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Data Visualization with Python: The Complete Guide

Video Description

Learn to visualize your data using Python in this data science course

About This Video

  • This course will help you understand the importance of data science, along with becoming familiar with Matplotlib, Python’s very own visualization library.
  • Learn about the linear general statistics and data analysis.
  • We’ll also go over important concepts such as data clustering, hypothesis gradient descent, and advanced data visualizations.

In Detail

Data is becoming a force to reckon with. With the amount of data that is being generated every minute, dealing with data has become more important. The importance of data lies in the fact that it allows us to look at our history and predict the future. Data science is the field that deals with collecting, sorting, organizing and also analyzing huge amounts of data. This data is then used to understand the current and future trends. This field borrows techniques and theories from across multiple fields such as mathematics, statistics, computer science, information science, etc. It also aids other domains such as machine learning, data mining, databases and visualization. Data scientists are gaining importance and are also earning higher salaries, which means this is the right time to become a data scientist. While, it might seem easy, sorting data, these scientists are responsible for writing important algorithms and programs to help sort and analyze the data – and this isn’t an easy task. The course will cover a number of different concepts such as an introduction to data science including concepts such as linear algebra, probability and statistics, Matplotlib, charts and graphs, data analysis, visualization of non uniform data, hypothesis and gradient descent, data clustering and so much more. That’s not all, we’ll also include projects to help you show exactly how to build visuals using Python. You can learn all this and tons of interesting stuff in this unique data science course. Enroll now and start building next generation interfaces for your data.

Table of Contents

  1. Chapter 1 : Introduction to Course
    1. Introduction 00:01:01
    2. Overview of Course 00:04:05
    3. Understanding Concepts of Data Science 00:06:58
    4. Python as a Tool 00:03:23
    5. Crash Course of Python 00:10:09
    6. Sample Scripts with Loops in Python 00:07:47
    7. Object Oriented Programming 00:06:31
    8. Functional Tools 00:04:15
  2. Chapter 2 : Data Visualization
    1. Understanding Data Visualization 00:04:14
    2. Matplotlib library 00:08:04
    3. Bar Charts 00:10:00
    4. Line Charts 00:06:42
    5. Scatter Plots 00:06:00
    6. A1. Activity for Data Visualization 00:07:52
  3. Chapter 3 : Linear Algebra
    1. What are Vectors? Various operations of vectors 00:04:11
    2. Vectors 00:07:57
    3. Understanding Matrices 00:05:31
    4. Matrices 00:09:38
    5. A2. Activity for Vectors Implementation 00:09:33
    6. A3. Activity for Matrix Implementation 00:07:18
  4. Chapter 4 : Statistics
    1. A. Single Set of Data 00:02:32
    2. Single set of data 00:07:08
    3. Concepts of Central Tendencies 00:04:54
    4. Central Tendencies 00:07:46
    5. Dispersion 00:09:04
    6. A4. Activity for implementation of statistics 00:07:34
  5. Chapter 5 : Probability
    1. Probability Concepts 00:03:07
    2. The Normal Distribution 00:09:09
    3. Central Limit Theorem 00:07:23
    4. A5.Activity for understanding 00:06:02
  6. Chapter 6 : Data Analysis
    1. Understanding Data Analysis 00:03:43
    2. Exploring one dimensional Data 00:08:12
    3. Exploring Two dimensional data 00:12:49
    4. Exploring many dimensions 00:08:49
    5. Bubble charts representation 00:04:17
    6. Data Munging 00:08:13
    7. A6. Activity for understanding data analysis 00:07:15
  7. Chapter 7 : Advanced Data Visualization
    1. Visualizing the content of a 2D array 00:07:27
    2. Adding a colormap legend to the figure 00:03:38
    3. Visualizing nonuniform 2D data 00:07:48
    4. Visualizing a 2D scalar Field 00:04:46
    5. Visualizing contour lines 00:07:32
    6. Polar charts 00:05:56
    7. Plotting log charts for research 00:08:37
  8. Chapter 8 : Export Feature - Data Visualization
    1. Generating a PNG picture 00:09:13
    2. Generating PDF documents 00:05:53
    3. Multiple graph plotting and export 00:07:56
    4. Inserting sub figures 00:04:35
  9. Chapter 9 : Hypothesis and Gradient Descent
    1. Understanding Hypothesis 00:03:46
    2. Implementation of hypothesis in Python 00:13:22
    3. Gradient Descent 00:04:08
    4. Implementation of Gradient Descent 00:12:44
    5. A7. Activity for illustration of Gradient Descent 00:14:54
    6. A7. Output for Gradient Descent Activity 00:05:12
  10. Chapter 10 : Data Clustering
    1. Data clustering concepts 00:11:36
    2. Developing a data cluster model 00:10:22
    3. Illustration of data clustering 00:14:41
    4. A8 Activity for understanding data clusters 00:07:10