2023 CORE: Data Science and Machine Learning

Focused View

Dr. Isaac Faber

28:10:11

194 View

1. Introduction - First Principals

1. Introduction.mp4

01:30

2. Course Overview.html

3. Course Structure.mp4

03:58

4. Course Philosophy.mp4

06:57

5. First Principles - Who.mp4

05:31

6. First Principles - Why 13.mp4

05:32

7. First Principles - Why 23.mp4

04:24

8. First Principles - Why 33.mp4

09:48

9. Reading Assignment.html

10. First Principles - What.mp4

08:47

11. First Principles - What Data Analyst Example Product.mp4

02:56

12. First Principles - What Data Scientist Example Product.mp4

04:45

13. First Principles - What Machine Learning Engineer Example Product.mp4

03:19

14. First Principles - What Data & Sources.mp4

04:32

15. First Principles - What Kaggle Introduction.mp4

02:39

16. First Principles - How.mp4

06:08

17. Data Science Battle Station.mp4

02:09

18. Section Wrap Up.mp4

04:36

19. Assignments.html

2. Data Analyst - Case Study - Intro & Basic Spreadsheets

1. Data Analyst Overview.mp4

06:49

2. Spreadsheets Overview.mp4

02:57

3. Introduction to MS Excel.mp4

02:27

4. Setting up MS Excel.html

5. Overview of MS Excel.mp4

08:50

6. Excel Templates.mp4

01:46

7. Workbook Overview.mp4

06:10

8. Protecting Workbooks.mp4

01:25

9. Sharing Workbooks.mp4

01:42

10. Operators.mp4

04:04

11. Built-in Functions.mp4

05:59

3. Data Analyst - Case Study - Intermediate Spreadsheets

1. Math - Summary Statistics.mp4

15:33

2. Calculating Summary Statistics from Scratch.mp4

06:16

3. Import a Text File.mp4

07:48

4. Data Tables.mp4

07:17

5. Summary Statistics on Tables.mp4

09:07

6. Summary Statistics Dashboard.html

7. Assignment Review.mp4

04:59

8. Importing Data - Intermediate.mp4

02:25

9. Lookups and Matches.mp4

07:04

10. Calculating Churn and Customer Lifetime Value.mp4

04:09

11. Financial Forecasting (Time Series).mp4

05:21

12. Data Visualization Introduction.mp4

04:04

13. Data Visualization Excel.mp4

06:53

14. Dashboards Best Practices.mp4

05:48

15. Dashboards in Excel.mp4

05:48

16. Build a Dashboard.html

17. Assignment Solution.mp4

02:29

4. Data Analyst - Case Study - Advanced Spreadsheets

1. Importing Data - Power Query.mp4

08:56

2. Pivot Tables.mp4

07:26

3. Mathematical Modeling - Linear Programming.mp4

08:58

4. Solver - Linear Programming in Excel.mp4

15:18

5. Analysis Toolpack.mp4

04:27

6. Visual Basic for Applications (VBA) - Introduction.mp4

06:21

7. Spreadsheet Conclusion.mp4

03:44

8. Complete LinkedIn Excel Assessment.html

5. Data Analyst - Case Study - SQL Basics

1. SSI - databases.mp4

20:32

2. SQL Text Editor - Sublime.mp4

03:34

3. SQL Syntax.mp4

06:45

4. Introduction to SQLite Databases.mp4

03:18

5. SQLite Install.mp4

03:02

6. SQLite Database Creation.mp4

06:47

7. Basic SQL - SELECT, FROM, WHERE statements.mp4

11:28

8. Basic SQL - BETWEEN, LIKE statements.mp4

02:27

9. Basic SQL - AND, OR, NOT, EXISTS, NULL statements.mp4

08:20

10. Basic SQL - ORDER BY, DISTINCT statements.mp4

04:21

6. Data Analyst - Case Study - SQL Intermediate and Advanced

1. Intermediate SQL - Aggregate Functions.mp4

07:48

2. Intermediate SQL - WITH and subqueries.mp4

06:51

3. Advanced SQL - Inserting, Updating, and Deleting data.mp4

09:11

4. Advanced SQL - Views.mp4

05:39

5. Connecting SQLite to Excel.mp4

04:10

6. Kaggle SQL Course.html

7. Data Analyst - Case Study - Business Intelligence and Tableau Introduction

1. Introduction to Business Intelligence (BI).mp4

10:41

2. Why Tableau.mp4

07:52

3. Installing Tableau Public.mp4

02:08

4. Tableau Overview.mp4

09:58

5. Tableau Data Types.mp4

02:30

6. Tableau Basic Viz.mp4

09:18

7. Tableau Filters.mp4

03:45

8. Connecting Tableau to OData Sources.mp4

07:03

9. Joining Data in Tableau.mp4

08:41

8. Data Analyst - Case Study - Tableau Intermediate and Advanced Topics

1. Tableau Intermediate Bar Charts.mp4

08:34

2. Tableau Dates.mp4

03:06

3. Tableau Visualizing Comparisons.mp4

04:56

4. Tableau Visualizing Distributions.mp4

07:05

5. Tableau Multiple Axis.mp4

04:18

6. Tableau Formating.mp4

06:06

7. Tableau Calculations and Parameters.mp4

11:46

8. Tableau Dashboards and Stories.mp4

15:47

9. Tableau Advanced Analysis.mp4

07:42

10. Sharing with Tableau Public.mp4

03:41

11. Tableau Desktop Pro Overview.mp4

04:57

12. Assignment Portfolio, and Resume Updates.html

9. Data Scientist - Case Study - Introduction to R

1. Introduction to the Data Scientist (Generalist).mp4

07:18

2. Overview of R.mp4

07:46

3. Intro to CRAN and installing base R.mp4

04:18

4. Installing RStudio.mp4

05:31

5. Overview of RStudio.mp4

06:06

6. Calculations in Base R.mp4

06:02

7. Objects in Base R.mp4

09:20

8. Functions in Base R.mp4

14:02

9. The Basics of R Scripts.mp4

04:46

10. Base R Datasets.mp4

02:48

11. Base R Help and Plots.mp4

05:48

12. Installing R Packages - More on Plots and Objects.mp4

09:30

13. Atomic Vectors.mp4

10:38

14. Object Attributes.mp4

03:58

15. Matrix and Array Objects.mp4

03:27

16. Classes.mp4

04:03

17. Factors.mp4

03:07

18. Coercion.mp4

03:25

19. Lists.mp4

05:30

20. Data Frames.mp4

06:57

21. Loading and Saving Data Part 1.mp4

10:43

22. Loading and Saving Data Part 2.mp4

02:50

23. Selecting Values from Data Frames.mp4

11:56

24. Changing Values in Data Frames.mp4

08:31

25. Sub Setting Data Frames.mp4

08:17

26. Missing Values.mp4

05:15

27. More on Selecting Values.mp4

01:28

28. Programming Flow Controls.mp4

06:57

10. Data Scientist - Case Study - Exploratory Data Analysis and the R Tidyverse

1. An Introduction to EDA.mp4

07:56

2. EDA Example on Kaggle.mp4

02:42

3. Expanding Summary Statistics - Location.mp4

07:55

4. Location Examples in R.mp4

02:25

5. Expanding Summary Statistics - Spread.mp4

04:24

6. Spread Examples in R.mp4

01:44

7. Important EDA Tools.mp4

04:03

8. Introduction to the Tidyverse and ggplot2.mp4

06:38

9. Tidyverse website.mp4

01:03

10. ggplot - Mapping Aesthetics.mp4

05:36

11. ggplot - Facets.mp4

04:09

12. ggplot - Multiple Geom.mp4

04:22

13. ggplot - Stat Transforms.mp4

02:57

14. ggplot - Position Adjustments.mp4

03:31

15. ggplot - Coord Systems.mp4

02:59

16. ggplot - Summary.mp4

01:28

17. ggplot - Gallery Book.mp4

01:22

18. R Object Names.mp4

03:22

19. dplyr - Overview.mp4

04:39

20. dplyr - Filter.mp4

06:48

21. dplyr - Arrange and Select.mp4

05:12

22. dplyr - Mutate.mp4

03:23

23. dplyr - Pipes, group_by, and summarise.mp4

09:19

24. stringr - Basics.mp4

11:53

25. stringr - Matching.mp4

09:09

26. lubridate - Basics.mp4

09:15

27. Intro to Markdown.mp4

10:25

28. Intro to RMarkdown.mp4

08:16

29. Quick Overview of Notebooks.mp4

03:17

30. EDA Assignment.html

11. Data Scientist - Case Study - Useful Statistics for Data Scientists

1. Intro to Useful Statistics.mp4

18:33

2. Useful Probability 1 of 2.mp4

15:29

3. Useful Probability 2 of 2.mp4

09:55

4. Distributions Using R.mp4

12:55

5. Useful Frequentist Statistics.mp4

07:35

6. Useful Frequentists Hypothesis Testing.mp4

12:18

7. Hypothesis Testing in R.mp4

09:40

8. AB Testing.mp4

07:57

9. AB Testing in R.mp4

03:03

10. Bootstrap Introduction.mp4

06:45

11. Bootstrap in R.mp4

04:59

12. Useful Bayesian Statistics.mp4

11:40

13. Bayesian Stats in R (beta-binomial).mp4

06:46

14. Bayesian Stats in R (Thompson Sampling).mp4

04:38

15. Useful Simulations - Monte Carlo.mp4

06:28

16. Monte Carlo Simulations in R.mp4

06:49

17. Useful Regression Modeling Introduction.mp4

13:24

18. Simple Linear Regression in R.mp4

10:33

19. Useful Multiple Regression.mp4

04:05

20. Multiple Regression in R.mp4

09:12

21. Regression Issues and Concerns.mp4

09:16

22. Useful Time Series Modeling.mp4

07:11

23. Assignment Update resume and portfolio.html

24. Time Series Modeling in R.mp4

06:41

12. Data Scientist - Case Study - Sharing Data Science With the Web

1. SSI - Web Development.mp4

11:54

2. SSI - Basic Web Development Example.mp4

08:29

3. Web Development with Git in RStudio 1 of 2.mp4

09:36

4. Web Development with Git in RStudio 2 of 2.mp4

08:09

5. Reading Assignment Git.html

6. Hosting with Github Pages.mp4

06:58

7. Using the blogdown R package to create websites.mp4

15:52

8. Introduction to Shiny.mp4

03:15

9. Hello World Shiny App.mp4

04:15

10. Closer Look at Shiny.mp4

05:03

11. Hosting Shiny and shinyapps.io.mp4

06:48

12. shinyapps.io and The shinydashboard Package.mp4

04:25

13. Data Scientist Section Closeout

1. Data Scientist Section Closeout.mp4

04:44

2. Assignment Update resume and projects.html

14. Machine Learning Engineer - Case Study - Introduction

1. Job Overview.mp4

08:35

15. ML Engineer - The Cloud, Docker, and Python Development Environments

1. SSI - Intro to the Cloud.mp4

09:46

2. SSI - The AWS Cloud Console.mp4

07:32

3. SSI - The Command Line Interface.mp4

06:48

4. Command Line Demo.mp4

07:19

5. SSI - Intro to Docker.mp4

05:46

6. SSI - Docker Continued.mp4

07:34

7. SSI - Docker Demo.mp4

03:55

8. Project Jupyter Docker Stack.mp4

05:06

9. Using Docker on a Cloud Server part 1.mp4

10:21

10. Using Docker on a Cloud Server part 2.mp4

08:47

11. Other Python Development Environments.mp4

04:01

12. Project Jupyter.mp4

06:29

16. Machine Learning Engineer - Case Study - Basic Python

1. Python Overview.mp4

04:31

2. Python in Jupyterlab.mp4

10:11

3. Basic Notebook Cell Operations.mp4

04:04

4. Basic Math Operations.mp4

05:09

5. Basic Data Types.mp4

02:38

6. Basic Variables.mp4

04:14

7. Basic Built-in Functions.mp4

04:36

8. Basic Comparison Operators.mp4

02:43

9. Basic Boolean Operators.mp4

02:07

10. Combining Boolean and Comparison Operators.mp4

01:47

11. Basic Elements of Control Flow.mp4

03:19

12. Control Flow Continued.mp4

12:58

13. Importing Modules.mp4

06:01

14. Functions.mp4

04:52

15. Local vs. Global Variables.mp4

02:43

16. Lists in Depth.mp4

05:03

17. Lists in Depth Continued.mp4

07:12

18. Additive Operators.mp4

01:40

19. Methods on Lists.mp4

05:13

20. Dictionaries.mp4

07:11

21. Classes and Methods.mp4

05:44

22. Interacting with Files.mp4

05:52

17. Machine Learning Engineer - Case Study - Intermediate Python for Data Science

1. Python for Data Science.mp4

02:45

2. Useful Matrix Operations.mp4

10:17

3. Numpy for Matrix Operations.mp4

07:18

4. Numpy Indexing and Slicing.mp4

04:07

5. Numpy Boolean Indexing.mp4

02:08

6. Numpy Reshape and Transpose.mp4

01:35

7. Numpy Pseudorandom Numbers.mp4

02:55

8. Numpy Unary and Binary Functions.mp4

02:48

9. Numpy Aggregate Functions.mp4

03:28

10. Numpy Saving and Loading Data.mp4

01:45

11. Pandas Overview.mp4

06:03

12. Pandas Read Data.mp4

04:57

13. Pandas Basic Exploration.mp4

07:51

14. Pandas at and iat.mp4

01:35

15. Pandas Reshaping Data.mp4

06:03

16. Pandas Subsetting.mp4

05:05

17. Pandas Summarize Data.mp4

03:47

18. Pandas Groupby.mp4

04:00

19. Pandas Missing Data.mp4

10:24

20. Pandas and Ploting.mp4

01:56

21. Matplotlib Documentation.mp4

02:01

22. Matplotlib Low Level.mp4

06:17

23. Matplotlib More on Subplots.mp4

02:41

24. Matplotlib Color, Linesytle, and Markers.mp4

06:31

25. Matplotlib Axis Labels.mp4

02:52

26. Matplotlib Saving Plots.mp4

01:39

27. Seaborn Documentation.mp4

01:36

28. Seaborn and Pandas.mp4

05:06

18. Machine Learning Engineer - Case Study - Machine Learning with Python

1. Python for Machine Learning.mp4

23:52

2. Machine Learning Considerations.mp4

13:11

3. Evaluating Supervised Models.mp4

16:48

4. Heuristic Modeling.mp4

03:39

5. Heuristic Modeling in Python.mp4

12:43

6. Model Training Process.mp4

10:43

7. Linear Regression.mp4

13:28

8. Linear Regression in Python.mp4

13:50

9. Logistic Regression.mp4

10:53

10. Logistic Regression in Python.mp4

10:13

11. Classification and Regression Tree (CART) Models.mp4

09:49

12. Decision Trees in Python.mp4

04:45

13. Ensemble Models.mp4

10:00

14. Random Forests and XGBoost in Python.mp4

07:21

15. Feature Engineering and Unsupervised Learning.mp4

09:35

16. Feature Engineering in Python.mp4

11:28

17. ssi - Deep Learning.mp4

09:46

18. ssi - Deep Learning Useful Applications and APIs.mp4

08:11

19. ssi - Deploying an ML Model in Production.mp4

07:08

20. ssi- Packaging and Deploying an ML Model With Docker.mp4

17:51

21. Section Closeout.mp4

03:53

22. Assignment Update resume and portfolio.html

Description

A complete survey of all core skills required on the job

What You'll Learn?

Learn all necessary core skills for Data Analysis, Data Science, and Machine Learning
Understand the first principles of data science and why it is so popular and important
Learn how to use, from scratch, Python, R, SQL, Tableau, and MS Excel for data science
Learn about a broad range of data science and machine learning libraries and resources
Build and host a personal resume and portfolio of data science projects using GitHub Pages
Learn about key supporting skills like Git/version-control, Kaggle, Databases, Command Line tools, and much more!
Learn how to setup development environments from scratch in R and Python
Learn about important related technologies like cloud, docker, and web development,
Learn to deploy a machine learning model using docker

Who is this for?

Those who feels like they don't know where to start with data science and machine learning

Those tired of courses that don't show the entire picture of data science and leave them asking 'now what?'

Those interested in starting a journey into the data science and machine learning career field.

For those wanting to super-charge an existing skill set with the latest techniques and tools.

More details

Description
This is an ambitious course. The goal here is simple: Only teach what you need to know for day 1 of your first data science job. No fluff, nothing out of context, no topics that are not relevant to real world applications. We will cover EVERY core topic and tool required for those new to data science: Python, R, SQL, Useful Math/Stats/Algorithms, Tableau, and Excel in depth. The course will cover skills that align with three different job types:
- Data Analyst
- General Data Scientist
- Machine Learning Engineer
You can expect to learn from first principles the foundational topics and tools used in practice today. We will avoid topics that are not useful or are simply too advanced when starting out. Your journey will be guided by the Data Science Road Map, a collection of the best resources gathered through years of experience by the instructor.
In addition, we will survey every important technology required on the job including GitHub, Kaggle, the basics of cloud, web development and docker. With over 200 videos, readings, and assignments, you can be sure you will be well prepared to join the data community.
If you are just getting started or want to fill in some of your knowledge gaps this course is for you!
Who this course is for:
Those who feels like they don't know where to start with data science and machine learning
Those tired of courses that don't show the entire picture of data science and leave them asking 'now what?'
Those interested in starting a journey into the data science and machine learning career field.
For those wanting to super-charge an existing skill set with the latest techniques and tools.

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Experienced Data Scientist with a background of developing and deploying operational data products in Industry and the Government. Worked as a lead data scientist with Army Cyber Command and is an Assistant Professor at the United States Military Academy at West Point. Strong engineering professional with a Master of Science focused in Industrial Engineering from the University of Washington and Ph.D. from Stanford's Decision and Risk Analysis program where he studied AI applied to cybersecurity.Was also named LinkedIn's 'Top Voice' for Data Analytics and has founded a venture capital backed startup.

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