HANDS-ON Introduction to Python for Data Science
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4 students enrolled

HANDS-ON Introduction to Python for Data Science

Jupyter Notebook + Python Data Science Libraries + Practical Examples
0.0 (0 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4 students enrolled
Last updated 4/2020
English
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Current price: $11.99 Original price: $19.99 Discount: 40% off
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This course includes
  • 2 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • python
  • data science
  • linear regression
  • logistic regression
  • jupyter notebook
  • pandas
  • statsmodels
  • sklearn
  • data analysis
  • data preparation and cleaning
Requirements
  • Basic Computer Science Understanding
Description

This course is formed in such a way that you will be writing code from the beginning to the end!

Why did I choose this format:

  1. You will be more engaged during the course

  2. You will understand concepts better when you try them out on your own

  3. You will get more confidence knowing you are capable of writing your own programs

  4. You will get more satisfaction from successfully finishing tasks.

Who this course is for:
  • Beginner Python students interested in Data Science
  • Students interested in Data Science
Course content
Expand all 17 lectures 01:58:48
+ Python Fundamentals
5 lectures 31:22
Jupyter Notebook
06:38
Python Syntax
02:14
Variables in Python
06:41
Pandas
14:10
+ Data Science
11 lectures 01:17:38
What is Linear Regression?
04:50
Checking Linear Regression Assumptions
05:19
Simple Linear Regression Model using StatsModels
13:22
Multiple Linear Regression Model using StatsModels
10:27
Simple Linear Regression Model using Sklearn
06:32
Overfitting and Underfitting
05:25
Splitting data into Testing and Training
05:58
Cleaning and Preparing the Data
08:39
Dealing with Categorical Variables
04:36
What is Logistic Regression
04:13
Logistic Regression Model
08:17