Programming for Data Science Online Training

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Jonathan Barrios

20:02:35

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1. Explore Data Science Domains and Roles

1. Explore Data Science Domains and Roles .mp4

02:50

2. What is Data Science .mp4

12:39

3. Data Science Tools .mp4

11:37

4. Data Science Development Environments .mp4

09:59

5. What is Anaconda .mp4

06:21

6. Data Science Roles .mp4

05:04

7. The Data Science Roadmap .mp4

05:12

2. Access the Command Line for Data Science

1. Introduction .mp4

06:00

2. What is a command-line, terminal, and Shell .mp4

12:13

3. macOS Terminal, Git for Windows, and Linux Emulators .mp4

09:31

4. Basic Linux Commands .mp4

13:30

5. Create Projects and Workflows .mp4

11:51

3. Set Up a Data Science Development Environment

1. Introduction .mp4

04:04

2. Install Anaconda macOS .mp4

05:18

3. Install Anaconda Windows .mp4

04:29

4. Virtual Environments with Conda .mp4

05:28

5. Install Jupyter Notebook .mp4

08:38

6. Starting a Jupyter Notebook and Session .mp4

07:02

7. Closing a Jupyter Notebook Session .mp4

02:36

8. Explore Visual Code for Data Science .mp4

05:49

4. Explore Python Data Types for Data Science

1. Introduction -3.mp4

03:00

2. Primitive And Non-Primitive Data Types, Part 1 Conda Environment and GitHub .mp4

05:39

3. Primitive And Non-Primitive Data Types, Part 2 Data Types in Jupyter Notebook .mp4

11:14

4. Numbers Integers and Floats .mp4

05:51

5. Text Strings and Bools .mp4

06:22

6. Collections Lists .mp4

05:23

7. Collections Dictionaries .mp4

07:25

8. Collections Tuples, and Sets .mp4

08:20

5. Explore Strings and Sequences for Data Science

1. Introduction .mp4

04:35

2. Working with Variables .mp4

08:04

3. Leaving Comments .mp4

05:04

4. Working with Strings .mp4

07:47

5. String Formatting .mp4

05:35

6. Indexing .mp4

05:31

7. Slicing .mp4

06:48

6. Explore Math Operators and LaTex for Data Science

1. Introduction .mp4

04:40

2. Python and Math .mp4

08:14

3. Math Operators .mp4

11:58

4. Boolean Values .mp4

05:33

5. Built-in Python Functions .mp4

06:09

6. Scientific Notation .mp4

04:35

7. LaTex for Equations and Formulas .mp4

05:18

7. Write Reusable Python Functions for Data Science

1. Introduction .mp4

02:23

2. Comparison and Logical Operators .mp4

10:55

3. Writing Functions .mp4

12:21

4. If statements and Functions .mp4

09:29

5. Understanding Functions .mp4

10:37

6. Pseudocode .mp4

05:17

7. Asking for Input .mp4

04:52

8. Write Loops to Automate Tasks for Data Science

1. Introduction - Loops to Automate Tasks .mp4

02:45

2. Functions Review .mp4

09:11

3. if Statements Part 1 .mp4

08:31

4. if Statements Part 2 .mp4

09:18

5. for Loops .mp4

09:46

6. while Loops .mp4

07:40

7. Challenge .mp4

11:27

9. Use Python Built-In Methods for Data Science

1. Introduction Python Built-in Methods .mp4

03:33

2. List Review .mp4

13:37

3. List Methods .mp4

11:36

4. Dictionary Review .mp4

06:28

5. Dictionary Methods .mp4

06:28

6. Numpy and Pandas .mp4

07:13

10. Write Code using OOP Concepts for Data Science

1. Introduction .mp4

05:01

2. Programming Styles .mp4

13:05

3. Python Class Objects .mp4

17:18

4. EDA Dimensions .mp4

06:56

5. EDA Summary Statistics .mp4

08:24

6. EDA Complete with Histograms .mp4

06:45

11. Wrangling Data with Pandas for Data Science

1. Introduction .mp4

05:43

2. What is Pandas Part 1 .mp4

08:34

3. What is Pandas Part 2 .mp4

09:40

4. EDA (Exploratory Data Analysis) .mp4

09:10

5. Clean and Manipulate Data .mp4

07:53

6. Data Visualization with Pandas (it does that also!) .mp4

08:24

12. Work with Arrays Using Numpy Data Science Library

1. Introduction -3.mp4

03:51

2. What is Numpy .mp4

08:04

3. Numpy Vs Pandas .mp4

09:39

4. Creating and Manipulating Arrays .mp4

10:14

5. Array Operations, Array Methods and Functions .mp4

09:29

13. Visualizing Data with Matplotlib for Data Science

1. Introduction .mp4

03:01

2. What is Matplotlib .mp4

13:45

3. Fields in the dataset from Kaggle .mp4

14:50

4. Customizing Plots .mp4

09:38

14. Visualize Data with Seaborn for Data Science

1. Introduction -3.mp4

04:12

2. Matplotlib vs Seaborn .mp4

12:55

3. Plotting with Seaborn .mp4

13:08

4. Customizing Plots .mp4

07:47

5. Real-world Notebook .mp4

03:05

15. Explore Web Scraping Fundamentals for Data Science

1. Introduction .mp4

02:49

2. How the Internet Works .mp4

03:49

3. Visual Studio Code .mp4

08:26

4. HTML .mp4

09:03

5. CSS .mp4

06:05

6. Web Scraping with BeautifulSoup .mp4

10:51

16. Collect Web Data with Python and BeautifulSoup

1. Introduction .mp4

04:00

2. What is BeautifulSoup .mp4

03:08

3. The find() Method Part 1 .mp4

09:15

4. The find() Method Part 2 .mp4

15:25

5. The find all() Method Part 1 .mp4

10:09

6. The find all() Method Part 2 .mp4

09:14

17. Use GitHub Repositories for Data Science

1. Introduction -2.mp4

03:32

2. What is Git .mp4

08:55

3. What is GitHub .mp4

06:50

4. Create an Online Repo and Push Your Code to GitHub .mp4

08:36

5. Hosting Datasets for use in Jupyter Notebook .mp4

10:19

6. Challenge .mp4

04:04

18. Analyze Core Data Structures for Data Science

1. Introduction .mp4

04:54

2. What are Data Structures .mp4

08:17

3. Python Basic Data Structure Limitations .mp4

11:08

4. Data Structures Deep Dive .mp4

09:52

5. Social Network Analysis Use Case .mp4

10:44

19. Evaluate Complexity and Memory for Data Science

1. Introduction - Programming for Data Science CBT Nuggets-3.mp4

06:01

2. Complexity Analysis and Memory .mp4

12:11

3. Algorithm Comparison .mp4

09:44

4. Pandas Data Types .mp4

12:22

20. Apply Big O Notation Concepts for Data Science

1. Introduction .mp4

04:32

2. Big O Notation .mp4

03:46

3. Big O Notation and Time Complexity Visualization .mp4

11:26

4. Quadratic time .mp4

07:59

5. Factorial time .mp4

10:51

6. Coffee Shop Complexity .mp4

02:42

21. Explore R Fundamentals for Data Science

1. Introduction -3.mp4

09:03

2. What is R and Why Should I Learn it in 2023 .mp4

07:24

3. Getting Started with R and Google Colab .mp4

11:39

4. R Data Types .mp4

14:25

22. Implement and Compare R Data Structures

1. Introduction .mp4

04:21

2. R and Python Data Structures Part 1 Vectors .mp4

06:42

3. R and Python Data Structures Part 2 Arrays and Lists .mp4

07:17

4. R and Python Data Structures Part 3 Data Frames .mp4

05:43

5. Operations and Calculations .mp4

05:25

6. Matrix Calculations .mp4

06:13

7. Data Exploration .mp4

05:34

23. Perform EDA with R and Python for Data Science

1. Introduction .mp4

01:19

2. Load and Prepare the Dataset (EDA light) .mp4

09:08

3. Perform Exploratory Data Analysis (EDA) Part II .mp4

18:39

4. Perform Exploratory Data Analysis (EDA) Part I .mp4

09:41

5. Challenge .mp4

06:11

24. Explore AI Language Models and OpenAIs ChatGPT

1. Introduction.mp4

04:05

2. What is AI.mp4

06:47

3. OpenAI GPT-3 Language Models.mp4

06:01

4. What is ChatGPT and How Does it Work Under the Hood.mp4

03:35

5. Prompts and Completions.mp4

16:29

25. Query OpenAIs Language Model API with Googles Colab

1. Introduction.mp4

03:35

2. Bare Bones Completion.mp4

09:03

3. API Authentication.mp4

05:10

4. Creating a Completion.mp4

10:13

5. Time Complexity.mp4

07:15

6. Bonus Use Case White Paper Summarization.mp4

04:47

26. Create an AI Powered Web App with OpenAI, Streamlit

1. Introduction .mp4

04:09

2. What is Streamlit .mp4

06:34

3. What is Streamlit Community Cloud .mp4

03:15

4. Designing an AI Web App .mp4

04:36

5. HungryBear Non-production Code .mp4

10:33

6. HungryBear Production Code Part 1 .mp4

06:44

7. HungryBear Production Code Part 2 .mp4

12:47

Description

This intermediate Programming for Data Science training prepares learners to write code that makes sense of unstructured sets from multiple channels and sources and processes information you need, how you need it.

Coding and programming is fundamental to data science. If you want a career in data science, you have to plan on learning at least one or two programming languages, or else prepare yourself for a job hemmed in and restricted by whatever programs you happen to get your hands on.

More details

When you learn programming for data science, you unlock the power of making your data do exactly what you'd like it to do for you. Without programming, your results and findings are dependent on someone else's program and code â€” unlock your own future in data science by learning a programming language.

Once you're done with this Programming for Data Science training, you'll know how to write code that makes sense of unstructured sets from multiple channels and sources and processes information you need, how you need it.

For anyone who leads an IT team, this Data Science training can be used to onboard new data analysts, curated into individual or team training plans, or as a Data Science reference resource.

Programming for Data Science: What You Need to Know

This Programming for Data Science training has videos that cover topics including:

Writing reusable Python functions for data science
Writing Python code using object-oriented programming (OOP)
Wrangling data with Numpy and Pandas
Visualizing data with Matplotlib and Seaborn

Who Should Take Programming for Data Science Training?

This Programming for Data Science training is considered associate-level Data Science training, which means it was designed for data analysts and data scientists. This data science skills course is designed for data analysts with three to five years of experience with data science.

New or aspiring data analysts. Brand new data analysts should get started with a course like this that familiarizes them with all the programming language options that are out there. Start your career off with a primer in how analysis becomes more useful and faster with the right coding languages, and get started writing in them.

Experienced data analysts. If you've been working as a data analyst for several years and haven't learned a programming language yet, this course can help you understand why it's important and which one would be the right fit for you. Learning a coding language isn't as daunting as you might think â€” try out this course and see how to incorporate programming into your data science.

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Data Science

Jonathan Barrios

Instructor's Courses

"Helping aspiring data professionals learn about data and seeing them succeed is one of my greatest passions as a trainer. I love to learn, share my knowledge, and help others succeed—this is why I am passionate about being a trainer at CBT Nuggets."

Jonathan started his career as a full-stack developer and quickly became interested in combining his online education experience with his data science and machine learning knowledge. Jonathan has been a programmer, data analytics instructor, and curriculum writer for several leading online education platforms is excited to share his skills and education experience with aspiring data practitioners at CBT Nuggets.

Certifications: None

Areas of expertise: Full-stack software development, data analytics, data science, machine learning, and cloud technologies such as AWS and Google Cloud. HTML, CSS, JavaScript, PHP, Python, SQL, NoSQL, and frameworks/libraries such as Laravel, Vue, Tailwind, React, Gatsby, Django, NumPy, pandas, Matplotlilb, Scrappy, BeautifulSoup, SciPy, Seaborn, Plotly, Scikit-learn, Tensorflow, and PySpark.

CBT Nuggets

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CBT Nuggets is renowned for providing innovative training that's informative, meaningful, and engaging. We provide a variety of training, primarily in IT, project management, and office productivity topics. Our comprehensive library contains thousands of training videos ranging from Cisco networking to Microsoft Word. Whether you want to pass a certification exam, increase your skills, or simply learn new things, we've got you covered! All of our training is delivered through high-quality online streaming video. Subscribers can train 24 hours a day, seven days a week, from the convenience of a computer or mobile device. CBT Nuggets trainers are the rock stars of training, renowned for their expertise, industry-wide credibility, and engaging personalities. They enable CBT Nuggets to deliver accurate, up-to-date training, using a laid-back whiteboard presentation style. There are no scripts, EVER. Our trainers love to teach, and it shows! CEO and founder Dan Charbonneau was a Microsoft trainer when he began recording CBT Nuggets' very first training videos back in the 1990s. He wanted to help provide large organizations, small teams and individuals with comprehensive and budget-conscious training, and he realized it couldn't be done in a classroom. From the CBT Nuggets World Headquarters in Eugene, Oregon, Dan and his team promise each video will be informative, comprehensive, accurate, and fun to watch.