2023 CORE: Data Science and Machine Learning

سرفصل های دوره

A complete survey of all core skills required on the job

1. Introduction - First Principals

1. Introduction

2. Course Overview.html

3. Course Structure

4. Course Philosophy

5. First Principles - Who

6. First Principles - Why 13

7. First Principles - Why 23

8. First Principles - Why 33

9. Reading Assignment.html

10. First Principles - What

11. First Principles - What Data Analyst Example Product

12. First Principles - What Data Scientist Example Product

13. First Principles - What Machine Learning Engineer Example Product

14. First Principles - What Data & Sources

15. First Principles - What Kaggle Introduction

16. First Principles - How

17. Data Science Battle Station

18. Section Wrap Up

19. Assignments.html

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

1. Data Analyst Overview

2. Spreadsheets Overview

3. Introduction to MS Excel

4. Setting up MS Excel.html

5. Overview of MS Excel

6. Excel Templates

7. Workbook Overview

8. Protecting Workbooks

9. Sharing Workbooks

10. Operators

11. Built-in Functions

3. Data Analyst - Case Study - Intermediate Spreadsheets

1. Math - Summary Statistics

2. Calculating Summary Statistics from Scratch

3. Import a Text File

4. Data Tables

5. Summary Statistics on Tables

6. Summary Statistics Dashboard.html

7. Assignment Review

8. Importing Data - Intermediate

9. Lookups and Matches

10. Calculating Churn and Customer Lifetime Value

11. Financial Forecasting (Time Series)

12. Data Visualization Introduction

13. Data Visualization Excel

14. Dashboards Best Practices

15. Dashboards in Excel

16. Build a Dashboard.html

17. Assignment Solution

4. Data Analyst - Case Study - Advanced Spreadsheets

1. Importing Data - Power Query

2. Pivot Tables

3. Mathematical Modeling - Linear Programming

4. Solver - Linear Programming in Excel

5. Analysis Toolpack

6. Visual Basic for Applications (VBA) - Introduction

7. Spreadsheet Conclusion

8. Complete LinkedIn Excel Assessment.html

5. Data Analyst - Case Study - SQL Basics

1. SSI - databases

2. SQL Text Editor - Sublime

3. SQL Syntax

4. Introduction to SQLite Databases

5. SQLite Install

6. SQLite Database Creation

7. Basic SQL - SELECT, FROM, WHERE statements

8. Basic SQL - BETWEEN, LIKE statements

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

10. Basic SQL - ORDER BY, DISTINCT statements

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

1. Intermediate SQL - Aggregate Functions

2. Intermediate SQL - WITH and subqueries

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

4. Advanced SQL - Views

5. Connecting SQLite to Excel

6. Kaggle SQL Course.html

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

1. Introduction to Business Intelligence (BI)

2. Why Tableau

3. Installing Tableau Public

4. Tableau Overview

5. Tableau Data Types

6. Tableau Basic Viz

7. Tableau Filters

8. Connecting Tableau to OData Sources

9. Joining Data in Tableau

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

1. Tableau Intermediate Bar Charts

2. Tableau Dates

3. Tableau Visualizing Comparisons

4. Tableau Visualizing Distributions

5. Tableau Multiple Axis

6. Tableau Formating

7. Tableau Calculations and Parameters

8. Tableau Dashboards and Stories

9. Tableau Advanced Analysis

10. Sharing with Tableau Public

11. Tableau Desktop Pro Overview

12. Assignment Portfolio, and Resume Updates.html

9. Data Scientist - Case Study - Introduction to R

1. Introduction to the Data Scientist (Generalist)

2. Overview of R

3. Intro to CRAN and installing base R

4. Installing RStudio

5. Overview of RStudio

6. Calculations in Base R

7. Objects in Base R

8. Functions in Base R

9. The Basics of R Scripts

10. Base R Datasets

11. Base R Help and Plots

12. Installing R Packages - More on Plots and Objects

13. Atomic Vectors

14. Object Attributes

15. Matrix and Array Objects

16. Classes

17. Factors

18. Coercion

19. Lists

20. Data Frames

21. Loading and Saving Data Part 1

22. Loading and Saving Data Part 2

23. Selecting Values from Data Frames

24. Changing Values in Data Frames

25. Sub Setting Data Frames

26. Missing Values

27. More on Selecting Values

28. Programming Flow Controls

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

1. An Introduction to EDA

2. EDA Example on Kaggle

3. Expanding Summary Statistics - Location

4. Location Examples in R

5. Expanding Summary Statistics - Spread

6. Spread Examples in R

7. Important EDA Tools

8. Introduction to the Tidyverse and ggplot2

9. Tidyverse website

10. ggplot - Mapping Aesthetics

11. ggplot - Facets

12. ggplot - Multiple Geom

13. ggplot - Stat Transforms

14. ggplot - Position Adjustments

15. ggplot - Coord Systems

16. ggplot - Summary

17. ggplot - Gallery Book

18. R Object Names

19. dplyr - Overview

20. dplyr - Filter

21. dplyr - Arrange and Select

22. dplyr - Mutate

23. dplyr - Pipes, group_by, and summarise

24. stringr - Basics

25. stringr - Matching

26. lubridate - Basics

27. Intro to Markdown

28. Intro to RMarkdown

29. Quick Overview of Notebooks

30. EDA Assignment.html

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

1. Intro to Useful Statistics

2. Useful Probability 1 of 2

3. Useful Probability 2 of 2

4. Distributions Using R

5. Useful Frequentist Statistics

6. Useful Frequentists Hypothesis Testing

7. Hypothesis Testing in R

8. AB Testing

9. AB Testing in R

10. Bootstrap Introduction

11. Bootstrap in R

12. Useful Bayesian Statistics

13. Bayesian Stats in R (beta-binomial)

14. Bayesian Stats in R (Thompson Sampling)

15. Useful Simulations - Monte Carlo

16. Monte Carlo Simulations in R

17. Useful Regression Modeling Introduction

18. Simple Linear Regression in R

19. Useful Multiple Regression

20. Multiple Regression in R

21. Regression Issues and Concerns

22. Useful Time Series Modeling

23. Assignment Update resume and portfolio.html

24. Time Series Modeling in R

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

1. SSI - Web Development

2. SSI - Basic Web Development Example

3. Web Development with Git in RStudio 1 of 2

4. Web Development with Git in RStudio 2 of 2

5. Reading Assignment Git.html

6. Hosting with Github Pages

7. Using the blogdown R package to create websites

8. Introduction to Shiny

9. Hello World Shiny App

10. Closer Look at Shiny

11. Hosting Shiny and shinyapps.io

12. shinyapps.io and The shinydashboard Package

13. Data Scientist Section Closeout

1. Data Scientist Section Closeout

2. Assignment Update resume and projects.html

14. Machine Learning Engineer - Case Study - Introduction

1. Job Overview

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

1. SSI - Intro to the Cloud

2. SSI - The AWS Cloud Console

3. SSI - The Command Line Interface

4. Command Line Demo

5. SSI - Intro to Docker

6. SSI - Docker Continued

7. SSI - Docker Demo

8. Project Jupyter Docker Stack

9. Using Docker on a Cloud Server part 1

10. Using Docker on a Cloud Server part 2

11. Other Python Development Environments

12. Project Jupyter

16. Machine Learning Engineer - Case Study - Basic Python

1. Python Overview

2. Python in Jupyterlab

3. Basic Notebook Cell Operations

4. Basic Math Operations

5. Basic Data Types

6. Basic Variables

7. Basic Built-in Functions

8. Basic Comparison Operators

9. Basic Boolean Operators

10. Combining Boolean and Comparison Operators

11. Basic Elements of Control Flow

12. Control Flow Continued

13. Importing Modules

14. Functions

15. Local vs. Global Variables

16. Lists in Depth

17. Lists in Depth Continued

18. Additive Operators

19. Methods on Lists

20. Dictionaries

21. Classes and Methods

22. Interacting with Files

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

1. Python for Data Science

2. Useful Matrix Operations

3. Numpy for Matrix Operations

4. Numpy Indexing and Slicing

5. Numpy Boolean Indexing

6. Numpy Reshape and Transpose

7. Numpy Pseudorandom Numbers

8. Numpy Unary and Binary Functions

9. Numpy Aggregate Functions

10. Numpy Saving and Loading Data

11. Pandas Overview

12. Pandas Read Data

13. Pandas Basic Exploration

14. Pandas at and iat

15. Pandas Reshaping Data

16. Pandas Subsetting

17. Pandas Summarize Data

18. Pandas Groupby

19. Pandas Missing Data

20. Pandas and Ploting

21. Matplotlib Documentation

22. Matplotlib Low Level

23. Matplotlib More on Subplots

24. Matplotlib Color, Linesytle, and Markers

25. Matplotlib Axis Labels

26. Matplotlib Saving Plots

27. Seaborn Documentation

28. Seaborn and Pandas

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

1. Python for Machine Learning

2. Machine Learning Considerations

3. Evaluating Supervised Models

4. Heuristic Modeling

5. Heuristic Modeling in Python

6. Model Training Process

7. Linear Regression

8. Linear Regression in Python

9. Logistic Regression

10. Logistic Regression in Python

11. Classification and Regression Tree (CART) Models

12. Decision Trees in Python

13. Ensemble Models

14. Random Forests and XGBoost in Python

15. Feature Engineering and Unsupervised Learning

16. Feature Engineering in Python

17. ssi - Deep Learning

18. ssi - Deep Learning Useful Applications and APIs

19. ssi - Deploying an ML Model in Production

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

21. Section Closeout

22. Assignment Update resume and portfolio.html

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تولید کننده: Udemy-Training