Roadmap to Leveraging Generative AI in Data Science

Welcome to Data Science Dojo's weekly newsletter, The Data-Driven Dispatch!

Beyond the hype, generative AI is truly transforming data science. How is it doing this? That's what we discuss in this newsletter.

We are just at the beginning of the generative AI revolution, but already, you can use AI tools to upload data files, trigger Python for regression and descriptive analysis, identify patterns within your data, and even generate visualizations—all without writing or understanding a single line of Python code.

Fascinating, isn't it?

Let's take a closer look at how generative AI can be harnessed for data science and envision the exciting possibilities that lie ahead.

Meet Ms. Emma and Ms. Noelle. Ms. Emma spent 40 hours analyzing a dataset of 20,000 customers, while the latter used ChatGPT to do the same in just 1 hour.

Want to know how?

Read this article to learn how to use ChatGPT for Exploratory Data Analysis (EDA), feature engineering, model selection, model training and evaluation, and much more.

Apart from this, new frameworks for large language models (LLMs) are emerging, like LangChain which facilitates the integration of diverse LangChain agents into programming environments, empowering the system to interpret human language and translate it into executable code. Consequently, data analysis becomes feasible without the necessity of writing code.

If you want to share how you leverage AI for your tasks, write us a blog.

Different Large Language Models enable different applications such as ChatGPT to understand your queries and offer a solution that might work wonders for you. However, when it comes to enterprises, they require custom LLM-powered applications that ensure data privacy and context related to their industry.

Do you want to learn how to build a custom LLM-powered application? In other words, a personal ChatGPT.

Join Raja Iqbal , Chief Data Scientist at Data Science Dojo for a session on how to build a custom LLM-powered application.

What will you Learn?

1. Foundation LLMs, vector databases, vector embeddings, semantic search, and orchestration frameworks
2. Common design patterns for building an LLM application on enterprise data
3. Trade-offs, challenges, and pitfalls faced while building these applications to solve real-world problems

If this is something that you find interesting, RSVP on our page right away.

Moreover, if it has piqued your interest and you want to dive deeper into building custom LLM applications, check out Large Language Models Bootcamp.

Adding a brisk of humor to your day! 😄👾 Check out this meme that's sure to bring a smile to your face!

Can you imagine developers referring to long documentation to write code? 😭

For more content related to data science and AI, follow us on Threads.

If you're using ChatGPT for your data-related tasks, it is very important for you to write the correct prompts. Here is an article that covers 60 prompts for data science tasks! You can save them as templates and use them to get your desired outcomes.

If you are looking for more tips and tricks related to AI, follow us on Instagram.

Learning is an ongoing process and it's never too late to start something new. If you're eager to embark on a data science career and are seeking expert direction, take a look at our Data Science Bootcamp.

If you want to network with data science experts, join our LinkedIn and Twitter communities.

🎉We trust that you had a delightful and enriching experience with us this week, leaving you more knowledgeable than before!🎉

✅ If you want to convert data into meaningful stories through apt visualizations, check out our online Introduction to Power BI training.

✅ If you wish to use Python effectively for data analysis, check out our Python for Data Science Bootcamp.

✅ Don't forget to Join Data Science Dojo's Community and stay tuned to our ongoing events all the time.

Until we meet again, take care!