Leveraging AI Tools for Your Data Science Journey
AI tools like ChatGPT and Gemini are supplemental. They won’t replace the need for dedicated learning and practice.
Hitting the Tutorial Wall
There’s a phase in the data science journey many experience – the “tutorial limbo.” Like me, you might polish off a comprehensive course (like the Udemy Complete Data Science Bootcamp in my case, back in November 2023) and grasp the core concepts. But then, what?
Balancing a full-time job or your studies with this new path can stretch your timeline. That’s exactly where I found myself. Thankfully, discovering the power of AI tools was a game-changer. They helped me leverage my existing knowledge and provided valuable guidance to move forward.
So this is my guide on how to use AI tools to be more effective and efficient.
Table of Contents
Build Your Foundation
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Conceptual Understanding: While AI tools can explain complex topics, solidify your grasp of core data science concepts first. Online courses on Coursera or Udemy, and Youtube tutorials are excellent resources.
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AI Assistance: Once you have a basic understanding, use Gemini or ChatGPT to clarify specific concepts. Ask questions about data cleaning techniques, statistical methods, or machine learning algorithms.
Example Prompts: “Explain the difference between supervised and unsupervised learning in simple terms”, “Can you tell me how apriori algorithm works?”
Exploring Tools and Techniques
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Researching Tools: Leverage AI assistants to research popular programming languages (Python, R), data visualisation libraries (Matplotlib, Seaborn), and database management systems (SQL).
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AI’s Role: You can use AI to generate code snippets or functions to understand the syntax and functionality. I always customize the code to fit my specific data format and then debug it thoroughly to ensure it works as intended.
Example Prompts: “Show me how to use LEFT JOIN in SQL”, “Can you provide a code snippet for creating a bar chart in Seaborn?”
Practice Makes Perfect
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Finding Datasets: Utilize online repositories like Kaggle and UCI Machine learning repository to find practice datasets relevant to your interests.
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AI-powered Practice: Use Gemini or ChatGPT to brainstorm initial data analysis steps for your chosen dataset. Ask them to suggest relevant visualisations or potential insights.
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Refine and Analyze: Don’t rely solely on AI suggestions. Use your data analysis knowledge to refine their recommendations and conduct your own analysis.
Example Prompts: “What kind of data cleaning might be necessary for this dataset?”, “Can you propose some visualisations to explore trends in this data?”
Sharpening Your Skills
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Communication is Key: Data analysts and data scientists need to effectively communicate insights. Use AI tools to practice explaining complex concepts in a clear and concise way.
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AI as a Mock Audience: Provide data analysis findings to your AI assistant and ask them to generate questions as if they were a non-technical audience member.
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Refine Your Explanations: Based on the AI’s “questions,” refine your explanations to ensure they are understandable for a broader audience.
Example Prompt: “I found a correlation between X and Y in the data. Help me craft a clear and concise explanation for someone unfamiliar with data analysis”
Remember:
- Focus on understanding core concepts.
- Develop a critical eye. Don’t blindly accept AI-generated code or explanations. Verify and understand them before applying them.
By strategically using AI tools alongside dedicated learning, you can accelerate your journey towards becoming a data analyst or data scientist.