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Complete Data Wrangling and Data Visualization With Python

(33 Ratings) 1324 Students Enrolled
Created By Kanesha L Last Updated Sat, 04-Jul-2020 English
  • Course Duration
    6 Hours
  • Mode of Training
    Self-Paced
  • Lessons
    51 Lessons
  • Placement Assistance
    Guaranteed
$ 199.99 $ 14.99 93% off 100% Money Back Guarantee
12k+ satisfied learners Read Reviews
What Will I Learn?
  • Learn how to install and use Python Data Science Environment
  • Learn how to process Data Pre-processing & Wrangling in the Jupyter Environment
  • Learn from basic to advanced concepts of Data Processing, Data Wrangling and Data Visualization
  • Build Powerful graphs and visualization from Real data
  • Read in Data into the Jupyter / iPython Environment from different sources
  • Learn how to use the most important Data Wrangling and Visualization packages such as Matplotlib
  • Learn to identify which Visualization should be used in ANY given situations.

Requirements
  • Anaconda Environmental Setup
  • Data Visualization basics
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Description

This course get you the knowledge of how to acquire the statistical data analysis wrangling and visualization skills that will be useful for your career.

Following things are learned from this course,such as:

  • This course gives you from basic to advanced most common Python data wrangling and visualization tasks.
  • It will also equip you to use most important Python Data wrangling and visualization packages such as seaborn.
  • Through this course you learn the most of data visualizations concepts in a practical manner so that you can apply those concepts for practical data analysis and interpretation.
  • Learn how to decide which data wrangling and visualization techniques are best suited to answer your questions and applicable to your data and interpret the results.
Curriculum For This Course
51 Lessons 6 Hours
  • Best Python Data Science Environment 00:10:58 Preview
  • For Mac Users 00:04:05
  • Introduction to IPython/Jupyter 00:19:14
  • ipython in Browser 00:03:27
  • Data and Code - 1
  • Data and Code - 2
  • Data and Code - 3
  • Data and Code - 4
  • What are Pandas? 00:12:07 Preview
  • Read CSV Data 00:05:43
  • Read Excel Data 00:05:32
  • Remove NA Values 00:10:28 Preview
  • Missing Values in a Real Dataset 00:06:04
  • Data Imputation 00:09:07
  • Imputing Qualitative Values 00:03:28
  • Use k-NN for Data Imputation 00:06:23
  • Basic Principles 00:04:20
  • Preliminary Data Explorations 00:08:17
  • Basic Data Handling With Conditional Statements 00:05:24
  • Drop Column/Row 00:04:42
  • Change Column Name 00:03:36
  • Change the Column Type 00:03:50
  • Explore Date Related Data 00:04:03
  • Simple Date Related Computations 00:03:46
  • Data Grouping 00:09:47 Preview
  • Data Subsetting and Indexing 00:09:44
  • More Data Subsetting 00:08:55
  • Extract Information From Strings 00:04:40
  • (Fuzzy) String Matching 00:02:40
  • Ranking & Sorting 00:08:03
  • Concatenate 00:08:16
  • Merging and Joining 00:10:47
  • Correlation Analysis 00:08:27 Preview
  • Using Correlation to Decide Which Features to Retain 00:05:01
  • Univariate Feature Selection 00:04:56
  • Recursive Feature Elimination (RFE) 00:04:27
  • Theory Behind PCA 00:02:38
  • Implement PCA 00:03:53
  • Data Standardisation 00:04:10
  • Create a New Feature 00:06:17
  • What is Data Visualisation? 00:09:34 Preview
  • Some Theoretical Principles Behind Data Visualisation 00:06:46
  • Histograms-Visualize the Distribution of Continuous Numerical Variables 00:12:13 Preview
  • Boxplots-Visualize the Distribution of Continuous Numerical Variables 00:05:55
  • Scatter plot-Relationship Between Two Numerical Variables 00:11:58
  • Barplot 00:22:26
  • Pie Chart 00:05:30
  • Line Charts 00:12:32
  • More Line Charts 00:02:32
  • Some More Plot Types 00:11:15
  • And Some More 00:08:40

Complete Data Wrangling and Data Visualization With Python