Data Science Quick: Focus On Correlation & Python

Name: Data Science Quick: Focus On Correlation & Python
Rating: 4.6 (14 reviews)

Make This The Year To Get Started in Data Science: Learn The Rudiments of Correlation Analysis & Python.

Created byAdam Ross Nelson

Last updated 5/2025

English

What you'll learn

Students will learn to get started with Data Science, quick.
This course will teach the rudiments of correlation analysis.
This course will also teach the rudiments of python.
Software installation is not required: but this course will provide helpful suggestions for students that wish to install Python.
Students will learn to execute correlation analysis in Ptyhon from scratch.
The bonus material also teaches students about Pandas, Data Management, and other related topics.

Course content

7 sections • 42 lectures • 2h 12m total length

Welcome & Introduction2:09
Welcome to the data science quick start course. My name is Adam Ross Nelson. I am a data scientist. In this lecture I will explain the purpose of this course.
This course is for:
- Students who have an interest in learning about data science from the start.
- The course will teach students how to execute correlation analysis from scratch, using pen and paper.
- Students will also receive a crash course in Python.
- The capstone experience is for students to combine their knowledge of correlation analysis with their knowledge of Python in order to execute correlation analysis in Python.
This video provides a thorough overview of the entire course.
How To Ask For Help Online0:09
It is useful to know how to ask questions online. This article offers advice on how to ask for help online.
A Few Key Data Science & Statistics Concepts3:44
This lecture will introduce students to data and data sets. It will answer four questions:
First, what is data?
Second, what is a data set?
Third, what are variables?
Fourth, what are observations?
There are two articles, provided in PDF and via link to Medium's Towards Data Science. These articles are optional reading to help learn the key concepts explained in the lecture.
Introduction To Data Science Module Recap0:04

Correlation Worksheets (Statistics)0:09
Download these worksheets for reference during the Correlation Parts 1 & 2 lectures.
These worksheets will also be useful as an additional reference during the course capstone. Print these worksheets for your use and reference during the lectures.
Correlation Part 13:35
This video is the first of two videos that provide instruction on how to calculate a correlation coefficient. This video specifically:
- Explores a simple data set that compares human age and height.
- Discusses why and why a correlation coefficient is useful and how to interpret a correlation coefficient.
- Introduces the notation used to communicate work related to correlation analysis.
- Discusses and explains the formula used to calculate a correlation coefficient.
Correlation Part 211:35
This video is the second of two videos that provide instruction on how to calculate a correlation coefficient. This video specifically:
- Shows the step-by-step process that produces a correlation coefficient.
- This video shows on-screen with a simulated on-screen pen & paper experience how to calculate a correlation analysis.
Correlation Part 34:10
Correlation Knowledge Check Introduction0:39
The correlation knowledge check is an opportunity to check and test your understanding of the process involved in calculating a correlation coefficient.
This video will introduce the correlation knowledge check. Students should both watch this lecture and work through the attached knowledge check worksheet before proceeding to the next video that explains the worksheet's solution.
Correlation Knowledge Check Solution6:42
This lesson provides an overview of the knowledge check solution. Use this video lesson to check your work.
The correlation knowledge check is an opportunity to check and test your understanding of the process involved in calculating a correlation coefficient.
This video will explain the solution to the correlation knowledge check.
Students should both watch the previous lecture and work through the knowledge check worksheet before proceeding to this video.
Correlation, Data Science Quick Start Recap0:04
This article summarizes information presented throughout the correlation module in Data Science Quick Start: A Focus on Correlation & Python.

Optional Discussion Lecture3:40
In this video I discuss my gratitude to those who take this course. I also offer congratulations to those who have already completed the course's first major module on correlation analysis.
My hope is that you are enjoying your journey in statistics, data science, correlation, and python.
This video also provides information related to connecting me via social media. I ask course attendees to reach out and let me know what is going well with the course, and also what could go better.

How to Access Python5:17
Python Roadmap + Restarting Notebooks2:36
Navigating Notebooks, Variables, + Math8:01
A Quick Look at Operators2:31
Lists + Referencing Specific Items3:13
Operating on Lists4:41
Pairwise List Operations3:05
Aggregation, Sum, Len, + Related4:34
Square Roots + Imports3:26
Python Crash Knowledge Check Introduction3:30
The Python crash course knowledge check is an opportunity to check and test your understanding of Python.
This video will introduce the Python knowledge check. Students should both watch this lecture and work through the attached knowledge check Jupyter Notebook before proceeding to the next video that explains the worksheet's solution.
The video demonstrates how to open this Jupyter Notebook on the free online notebook server at https://jupyter.org/try
Python Crash Knowledge Check Soultion4:43
This lesson provides an overview of the Python knowledge check solution. Use this video lesson to check your work on the Python knowledge check solution.
The Python knowledge check is an opportunity to check and test your understanding.
This video will explain the solution to the Python knowledge check Jupyter Notebook.
The video demonstrates how to open this Jupyter Notebook on the free online notebook server at https://jupyter.org/try
Students should both watch the previous lecture and work through the Python knowledge check worksheet before proceeding to this video.
Python Crash Course Recap0:02

Capstone Experience Introduction2:22
Congratulations on reaching this stage in the course. You are now ready to calculate a correlation coefficient on your own. This video introduces the three knowledge check worksheets I provided for students in this course.
Capstone Experience Solution6:06
This video shows the solution to the first of the three capstone experiences that were introduced in the previous video.

Addon 1.0 Function Introduction1:10
Addon 2.0 Function Definition (The def statement)1:48
Addon 3.0 Refactoring Correlation Code2:35
Addon 3.2 Refactoring Correlation...2:40
Refactor the correlation calculation into a reusable python function by converting age and salary to generic x and y, subtracting means to form errors, and computing covariance from these errors.
Addon 3.4 Refactoring Correlation...1:29
Addon 3.6 Refactoring Correlation1:47
Addon 4.0 Checking For Errors Pt 11:17
Addon 4.2 Checking For Errors Pt 21:19
Addon 5.0 Return Values1:44
Addon 6.0 Planning Output2:30
Addon 7.0 Python Correlation Function Conclusion0:18

Additional Python One - Corr Shortcuts4:54
Usually, it is unnecessary to calculate correlation from scratch. This video shows one of the many available shortcuts.
Additional Python Two - Explore Pandas5:31
Building on the information discussed in the previous bonus, this video explores the features available in Pandas.
Additional Python Three - More List Comprehension5:41
This video provides additional instruction on Python list comprehension. Use it as a refresher or as a supplant to this course's Python crash course module.
Additional Python Four - Merge Data3:46
Eventually, it will be helpful to know how to merge multiple datasets. This video shows how to do that using Pandas.
Additional Python Five - New Features Data Mgmt3:37
After creating new columns it is helpful to move the order of those columns within a Pandas data frame. This video shows creating new columns and then also changing their order for better organization.

Requirements

There are no prerequisites.
This course is designed for individuals that have no, or very little, experience with data science.
Students looking to a review or a refresher of correlation analysis or Python will also benefit from this course.
No software installation is required.

Description

Taught by a professional data scientist with more than 20 years of experience in teaching and education. Dr. Adam Ross Nelson has taught students of all ages and throughout the world. He has a PhD in Education. He has also taught high school students, college students, graduate students, and seasoned professionals. He also frequently helps technical professionals enter and level up in data science.

Everyone has to start someplace! A "quick start" in data science isn't a contradiction in terms! This course is for beginners.

Students will learn to get started with Data Science, quick. This course will teach the rudiments of two topics.

First, it will teach correlation analysis.
Second, it will teach Python.

There are knowledge checks to help students assess their own learning.

There is also a capstone experience. The capstone experience will ask students to implement correlation analysis from scratch in Python. There are no prerequisites.

This course does not require software installation.

More about the suggested roadmap: The Road To A Quick Start In Data Science

Learn, or re-learn to execute correlation analysis from scratch.
Learn a few simple coding techniques.
Execute correlation analysis from scratch in python.
Keep going! If that is what you want. Don’t look back.

Once you finish with these four steps, there will be more work ahead. Inside this course are guides and suggestions for next steps.

Who this course is for:

Students looking to get started with data science.
Students looking to learn (or review) the rudiments of correlation analysis.
Students looking to learn (or review) the rudiments of Python.
Anyone interested in a path to getting started with data science, quickly.

Data Science Quick: Focus On Correlation & Python

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 6min

Module One - Correlation Analysis7 lectures • 27min

Optional Discussion1 lecture • 4min

Module Two - Python Crash Course12 lectures • 46min

Course Capstone Experience2 lectures • 8min

Addon My Python Lessons, How To Define A Correlation Function11 lectures • 19min

Additional Python Instruction5 lectures • 23min

Requirements

Description

Who this course is for: