Programming for Data Science

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.

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 — try out this course and see how to incorporate programming into your data science.

1. Explore Data Science Domains and Roles	10. Write Code using OOP Concepts for Data Science	11. Wrangling Data with Pandas for Data Science
12. Work with Arrays Using Numpy Data Science Library	13. Visualizing Data with Matplotlib for Data Science	14. Visualize Data with Seaborn for Data Science
15. Explore Web Scraping Fundamentals for Data Science	16. Collect Web Data with Python and BeautifulSoup	17. Use GitHub Repositories for Data Science
18. Analyze Core Data Structures for Data Science	19. Evaluate Complexity and Memory for Data Science	2. Access the Command Line for Data Science
20. Apply Big O Notation Concepts for Data Science	21. Explore R Fundamentals for Data Science	22. Implement and Compare R Data Structures
23. Perform EDA with R and Python for Data Science	24. Explore AI Language Models and OpenAI's ChatGPT	25. Query OpenAI's Language Model API with Google's Colab
26. Create an AI Powered Web App with OpenAI, Streamlit	3. Set Up a Data Science Development Environment	4. Explore Python Data Types for Data Science
5. Explore Strings and Sequences for Data Science	6. Explore Math Operators and LaTex for Data Science	7. Write Reusable Python Functions for Data Science
8. Write Loops to Automate Tasks for Data Science	9. Use Python Built-In Methods for Data Science

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