Understand how to use Python and Scikit learn

Get a good understanding of all buzz words like Data Science, Machine learning, Data Scientist etc.

Who Should Attend

Anyone who wants to learn about Data Science from absolute scratch.

Anyone who wants to switch or make a career in Data Science and Analytics

Anyone who is curious to know what is Data Science and what does a Data Scientist do in his/her day job.

Target Audiences

Anyone who wants to learn about Data Science from absolute scratch.

Anyone who wants to switch or make a career in Data Science and Analytics

Anyone who is curious to know what is Data Science and what does a Data Scientist do in his/her day job.

Are you completely new to Data science?

Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don’t know what this is?

Do you want to start or switch career to Data Science and analytics?

If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover in-depth algorithms. I?have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python.?I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.

This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don’t go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.

The other modules will cover more complex concepts.?

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Lecture 2: What is Data Science and Machine Learning?

Chapter 2: Getting setup with Tools

Lecture 1: Getting Started with Python and Scikit-Learn

Lecture 2: Types of Machine learning algorithms

Lecture 3: Playing Around with Anaconda and Jupyter

Lecture 4: Playing with some Python Code

Chapter 3: Getting Deeper into Machine learning frameworks and algorithms

Lecture 1: Fitting a Machine Learning Model (KNN Algorithm) – Part 1

Lecture 2: Fitting a Machine Learning Model (KNN Algorithm) Part 2

Lecture 3: Fitting a Machine Learning Model (Logistic Regression Algorithm)

Lecture 4: Validation using Model Selection (Train and Test)

Lecture 5: Finalizing Your optimum algorithm (K-Fold Cross Validation)

Lecture 6: Data Science – Whats Next?

Instructors

Rakesh Gopalakrishnan
Over 600,000 Students

Rating Distribution

1 stars: 89 votes

2 stars: 182 votes

3 stars: 1110 votes

4 stars: 2955 votes

5 stars: 2872 votes

Frequently Asked Questions

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