Course: ChatGPT For Data Analysis: Data Science Using Python & AI
I. Composition (12–15 sentences) Chat GPT, short for Generative Pre-trained Transformer, is a family of powerful language models that generate human-like text from prompts. It leverages deep learning and transformer architectures to predict and produce coherent sentences, making it useful across many domains. In practical settings, Chat GPT can automate customer support, draft marketing copy, summarize research, and assist with educational tutoring. Developers often integrate Chat GPT via APIs, embedding conversational agents into web apps, chatbots, and data pipelines. In data science workflows, Chat GPT can help generate SQL queries, draft data-processing code, and propose analytical approaches. When combined with Python libraries such as pandas, sqlite3, and recordlinkage, it enables streamlined data cleaning, matching, and query automation. Users should be mindful of safety, privacy, and ethical considerations when deploying models, including validating outputs and sanitizing inputs. Choosing between free and paid versions depends on usage needs, feature requirements, and budget constraints. Jupyter Lab and Anaconda provide excellent environments for experimenting with Chat GPT-powered scripts and notebooks. Prompt engineering—crafting clear, contextual prompts—substantially improves response quality and relevance. Developers can control creativity via parameters like temperature and top-k sampling to balance determinism and novelty. Finally, iterative refinement, evaluation, and transparent reporting (for example, in APA style) help ensure that Chat GPT enhances productivity without compromising scientific rigor or user trust. II. Structured lecture sheet (semantic HTML)Lesson
Topic: Introduction to Chat GPT — concepts, practical use, and Python integration
Learning Outcomes
- Understand what Chat GPT (Generative Pre-trained Transformer) is and how it works.
- Identify common applications and practical use cases, especially in data processing with Python.
- Know basic safety, account, and cost considerations (free vs paid).
- Be able to craft effective prompts and apply prompt engineering principles.
- Run simple integrations: calling an API, summarization, SQL generation, and Jupyter Lab usage.
Conceptual Explanation
Chat GPT is a transformer-based language model trained on large text corpora to predict next tokens and generate coherent, context-aware responses. It encodes context across turns (messages) enabling multi-step dialogue and can be tuned via parameters (temperature, top-k) to control creativity and variability. Key features include natural language understanding, context awareness across interactions, and versatility for tasks such as summarization, question answering, code generation, and conversational agents.
Practical integration patterns: (1) API call to a hosted model (example: OpenAI Chat Completions); (2) using model outputs to generate SQL, code or summaries; (3) embedding the model in Jupyter Lab for iterative data work; (4) combining with pandas and sqlite3 to automate data processing tasks. Prompt engineering—clear instructions, context, examples, and explicit output format—greatly improves result quality. Always validate outputs, sanitize user inputs, and monitor for safety or bias.
| Parameter | Effect | When to use |
|---|---|---|
| temperature | Controls randomness (low → deterministic, high → creative) | Lower for factual answers, higher for creative writing |
| top_k | Restricts sampling to top-k tokens | Use to avoid improbable tokens and reduce hallucination |
Bilingual (EN → বাংলা)
EN: Chat GPT is a transformer-based language model that can generate text, summarize content, and assist with coding tasks.
বাংলা: Chat GPT একটি ট্রান্সফর্মার-ভিত্তিক ভাষা মডেল যা পাঠ্য সৃষ্টিতে, বিষয়বস্তু সংক্ষেপে এবং কোডিং সহায়তায় ব্যবহার করা যায়।
Answer
EN note: Use clear prompts and provide examples when asking the model to produce structured outputs (tables, SQL, code).
বাংলা নোট: যখন মডেলকে কাঠামোবদ্ধ আউটপুট (টেবিল, SQL, কোড) দিতে বলবেন, স্পষ্ট প্রম্পট দিন এবং উদাহরণ দেখান।
Important Notes
- Do not share API keys in public code. Use environment variables or secret managers.
- Always validate generated SQL/code before execution: automated outputs can contain errors or vulnerabilities.
- Respect privacy and copyright when sending data to third-party APIs.
- Monitor model outputs for hallucinations, unsafe content, or bias.
- Document versions, prompts, and parameters for reproducibility.
Reference examples: Python (openai or requests), pandas, sqlite3, recordlinkage, Jupyter Lab.
Exercises
Practice tasks and assessment items
True / False
- Chat GPT always provides perfectly accurate facts.
Answer
False — models can hallucinate; always verify.
- Lower temperature makes outputs more deterministic.
Answer
True — lower temperature favors high-probability tokens.
- You should hard-code API keys in notebooks for convenience.
Answer
False — use environment variables or secure vaults instead.
Fill in the Blanks
- To control creativity in generation, adjust the __________ parameter.
Answer
temperature
- To avoid low-probability tokens, use __________ sampling.
Answer
top-k
Multiple Choice (MCQ)
- Which library is commonly used to read CSV files in Python?
- requests
- pandas
- matplotlib
- scikit-learn
Answer
B — pandas
- Which test is suitable for checking normality in small samples?
- Levene's test
- Shapiro-Wilk test
- ANOVA
- Kruskal-Wallis
Answer
B — Shapiro-Wilk test
Matching
Match the item (left) to its purpose (right).
| Item | Purpose |
|---|---|
| A. recordlinkage | 1. Visualize distributions |
| B. seaborn | 2. Identify potential duplicate records |
| C. sqlite3 | 3. Run file-based SQL queries |
Answer
A → 2, B → 1, C → 3
Descriptive
Question: Describe a safe workflow to use Chat GPT for generating SQL queries and executing them against a local SQLite database.
Answer
- Collect user intent and sanitize inputs to prevent injection.
- Use prompt engineering to ask Chat GPT to generate parameterized SQL or to return only the WHERE clause.
- Validate and review the generated SQL programmatically (e.g., check for forbidden keywords) before execution.
- Create a temporary table name (UUID suffix) and load data via pandas .to_sql when necessary.
- Execute the SQL within a controlled environment (read-only or with limited privileges) and log queries/results.
- Return results to the user and archive the prompt, model version, and parameters for reproducibility.
Activities, Vocabulary, Homework, Practice Task
Activities
- Activity 1: Craft three prompts for Chat GPT to (a) summarize a research paragraph, (b) generate a pandas cleaning pipeline, (c) propose SQL to filter sales above a threshold.
- Activity 2: In Jupyter Lab, implement a safe execute_query function that uses a sanitized table name and logs operations.
Vocabulary
Prompt engineering Designing and refining inputs to a model to obtain desired outputs. Temperature Sampling parameter that controls randomness of generation. Top-k Sampling strategy that restricts choices to the top k tokens. Hallucination Model output that is plausible but factually incorrect.Homework
- Write a 200–300 word APA-style summary of a dataset analysis where Chat GPT assisted in prompt generation. Include the model parameters used and a note on validation.
- Implement the SQL execution pattern described in the lecture and demonstrate with a small pandas DataFrame.
Practice Task
Using a provided CSV (or simulated data), create a Jupyter notebook that:
- Loads and cleans the data with pandas.
- Uses a Chat GPT prompt (simulate the API call if no key) to generate a summary and a suggested analysis plan.
- Executes a simple aggregated SQL query via sqlite3 using a sanitized temporary table name.
- Produces a final APA-style table and short write-up (use the generate_apa_table approach).
Answer / Hints
Hints: Use pd.read_csv, df.dropna(), df.to_sql(temp_name, conn), pd.read_sql_query(query, conn). For prompt simulation, you may format the prompt and store the intended response as a text cell to avoid API usage.