Applied Statistics and Data Preparation with Python

Applied Statistics with Python

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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 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 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. Mode

  8. Median

  9. Mean

  10. Range

  11. Range One Column

  12. Quantile

  13. Variance

  14. Standard Deviation

  15. Histogram

  16. QQPLot

  17. Shapiro Test

  18. Skewness and Kurtosis

  19. Describe()

  20. Correlation

  21. Covariance

  22. One Sample T Test

  23. Two Sample TTest

  24. Chi-Square Test

  25. One Way ANOVA

  26. Simple Linear Regression

  27. Multiple Linear Regression

  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

What You Will Learn!

  • Applied Statistics using Python

Who Should Attend!

  • Beginner Data Scientist or Analyst interested in Python programming