O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Python and Data Science : A Practical Guide

Video Description

Learn Data Science and Python to do Web Scraping, Data Analysis, Data Visualization, Machine Learning, Deep Learning...

About This Video

  • Learn Python and Data Science in a practical way
  • Learn to Set up Python environment

In Detail

This course is designed to teach you the basics of Python and Data Science in a practical way, so that you can acquire, test, and master your Python skills gradually.

You’ll see that you'll learn all these things with Python:

  • Using Variables & Strings
  • Using Booleans & Logical Operators
  • Using Functions & Packages
  • Using Lists, Tuples and Dictionaries
  • Using For & While Loops
  • Using Panda & Data Frames
  • Doing Data Visualization
  • Scraping Web Data
  • Doing some basic Natural Language Processing (NLP)
  • Basics of Machine Learning & Deep Learning
  • And much more to come.

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Introduction
    1. Introduction 00:01:41
  2. Chapter 2 : Environment
    1. Introduction to data science 00:16:12
    2. Installing dependencies 00:10:10
    3. Real world examples 00:07:59
    4. Using Anaconda and Jupyter notebooks 00:14:12
  3. Chapter 3 : Integers and Strings
    1. Introduction to variables 00:17:54
    2. Integers, floats, and math operators 00:14:31
    3. Strings and indexing part 1 00:13:14
    4. Strings and indexing part 2 00:09:11
    5. Using Modulo with Strings 00:09:24
    6. Problem 00:03:33
    7. Answer 00:06:50
  4. Chapter 4 : If Statements and Basic Programming Logic
    1. Booleans and Comparison Operators 00:13:57
    2. If / Else Statements 00:10:17
    3. Elif and Logic Operators 00:11:37
    4. Try / Except Statements 00:10:28
    5. Finally Statements and Advance Try/Except Logic 00:09:10
    6. Exception Types 00:12:59
    7. Problem 00:02:35
    8. Answer 00:06:35
  5. Chapter 5 : Lists, Tuples, Dictionaries and For/While Loops
    1. Lists and tuples 00:18:22
    2. Dictionaries 00:16:24
    3. For loops 00:16:07
    4. While loops 00:11:25
    5. Loop logic 00:12:59
    6. List/Dictionary comprehensions 00:12:00
    7. Problem 00:02:21
    8. Answer 00:10:51
  6. Chapter 6 : Functions and Packages
    1. Scripting in Python 00:12:57
    2. Functions 00:12:36
    3. Function parameters and scope 00:10:15
    4. Packages and pip 00:12:54
    5. Reading Files and the with statement 00:17:01
    6. Writing Out to Files 00:20:39
    7. Problem 00:02:47
    8. Answer 00:08:03
  7. Chapter 7 : Pandas and Data Frames
    1. Pandas and data Frames 00:13:03
    2. Pandas part 2 00:17:33
    3. Introduction to statistics 00:13:21
    4. Statistics part 2 00:10:26
    5. Practical analysis 00:15:12
    6. Problem 00:05:12
    7. Answer 00:05:54
  8. Chapter 8 : Visualization - Scatter Plots, Bar Plots
    1. Introduction to visualization 00:13:32
    2. Plotting and styling 00:14:23
    3. Scatter plots 00:13:23
    4. Bar Plots and standard deviation 00:14:11
    5. Problem 00:03:29
    6. Answer 00:12:25
  9. Chapter 9 : Scraping the Web with Python
    1. Scraping and HTML 00:14:56
    2. Building a simple crawler 00:12:12
    3. Scraping data from HTML 00:14:48
    4. Problem 00:03:45
    5. Answer 00:09:20
  10. Chapter 10 : Basics of Natural Language Processing (NLP)
    1. Introduction to NLP 00:13:13
    2. Basic NLP 00:16:24
    3. Introduction to sentiment analysis 00:19:04
    4. Problem 00:02:31
    5. Answer 00:08:13
  11. Chapter 11 : Introduction to Machine Learning
    1. Introduction to machine learning 00:15:35
    2. Introduction to reinforcement learning 00:05:30
    3. Introduction to random forest modeling 00:16:37
    4. Introduction to deep learning 00:16:08
    5. Problem 00:07:06
    6. Answer 00:06:54
  12. Chapter 12 : Optional Classes
    1. Python Style Guidelines 00:11:26
    2. Navigating Directories and os Package 00:12:23
    3. Creating a Class Type 00:11:51
  13. Chapter 13 : Project #1 - Analyze and visualize data on Kaggle
    1. Project 1 - Analyze and visualize data on Kaggle 00:04:38
    2. Project 1 Answer 00:02:47
  14. Chapter 14 : Project #2 - Natural language processing
    1. Project 2 - Natural language processing 00:02:19
    2. Project 2 Answer 00:04:43
  15. Chapter 15 : Project #3 - Create a simple support vector machine
    1. Project 3 - Create a simple support vector machine 00:01:46
    2. Project 3 Answer 00:03:06