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

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.