Learning Python for Data Science

Video description

Gain an in-depth understanding of data analysis with various Python packages

About This Video

  • Learn data analysis, manipulation, and visualization using the pandas library
  • Create statistical plots using Matplotlib and Seaborn to help you get insights into real-size patterns hidden in data
  • Gain an in-depth understanding of the various Python packages to perform data analysis and implement effective machine learning models

In Detail

Python is an open source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity, data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to deploy its features for data science applications.

In this course, you will learn all the necessary libraries that make data analytics with Python rewarding and effective. You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the NumPy library used for numerical and scientific computation. You will employ useful libraries for visualization (Matplotlib and Seaborn) to provide insights into data. Further, you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, to enable you to utilize your learning within your own projects.

By the end of this course, you’ll have progressed through a journey from data cleaning and preparation to creating summary tables, and from visualization to machine learning and prediction. This video course will prepare you to enter the world of data science. Welcome to our journey!

This course uses Python 3.6, while not the latest version available, it provides relevant and informative content for legacy users of Python.

Table of contents

  1. Chapter 1 : Beginning the Data Science Journey
    1. The Course Overview 00:02:09
    2. What Is Data Science? 00:02:29
    3. Python Data Science Ecosystem 00:02:26
  2. Chapter 2 : Introducing Jupyter
    1. Installing Anaconda 00:01:12
    2. Starting Jupyter 00:01:13
    3. Basics of Jupyter 00:02:02
    4. Markdown Syntax 00:02:43
  3. Chapter 3 : Understanding Numerical Operations with NumPy
    1. 1D Arrays with NumPy 00:07:43
    2. 2D Arrays with NumPy 00:11:31
    3. Functions in NumPy 00:10:25
    4. Random Numbers and Distributions in NumPy 00:08:45
  4. Chapter 4 : Data Preparation and Manipulation with Pandas
    1. Create DataFrames 00:06:44
    2. Read in Data Files 00:06:26
    3. Subsetting DataFrames 00:06:04
    4. Boolean Indexing in DataFrames 00:04:41
    5. Summarizing and Grouping Data 00:05:29
  5. Chapter 5 : Visualizing Data with Matplotlib and Seaborn
    1. Matplotlib Introduction 00:09:54
    2. Graphs with Matplotlib 00:06:15
    3. Graphs with Seaborn 00:11:44
    4. Graphs with Pandas 00:08:45
  6. Chapter 6 : Introduction to Machine Learning and Scikit-learn
    1. Machine Learning 00:03:29
    2. Types of Machine Learning 00:03:24
    3. Introduction to Scikit-learn 00:04:01
  7. Chapter 7 : Building Machine Learning Models with Scikit-learn
    1. Linear Regression 00:12:23
    2. Logistic Regression 00:06:25
    3. K-Nearest Neighbors 00:08:01
    4. Decision Trees 00:05:46
    5. Random Forest 00:05:47
    6. K-Means Clustering 00:05:18
  8. Chapter 8 : Model Evaluation and Selection
    1. Preparing Data for Machine Learning 00:11:15
    2. Performance Metrics 00:09:12
    3. Bias-Variance Tradeoff 00:08:04
    4. Cross-Validation 00:06:14
    5. Grid Search 00:08:37
    6. Wrap Up 00:02:38

Product information

  • Title: Learning Python for Data Science
  • Author(s): Ilyas Ustun
  • Release date: July 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781785886928