Course (Part 364)

Course: Data Analysis with ChatGPT in Excel

Course Introduction — Short Composition

Welcome to the course on Conversational AI and ChatGPT Advanced Data Analysis, a practical program designed to bridge natural language interfaces and data-driven insight generation. Conversational AI enables machines to understand, process, and respond to human language via chatbots, virtual assistants, and voice interfaces that are increasingly embedded in daily workflows. Throughout this course we examine foundational concepts, common APIs, and real-world use cases spanning customer support, e-commerce, healthcare, education, and HR. You will learn how to preprocess messy data, perform exploratory data analysis, and extract descriptive statistics using conversational tools. The curriculum demonstrates how ChatGPT can assist with data cleaning, feature engineering, and generating human-readable reports from structured inputs. We also discuss the practical limits of models—context windows, accuracy considerations, and the importance of human validation. API integration is emphasized so you can connect models to databases, CRM systems, and analytics dashboards for real-time queries. Hands-on exercises will guide you through tasks like outlier detection, unit normalization, and duplicate removal. Special modules cover date/time handling, converting hours to decimals, and calculating ages precisely for application logic. By the end of the course you will be able to prototype conversational agents that query data, summarize trends, and support decision-making. Emphasis is placed on responsible use, maintaining data integrity, and designing fallback strategies for ambiguous user inputs. This course aims to make data analysis accessible through natural language while preparing you to combine AI capabilities with traditional data science rigor.

Lesson

Topic: Conversational AI & ChatGPT Advanced Data Analysis — Foundations, Preprocessing, EDA, and Integration

Learning Outcomes

• Explain core concepts of conversational AI and key use cases across industries.
• Apply preprocessing techniques (cleaning, deduplication, normalization) using ChatGPT-assisted workflows.
• Perform EDA tasks and extract descriptive statistics with conversational prompts and API integration.
• Integrate ChatGPT with external APIs to query, transform, and report on datasets securely.
• Identify limitations, validate outputs, and design fallback/error-handling strategies.

Conceptual Explanation

Conversational AI refers to systems using NLP and ML to engage users in human-like dialogue. Core components include intent detection, entity extraction, dialogue management, and backend integrations (APIs and databases). ChatGPT and similar LLMs provide a flexible interface for both natural language interactions and data analysis tasks: they can summarize datasets, suggest visualizations, and generate code snippets for common operations (e.g., Pandas operations, SQL queries). Preprocessing is an essential stage: standardize formats, remove duplicates, handle missing values, normalize units, trim whitespace, and correct capitalization and inverted names. Exploratory Data Analysis (EDA) with conversational tools lets analysts ask natural language questions (e.g., "show distribution of sales by month") and receive descriptive statistics, plots, or code templates. For production systems, integrate conversational agents via REST/GraphQL APIs, authenticate securely, and instrument logging and user confirmation flows. Understand model limits—token/context constraints, static knowledge cutoff, and occasional hallucinations—so outputs are always validated against authoritative sources. Finally, design conversational flows that include clarification prompts, error correction, and escalation to human agents when ambiguities or high-risk decisions arise.

Bilingual (EN → বাংলা)

English (EN): Conversational AI uses NLP and ML to enable natural language interactions; common applications include chatbots for customer support, virtual assistants for scheduling, and voice interfaces for device control.

বাংলা (Bangla): কথোপকথনমূলক এআই মানে হল NLP ও ML ব্যবহার করে প্রাকৃতিক ভাষায় মানুষের সঙ্গে মেশিনের কথা বলা; সাধারণ অ্যাপ্লিকেশনগুলোর মধ্যে আছে কাস্টমার সাপোর্টের চ্যাটবট, সূচি নির্ধারণের ভার্চুয়াল অ্যাসিস্ট্যান্ট এবং ডিভাইস নিয়ন্ত্রণের ভয়েস ইন্টারফেস।

Important Notes

• Always validate model-generated analytics against raw data or authoritative computations.
• Be explicit in prompts: include format expectations, units, time zones, and required outputs (e.g., CSV, chart code).
• Design confirmation flows for sensitive operations (payments, personal data updates).
• Log interactions and retain provenance for reproducibility and auditing.
• Be mindful of privacy/regulatory constraints (PII handling, data residency).

Exercises → True / False / Fill in the Blanks / MCQ / Matching / Descriptive / Creative

True / False

1. Q: ChatGPT Advanced Data Analysis can always replace human data scientists for complex modeling.
   Answer

   False — it augments workflows but cannot fully replace domain expertise, model validation, and complex modeling judgment.

2. Q: Removing trailing and leading spaces is a trivial step but important for matching and lookups.
   Answer

   True — trimming whitespace prevents mismatches and improves data quality.

3. Q: Normalizing units (e.g., inches to cm) is unnecessary if the model can infer context.
   Answer

   False — unit normalization is required to avoid misinterpretation and calculation errors; do not rely on inference alone.

Fill in the Blanks

1. Q: The Pandas function to remove duplicate rows based on specified columns is ________.
   Answer

   drop_duplicates()

2. Q: To fill missing values forward in a time series in Pandas, use ________.
   Answer

   fillna(method='ffill')

3. Q: When converting hours and minutes to decimal hours, divide minutes by ________.
   Answer

   60

Multiple Choice Questions (MCQ)

1. Q: Which method best identifies outliers using interquartile range?
   1. Z-score with threshold ±3
   2. IQR rule: points outside Q1 − 1.5*IQR or Q3 + 1.5*IQR
   3. Isolation Forest only
   4. Mean absolute deviation solely
   Answer

   b) IQR rule: points outside Q1 − 1.5*IQR or Q3 + 1.5*IQR

2. Q: Which API style best lets you request only the fields you need to reduce payload size?
   1. REST
   2. SOAP
   3. GraphQL
   4. FTP
   Answer

   c) GraphQL

Matching (match column A with B)

Descriptive

Q: Outline a short pipeline (3–6 steps) for preprocessing a "Free Plan Users" sheet before feeding it to a conversational agent for personalized messaging.

Creative

Q: Design a short user-bot conversation (4–6 turns) where the bot uses preprocessed user data to recommend a feature upgrade. Include fallback if the user responds ambiguously.

Activities, Vocabulary, Homework, Practice Task

Activities

1. Hands-on: Load a CSV of transactions, remove duplicates across columns, and produce a summary by category.
2. Prompt engineering: Craft a clear EDA prompt for ChatGPT asking for descriptive stats and visualization code for sales by month.
3. API exercise: Build a small script that calls a unit conversion API to standardize measurement units in a dataset.

Vocabulary

Homework

1. Prepare a 200–300 word report (or prompt + dataset) for ChatGPT asking for descriptive statistics on a provided 'transactions.csv'.
2. List three ambiguous country names you might encounter and propose a disambiguation strategy for each.

Practice Task

Using Python and Pandas, implement a function that:

1. reads a CSV,
2. trims whitespace on string columns,
3. drops duplicates based on a subset,
4. and returns descriptive statistics for numeric columns.

import pandas as pd
def preprocess_and_describe(path, subset_cols):
    df = pd.read_csv(path)
    # Trim whitespace for object columns
    obj_cols = df.select_dtypes(include=['object']).columns
    df[obj_cols] = df[obj_cols].apply(lambda s: s.str.strip())
    # Drop duplicates
    df = df.drop_duplicates(subset=subset_cols)
    # Descriptive stats
    desc = df.describe(include='all')
    return df, desc
# Usage:
# df_clean, stats = preprocess_and_describe('transactions.csv', ['transaction_id', 'email'])
          

Final Recommendations

• Iterate on prompts: be explicit about formats, desired outputs, and validation criteria.
• Instrument datasets with provenance and use unit tests for analytics pipelines.
• Combine conversational AI with deterministic code for mission-critical calculations.