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Applied Statistics and Data Preparation with Python - Udemy

Doelgroep: Beginner
Duur: 47 colleges - 2 uur
Richtprijs: € 199,99
Taal: Engels
Aanbieder: Udemy

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This is the bite size course to learn Python Programming for Applied Statistics. In CRISP DM data mining process, Applied Statistics 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

  • Mode

  • Median

  • Mean

  • Range

  • Range One Column

  • Qunatile

  • Variance

  • Standard Deviation

  • Histogram

  • QQPLot

  • Shapiro Test

  • Skewness and Kurtosis

  • Describe()

  • Correlation

  • Covariance

  • One Sample T Test

  • Two Sample TTest

  • Chi Square Test

  • One Way ANOVA

  • Simple Linear Regression

  • Multiple LInear Regression

  • 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


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