Data Visualizations Using Python With Data Preparation | Simpliv

This is the 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 a 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 future)
• Machine Learning with Python (Modeling and Evaluation)

Content

• Getting Started
• Getting Started 2
• Getting Started 3
• Data Mining Process
• Download Data set
• Read Data set
• Bar Chart
• Histogram
• Line Chart
• Multiple Line Chart
• Pie Chart
• Box Plot
• Scatterplot
• Scatterplot Matrix
• Save To Image
• Bar Chart with Seaborn
• Histogram with Seaborn
• Line Chart with Seaborn
• Scatterplot with Seaborn
• Categorical PLot with Seaborn
• Boxplot with Seaborn
• Scatterplot Matrix with Seaborn
• Save To Image
• Interactive Charts
• Interactive Charts
• Interactive Charts
• Interactive Charts
• Data Processing: DF.head()
• Data Processing: DF.tail()
• Data Processing: DF.describe()
• Data Processing: Select Variables
• Data Processing: Select Rows
• Data Processing: Select Variables and Rows
• Data Processing: Remove Variables
• Data Processing: Append Rows
• Data Processing: Sort Variables
• Data Processing: Rename Variables
• Data Processing: GroupBY
• Data Processing: Remove Missing Values
• Data Processing: Is THere Missing Values
• Data Processing: Replace Missing Values
• Data Processing: Remove Duplicates