We did summarized statistics a few weeks back, and we are translating what we learned from Excel into Jupyter Notebooks.

• Lesson 1.2 – Measures of central tendency
• Lesson 1.3 – Variance, Standard Deviation, and Z-Score

We will be rehashing what we learned before.

As data analysts and scientists, statistics is the main skill where we derive insights from data. We don’t need to be PhD students to derive insights from data as we only need to be able to profile and understand the data according to the business context at hand.

Summary Statistics in Python

Let’s open up the file(s) in the 01-Ins_Summary_Statistics folder to get started.

Quantiles and Outliers in Python

Let’s open up the file in the 02-Ins_Quartiles_and_Outliers to get started.

Students Do: Summary Statistics in Python

Let’s open up the file in the 03-Stu_Settlements-PlottingPandas folder to get started.

Sample, Population, and SEM

Let’s open up the file(s) in the 04-Ins_Standard_Error folder to get started.

Students Do: SEM and Error Bars

Let’s open up the file(s) in the 05-Par_Standard_Error folder to get started.

Correlation Conundrum

Let’s open up the file(s) in the 06-Ins_Correlation_Conundrum to get started.

Students Do: Correlation Conquerors

Look at the file(s) in the 07-Stu_Correlation_Conquerors folder.

Fits and Regression

Let’s open up the file(s) in the 08-Ins_Fits_and_Regression to get started.

Students Do: Fits and Regression

Let’s open up the file(s) in the 09-Stu_Fits_and_Regression to get started.