Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Data Visualizations using Python with Data Preparation
Rating: 3.0 out of 5(6 ratings)
521 students
Created byGoh Ming Hui
Last updated 2/2019
English

What you'll learn

  • Applied Statistics using Python

Course content

1 section • 46 lectures • 1h 23m total length

  • Getting Started10:48
  • Getting Started 22:04
  • Getting Started 32:52
  • Getting Started 45:40
  • Data Mining Process5:37
  • Download Dataset1:11
  • Read CSV2:03

    Discover how to read csv data in python by importing modules, loading a file into variables, and appending data for visualization with basic libraries.

  • Bar Chart5:19
  • Bar CHart1:12
  • Histogram1:35

    Learn to create histograms in python to visualize data distributions, read data, and customize visuals with color and transparency.

  • LIne CHart1:25
  • Multiple Line Chart0:41

    Learn to create a multiple line chart in Python with data preparation, using color variations to emphasize changes across data series.

  • Pie Chart1:38
  • Scatterplot2:21
  • Boxplot0:53
  • Boxplot0:20
  • Scatterplot Matrix1:28
  • Save To Image0:53
  • Bar CHart with SeaBorn1:53
  • Histogram with SeaBorn1:15
  • LIne CHart with SeaBorn0:59
  • Scatterplot with SeaBorn0:20
  • Categorical PLot with SeaBorn0:46
  • Boxplot with SeaBorn0:35
  • Scatterplot Matrix with SeaBorn0:53

    Explore setting up data entries and preparing documentation as you build a scatterplot matrix with SeaBorn in Python, within data visualizations and data preparation.

  • Save Image for Seaborn0:54
  • INteractive Chart6:07
  • INteractive Chart3:51
  • INteractive Chart1:54

    Develop and customize an interactive chart by setting up the chart, copying and adjusting trees, and comparing ideas within a graph setup in python.

  • INteractive Chart2:13

    Explore interactive charts in Python, from scatter and bar charts to pie, Sankey, and population pyramid visualizations, and learn how to customize themes and present data online.

  • Data Processing: DF.Head()1:13
  • Data Processing: DF.Tail()0:16
  • Data Processing: DF.Describe()0:21

    Investigate descriptive statistics for your data by using the describe function to obtain a concise overview of the hard data.

  • Data Processing: Select Variable or Column0:24
  • Data Processing: Select Variable or Column0:25
  • Data Processing: Select Rows0:41
  • Data Processing: Select Rows and Variables0:51
  • Data Processing: Remove Variables0:27

    Drop columns in a dataset using Python to remove variables, streamlining data preparation for visualizations.

  • Data Processing: Append Rows1:26
  • Data Processing: Sort Variable1:10
  • Data Processing: Rename Variables2:39
  • Data Processing: GroupBy1:58

    Learn to perform data processing in Python using groupby to segment data into groups, then apply aggregations such as mean to reveal insights from species, lengths, and other group-level metrics.

  • Data Processing: Remove Missing Values0:37

    Learn to remove missing values in data preparation by dropping them with a simple function, ensuring clean data for Python-based data visualizations.

  • Data Processing: Is there Missing Values0:32
  • Data Processing: Replace Missing Values0:22
  • Data Processing: Remove Duplicates0:39

Requirements

  • Fundamentals Python programming

Description

Why learn Data Analysis and Data Science?


According to SAS, the five reasons are


1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.


2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.


3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.


4. It's only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.


5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths.  Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.


The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.


This is a bite-size course to learn Python Programming for Data Visualization. In CRISP-DM data mining process, Data Visualization is at the Data Understanding stage. This course also covers Data processing, which is at the Data Preparation Stage. 

You will need to know some Python programming, and you can learn Python programming from my "Create Your Calculator: Learn Python Programming Basics Fast" course.  You will learn Python Programming for applied statistics.


You can take the course as follows, and you can take an exam at EMHAcademy to get SVBook Certified Data Miner using Python certificate : 

- Create Your Calculator: Learn Python Programming Basics Fast (R Basics)

- Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)

- Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation, in the future)

- Machine Learning with Python (Modeling and Evaluation)


Content

  1. Getting Started

  2. Getting Started 2

  3. Getting Started 3

  4. Data Mining Process

  5. Download Data set

  6. Read Data set

  7. Bar Chart

  8. Histogram

  9. Line Chart

  10. Multiple Line Chart

  11. Pie Chart

  12. Box Plot

  13. Scatterplot

  14. Scatterplot Matrix

  15. Save To Image

  16. Bar Chart with Seaborn

  17. Histogram with Seaborn

  18. Line Chart  with Seaborn

  19. Scatterplot  with Seaborn

  20. Categorical PLot  with Seaborn

  21. Boxplot  with Seaborn

  22. Scatterplot Matrix  with Seaborn

  23. Save To Image

  24. Interactive Charts

  25. Interactive Charts

  26. Interactive Charts

  27. Interactive Charts

  28. Data Processing: DF.head()

  29. Data Processing: DF.tail()

  30. Data Processing: DF.describe()

  31. Data Processing: Select Variables

  32. Data Processing: Select Rows

  33. Data Processing: Select Variables and Rows

  34. Data Processing: Remove Variables

  35. Data Processing: Append Rows

  36. Data Processing: Sort Variables

  37. Data Processing: Rename Variables

  38. Data Processing: GroupBY

  39. Data Processing: Remove Missing Values

  40. Data Processing: Is THere Missing Values

  41. Data Processing: Replace Missing Values

  42. Data Processing: Remove Duplicates

Who this course is for:

  • Beginner Data Scientist or Analyst interested in Python programming

Report abuse