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CBT Nuggets - Programming for Data Science

CBT Nuggets - Programming for Data Science

Size
12.2 GB
Seeders
3
Leechers
3
Files
99
Category
Added
at 8:43pm GMT+1
Infohash
00f81955642021cc3f0d7b904e6f1124f462308f
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Description
Programming for Data Science - CBT Nuggets

English | Size: 12.25 GB
Genre: eLearning





Programming for Data Science Online Training
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.

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 — 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.

File list
  • CBT Nuggets - Programming for Data Science
  • 1. Explore Data Science Domains and Roles/1. Explore Data Science Domains and Roles .mp4 24.1 MB
  • 1. Explore Data Science Domains and Roles/2. What is Data Science .mp4 95.4 MB
  • 1. Explore Data Science Domains and Roles/3. Data Science Tools .mp4 102.3 MB
  • 1. Explore Data Science Domains and Roles/4. Data Science Development Environments .mp4 83.6 MB
  • 1. Explore Data Science Domains and Roles/5. What is Anaconda .mp4 48.4 MB
  • 1. Explore Data Science Domains and Roles/6. Data Science Roles .mp4 43.4 MB
  • 1. Explore Data Science Domains and Roles/7. The Data Science Roadmap .mp4 61.1 MB
  • 10. Write Code using OOP Concepts for Data Science/1. Introduction .mp4 125.1 MB
  • 10. Write Code using OOP Concepts for Data Science/2. Programming Styles .mp4 123.8 MB
  • 10. Write Code using OOP Concepts for Data Science/3. Python Class Objects .mp4 169.4 MB
  • 10. Write Code using OOP Concepts for Data Science/4. EDA Dimensions .mp4 66.5 MB
  • 10. Write Code using OOP Concepts for Data Science/5. EDA Summary Statistics .mp4 74.5 MB
  • 10. Write Code using OOP Concepts for Data Science/6. EDA Complete with Histograms .mp4 60.2 MB
  • 11. Wrangling Data with Pandas for Data Science/1. Introduction .mp4 75.4 MB
  • 11. Wrangling Data with Pandas for Data Science/2. What is Pandas Part 1 .mp4 79.1 MB
  • 11. Wrangling Data with Pandas for Data Science/3. What is Pandas Part 2 .mp4 71.6 MB
  • 11. Wrangling Data with Pandas for Data Science/4. EDA (Exploratory Data Analysis) .mp4 85.6 MB
  • 11. Wrangling Data with Pandas for Data Science/5. Clean and Manipulate Data .mp4 96.4 MB
  • 11. Wrangling Data with Pandas for Data Science/6. Data Visualization with Pandas (it does that also!) .mp4 103.3 MB
  • 12. Work with Arrays Using Numpy Data Science Library/1. Introduction -3.mp4 99.7 MB
  • 12. Work with Arrays Using Numpy Data Science Library/2. What is Numpy .mp4 52.9 MB
  • 12. Work with Arrays Using Numpy Data Science Library/3. Numpy Vs Pandas .mp4 95 MB
  • 12. Work with Arrays Using Numpy Data Science Library/4. Creating and Manipulating Arrays .mp4 63.2 MB
  • 12. Work with Arrays Using Numpy Data Science Library/5. Array Operations, Array Methods and Functions .mp4 67.6 MB
  • 13. Visualizing Data with Matplotlib for Data Science/1. Introduction .mp4 36.1 MB
  • 13. Visualizing Data with Matplotlib for Data Science/2. What is Matplotlib .mp4 161.5 MB
  • 13. Visualizing Data with Matplotlib for Data Science/3. Fields in the dataset from Kaggle .mp4 155.4 MB
  • 13. Visualizing Data with Matplotlib for Data Science/4. Customizing Plots .mp4 80 MB
  • 14. Visualize Data with Seaborn for Data Science/1. Introduction -3.mp4 69.4 MB
  • 14. Visualize Data with Seaborn for Data Science/2. Matplotlib vs Seaborn .mp4 149.4 MB
  • 14. Visualize Data with Seaborn for Data Science/3. Plotting with Seaborn .mp4 92.9 MB
  • 14. Visualize Data with Seaborn for Data Science/4. Customizing Plots .mp4 82.1 MB
  • 14. Visualize Data with Seaborn for Data Science/5. Real-world Notebook .mp4 22 MB
  • 15. Explore Web Scraping Fundamentals for Data Science/1. Introduction .mp4 26.9 MB
  • 15. Explore Web Scraping Fundamentals for Data Science/2. How the Internet Works .mp4 39.2 MB
  • 15. Explore Web Scraping Fundamentals for Data Science/3. Visual Studio Code .mp4 97 MB
  • 15. Explore Web Scraping Fundamentals for Data Science/4. HTML .mp4 45.7 MB
  • 15. Explore Web Scraping Fundamentals for Data Science/5. CSS .mp4 53.3 MB
  • 15. Explore Web Scraping Fundamentals for Data Science/6. Web Scraping with BeautifulSoup .mp4 148.9 MB
  • 16. Collect Web Data with Python and BeautifulSoup/1. Introduction .mp4 55.1 MB
  • 16. Collect Web Data with Python and BeautifulSoup/2. What is BeautifulSoup .mp4 34 MB
  • 16. Collect Web Data with Python and BeautifulSoup/3. The find() Method Part 1 .mp4 91.8 MB
  • 16. Collect Web Data with Python and BeautifulSoup/4. The find() Method Part 2 .mp4 129.4 MB
  • 16. Collect Web Data with Python and BeautifulSoup/5. The find_all() Method Part 1 .mp4 135.7 MB
  • 16. Collect Web Data with Python and BeautifulSoup/6. The find_all() Method Part 2 .mp4 82.3 MB
  • 17. Use GitHub Repositories for Data Science/1. Introduction -2.mp4 38.4 MB
  • 17. Use GitHub Repositories for Data Science/2. What is Git .mp4 61.2 MB
  • 17. Use GitHub Repositories for Data Science/3. What is GitHub .mp4 62 MB
  • 17. Use GitHub Repositories for Data Science/4. Create an Online Repo and Push Your Code to GitHub .mp4 84.8 MB
  • 17. Use GitHub Repositories for Data Science/5. Hosting Datasets for use in Jupyter Notebook .mp4 94 MB
  • 17. Use GitHub Repositories for Data Science/6. Challenge .mp4 28.9 MB
  • 18. Analyze Core Data Structures for Data Science/1. Introduction .mp4 133 MB
  • 18. Analyze Core Data Structures for Data Science/2. What are Data Structures .mp4 85.2 MB
  • 18. Analyze Core Data Structures for Data Science/3. Python Basic Data Structure Limitations .mp4 124.7 MB
  • 18. Analyze Core Data Structures for Data Science/4. Data Structures Deep Dive .mp4 141.5 MB
  • 18. Analyze Core Data Structures for Data Science/5. Social Network Analysis Use Case .mp4 118.3 MB
  • 19. Evaluate Complexity and Memory for Data Science/1. Introduction - Programming for Data Science CBT Nuggets-3.mp4 144 MB
  • 19. Evaluate Complexity and Memory for Data Science/2. Complexity Analysis and Memory .mp4 93.5 MB
  • 19. Evaluate Complexity and Memory for Data Science/3. Algorithm Comparison .mp4 121.9 MB
  • 19. Evaluate Complexity and Memory for Data Science/4. Pandas Data Types .mp4 210.6 MB
  • 2. Access the Command Line for Data Science/1. Introduction .mp4 170.8 MB
  • 2. Access the Command Line for Data Science/2. What is a command-line, terminal, and Shell .mp4 137.4 MB
  • 2. Access the Command Line for Data Science/3. macOS Terminal, Git for Windows, and Linux Emulators .mp4 80.9 MB
  • 2. Access the Command Line for Data Science/4. Basic Linux Commands .mp4 105.6 MB
  • 2. Access the Command Line for Data Science/5. Create Projects and Workflows .mp4 83 MB
  • 20. Apply Big O Notation Concepts for Data Science/1. Introduction .mp4 131.8 MB
  • 20. Apply Big O Notation Concepts for Data Science/2. Big O Notation .mp4 57.4 MB
  • 20. Apply Big O Notation Concepts for Data Science/3. Big O Notation and Time Complexity Visualization .mp4 57.1 MB
  • 20. Apply Big O Notation Concepts for Data Science/4. Quadratic time .mp4 38.2 MB
  • 20. Apply Big O Notation Concepts for Data Science/5. Factorial time .mp4 132.6 MB
  • 20. Apply Big O Notation Concepts for Data Science/6. Coffee Shop Complexity .mp4 109.6 MB
  • 21. Explore R Fundamentals for Data Science/1. Introduction -3.mp4 193.7 MB
  • 21. Explore R Fundamentals for Data Science/2. What is R and Why Should I Learn it in 2023 .mp4 167.8 MB
  • 21. Explore R Fundamentals for Data Science/3. Getting Started with R and Google Colab .mp4 176.8 MB
  • 21. Explore R Fundamentals for Data Science/4. R Data Types .mp4 101 MB
  • 22. Implement and Compare R Data Structures/1. Introduction .mp4 117.7 MB
  • 22. Implement and Compare R Data Structures/2. R and Python Data Structures Part 1 Vectors .mp4 57.2 MB
  • 22. Implement and Compare R Data Structures/3. R and Python Data Structures Part 2 Arrays and Lists .mp4 40.5 MB
  • 22. Implement and Compare R Data Structures/4. R and Python Data Structures Part 3 Data Frames .mp4 30.8 MB
  • 22. Implement and Compare R Data Structures/5. Operations and Calculations .mp4 59.3 MB
  • 22. Implement and Compare R Data Structures/6. Matrix Calculations .mp4 76.2 MB
  • 22. Implement and Compare R Data Structures/7. Data Exploration .mp4 133.4 MB
  • 23. Perform EDA with R and Python for Data Science/1. Introduction .mp4 19.1 MB
  • 23. Perform EDA with R and Python for Data Science/2. Load and Prepare the Dataset (EDA light) .mp4 104.5 MB
  • 23. Perform EDA with R and Python for Data Science/3. Perform Exploratory Data Analysis (EDA) Part II .mp4 116.1 MB
  • 23. Perform EDA with R and Python for Data Science/4. Perform Exploratory Data Analysis (EDA) Part I .mp4 76.3 MB
  • 23. Perform EDA with R and Python for Data Science/5. Challenge .mp4 74.7 MB
  • 24. Explore AI Language Models and OpenAI's ChatGPT/1. Introduction.mp4 71.6 MB
  • 24. Explore AI Language Models and OpenAI's ChatGPT/2. What is AI.mp4 131.1 MB
  • 24. Explore AI Language Models and OpenAI's ChatGPT/3. OpenAI GPT-3 Language Models.mp4 67.4 MB
  • 24. Explore AI Language Models and OpenAI's ChatGPT/4. What is ChatGPT and How Does it Work Under the Hood.mp4 35.4 MB
  • 24. Explore AI Language Models and OpenAI's ChatGPT/5. Prompts and Completions.mp4 185.4 MB
  • 25. Query OpenAI's Language Model API with Google's Colab/1. Introduction.mp4 82.9 MB
  • 25. Query OpenAI's Language Model API with Google's Colab/2. Bare Bones Completion.mp4 102.6 MB
  • 25. Query OpenAI's Language Model API with Google's Colab/3. API Authentication.mp4 45.3 MB
  • 25. Query OpenAI's Language Model API with Google's Colab/4. Creating a Completion.mp4 114.6 MB
  • 25. Query OpenAI's Language Model API with Google's Colab/5. Time Complexity.mp4 71.8 MB
  • 25. Query OpenAI's Language Model API with Google's Colab/6. Bonus Use Case White Paper Summarization.mp4 78.8 MB
  • 26. Create an AI Powered Web App with OpenAI, Streamlit/1. Introduction .mp4 98.3 MB

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