Prompt Engineering For Data Analysis Python, Pandas, Chatgpt

Prompt Engineering For Data Analysis Python, Pandas, Chatgpt
Published 5/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

ChatGPT & Python. No Coding Needed. Data Analysis & Data Visualisation with ChatGPT, Python, Pandas & Prompt Engineering

What you’ll learn

Confidently approach data analysis tasks with Python and Pandas, even without prior coding experience.

Leverage the power of ChatGPT and prompt engineering techniques to efficiently generate accurate, high-quality code for data analysis and visualisation.

Seamlessly integrate ChatGPT-generated code into their Python and Pandas workflows, saving time and effort on manual coding.

Effectively communicate with ChatGPT by crafting optimised prompts that guide the AI to produce the desired results.

Master the use of Jupyter Notebook and Google Colab, enabling a smooth and productive learning experience.

Create visually appealing and informative data visualisations using the Matplotlib library to support their data-driven decision-making processes.

Develop a strong foundation in Python, Pandas, and data analysis, paving the way for future learning and professional growth in the field.

Requirements

No prior experience with AI or programming is needed, but an eagerness to learn and explore new technologies is a plus!

Description

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Quick Preview on the Power of ChatGPT for Data Analysis

Lecture 3 Resources provided in this course

Lecture 4 Course Outline

Lecture 5 Download Resources

Section 2: Introduction to ChatGPT

Lecture 6 GPT 4 Intro

Lecture 8 Drafting a Prompt

Lecture 9 Drafting a prompt continued

Section 3: Basics of Prompt Engineering

Lecture 11 Intro to Prompt Engineering

Lecture 12 The Process of Drafting and Refining Prompts

Lecture 13 Types of Prompting

Lecture 14 Priming Prompt

Lecture 15 Task Decomposition

Section 4: Download, Install and Setup Anaconda on Mac

Lecture 16 Download Anaconda

Lecture 17 Install Anaconda on Mac

Lecture 18 Open Conda from Terminal and Create Environment

Lecture 19 Environments & Libraries

Lecture 20 Open Jupyter Notebook

Lecture 21 Closing Jupyter and Terminal

Section 5: Download and Install on Windows
Lecture 22 Installing Anaconda on Windows
Section 6: Intro to Jupyter Notebook

Lecture 23 Open and save new python scripts

Lecture 24 Keyboard Shortcuts in Jupyter

Lecture 25 Header in Jupyter

Lecture 26 Cell Types & Modes in JupyterNotebook

Lecture 27 Outputs from Jupyter Cells

Lecture 28 Importing Libraries

Section 7: Coding with Google Collab

Section 8: Python Crash Course

Lecture 30 Working with comments

Lecture 31 Data Types in Python

Lecture 32 Operators

Lecture 35 Built-in functions in Python

Lecture 36 Custom Functions

Lecture 37 String Methods

Lecture 39 In & Not In functions

Lecture 40 Working with Lists Data Type

Lecture 41 Index and Slicing

Lecture 42 Data Type Dictionary and IF function

Lecture 43 For Loop

Section 9: Series in Pandas

Lecture 44 Intro to Series Section

Lecture 45 What are Series

Lecture 46 Converting different data types into Series

Lecture 47 Series Methods

Lecture 48 Understanding the PD.Series Function with GPT

Lecture 49 Importing a column as a Series from CSV

Lecture 50 Apply basic functions on series data set

Lecture 51 Filter, Overwrite specific data in the series and Get method on Get Method

Lecture 52 Custom Functions and .apply() on a Series

Lecture 53 Series Attributes

Lecture 54 Working with Missing Values NaN

Section 10: Working with a DataFrame

Lecture 55 Intro to DataFrame section

Lecture 56 Importing a dataframe from CSV

Lecture 57 Working with missing (NaN) values

Lecture 58 Extracting numbers from a string column

Lecture 59 Filter & Sort Columns

Lecture 60 Identify and remove duplicate rows

Lecture 61 Filtering data frame by specific columns values

Lecture 62 Filtering by multiple column conditions

Lecture 63 Filter text columns by parsing strings

Lecture 64 Filter by one and more than one columns

Section 11: Mastering GroupBy function using prompt engineering

Lecture 65 Intro to GroupBy

Lecture 66 Using GroupBy for exploratory analysis and data insights

Lecture 67 GroupBy by multiple columns & Aggregate method

Section 12: Working with Multiple DataFrames

Lecture 68 Intro to the Dataset used in this section

Lecture 69 Combine DataFrames with Concat & Append

Lecture 70 Merging Dataset based on one KEY column

Lecture 71 Merging based on multiple columns

Lecture 72 "How" parameter for merging multiple dataframes

Lecture 73 Combining dataframes using "Left" Join

Lecture 74 Merging dataset with "Left" & "Right" Join by using different key parameters

Section 13: Visualisations

Lecture 75 Introduction to Visualisation Section

Lecture 76 Extract Apple Stock Price data using Yahoo Finance Library

Lecture 77 Plotting with Matplotlib library

Lecture 78 Understanding visualisations features available

Lecture 79 Visualisation features continued

Lecture 80 Applying visualisations features on AAPL stock price

Lecture 81 Plotting percent change in prices

Lecture 82 Plotting a Histogram

Lecture 83 Modifying the visual aesthetics of the histogram

Section 14: Importing and Exporting data in Python

Lecture 85 Intro to Importing and Exporting data

Lecture 86 Importing data from a url

Lecture 87 Exporting data to excel

Lecture 88 Exporting data as ".csv" & ".txt" files

Lecture 89 Importing multiple files as data frames from a folder path / location

Lecture 90 Importing multiple files continued

Section 15: Congratulations

Lecture 91 Congrats

This course is designed for individuals from diverse backgrounds who are eager to leverage the power of AI tools like ChatGPT to revolutionise their coding and data analysis journey. Whether you’re a complete beginner with no coding experience, an experienced programmer looking to enhance your skills, or a data enthusiast seeking innovative ways to tackle data analysis, this course is perfect for you. Embrace the potential of ChatGPT and prompt engineering to elevate your coding capabilities and make data-driven decisions with confidence.