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Data Visualization with Python: The Complete Guide

The amount of data being generated each day is no longer avoidable. The importance of data is becoming a crucial aspect for predicting the future. This is why Data Science has become an important aven...

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Course Description

The amount of data being generated each day is no longer avoidable. The importance of data is becoming a crucial aspect for predicting the future. This is why Data Science has become an important avenue for learning. Data Science is the art of creating algorithms to compile, sort, process and analyze the data that is being collected in bulk from across the internet. This data is then used to un...

The amount of data being generated each day is no longer avoidable. The importance of data is becoming a crucial aspect for predicting the future. This is why Data Science has become an important avenue for learning. Data Science is the art of creating algorithms to compile, sort, process and analyze the data that is being collected in bulk from across the internet. 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 is going to become more important as we continue, with more people using data to make crucial decisions. This is why Data Scientists are currently one of the well-paid individuals on the market. This is also the perfect time to get into Data Science. This is where we can help! We have a built an easy comprehensive course on Data Science to help you master data visualizations using Python. The course will start at the very beginning helping you understand the importance of Data Science, along with becoming familiar with Matplotlib, Python’s very own visualization library. From there you will learn about the linear general statistics and data analysis. That’s not all, we’ll also cover important concepts such as data clustering, hypothesis gradient descent and advanced data visualizations.

What you’ll learn
  • Introduction to Data Science including concepts such as Linear Algebra, Probability and Statistics
  • Python’s Data Science Library, Matplotlib
  • Charts and Graphs
  • Data Analysis
  • Visualization of non uniform data
  • Hypothesis and Gradient Descent
  • Data Clustering
  • Projects that show you how to build visuals using Python

Covering Topics

1
Section 1 : Introduction to Course
2
Section 2 : Data Visualization
3
Section 3 : Linear Algebra
4
Section 4 : Statistics
5
Section 5 : Probability
6
Section 6 : Data Analysis
7
Section 7 : Advanced Data Visualization
8
Section 8 : Export Feature - Data Visualization
9
Section 9 : Hypothesis and Gradient Descent
10
Section 10 : Data Clustering

Curriculum

      Section 1 : Introduction to Course
1
Introduction Preview
2
Overview of Course
3
Understanding Concepts of Data Science
4
Python as a Tool
5
Crash Course of Python
6
Sample Scripts with Loops in Python Preview
7
Object Oriented Programming
8
Functional Tools
      Section 2 : Data Visualization
9
Understanding Data Visualization
10
Matplotlib library
11
Bar Charts
12
Line Charts Preview
13
Scatter Plots
14
A1. Activity for Data Visualization
      Section 3 : Linear Algebra
15
What are Vectors. Various operations of vectors
16
Vectors
17
Understanding Matrices
18
Matrices Preview
19
A2. Activity for Vectors Implementation
20
A3. Activity for Matrix Implementation
      Section 4 : Statistics
21
A. Single Set of Data
22
Single set of data
23
Concepts of Central Tendencies
24
Central Tendencies
25
Dispersion
26
A4. Activity for implementation of statistics
      Section 5 : Probability
27
Probability Concepts
28
The Normal Distribution
29
Central Limit Theorem
30
A5.Activity for understanding
      Section 6 : Data Analysis
31
Understanding Data Analysis
32
Exploring One dimensional Data
33
Exploring Two dimensional data
34
Exploring many dimensions
35
Bubble charts representation
36
Data Munging
37
A6. Activity for understanding data analysis
      Section 7 : Advanced Data Visualization
38
Visualizing the contecnt of a 2D array
39
Adding a colormap legend to th figure
40
Visualizing nonuniform 2D data
41
Visualizing a 2D scalar Field
42
Visualizing contour lines
43
Polar charts
44
Plotting log charts for research
      Section 8 : Export Feature - Data Visualization
45
Generating a PNG picture
46
Generating PDF documents
47
Multiple graph plotting and export
48
Inserting sub figures
      Section 9 : Hypothesis and Gradient Descent
49
Understanding Hypothesis
50
Implementation of hypothesis in Python
51
Gradient Descent
52
Implementation of Gradient Descent
53
A7. Activity for illustration of Gradient Descent
54
A7. Output for Gradient Descent Activity
      Section 10 : Data Clustering
55
Data Clustering concepts
56
Developing a data cluster model
57
Illustration of data clustering
58
A8 Activity for understanding data clusters

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