## Applied Statistics and Data Preparation with Python

Applied Statistics and Data Preparation with Python
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 956 MB
Duration: 2 hours | Genre: eLearning Video | Language: English

Applied Statistics with Python

What you'll learn

Applied Statistics using Python

Requirements

Fundamentals Python programming

Description

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

Mode

Median

Mean

Range

Range One Column

Qunatile

Variance

Standard Deviation

Histogram

QQDescription

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.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

Who this course is for:

Beginner Data Scientist or Analyst interested in Python programming

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