Creating a Financial Data Analysis Application Using ChatGPT-o1

DataCamp · Intermediate ·🧠 Large Language Models ·1y ago

Key Takeaways

Creating a financial data analysis application using ChatGPT-o1 and Python, with a focus on complex reasoning tasks and coding tasks without requiring prior coding experience.

Original Description

Resources (including link to code along notebook): https://bit.ly/3CHNUaY OpenAI’s ChatGPT-o1, is built to handle complex reasoning tasks with ease, making it a powerful tool for building robust applications that would otherwise take days to build. In this code-along, Adel, VP of Media at DataCamp, shows you how to work with ChatGPT-o1 to create an application designed for financial data analysis using Python, with no coding experience required. We will walk through real-world use cases, demonstrating exactly how o1 stands out from other models on the market, when to use it, and why it’s uniquely suited for coding tasks. Key Takeaways: - Learn the steps involved in creating an application using ChatGPT-o1 - Build a financial analysis application using ChatGPT o1 using Python with no coding experience required - How o1 compares to other large language models for coding use-cases
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from DataCamp · DataCamp · 0 of 60

← Previous Next →
1 SQL Server Tutorial: Date manipulation
SQL Server Tutorial: Date manipulation
DataCamp
2 R Tutorial: Intermediate Interactive Data Visualization with plotly in R
R Tutorial: Intermediate Interactive Data Visualization with plotly in R
DataCamp
3 R Tutorial: Adding aesthetics to represent a variable
R Tutorial: Adding aesthetics to represent a variable
DataCamp
4 R Tutorial: Moving Beyond Simple Interactivity
R Tutorial: Moving Beyond Simple Interactivity
DataCamp
5 Python Tutorial: Why use ML for marketing? Strategies and use cases
Python Tutorial: Why use ML for marketing? Strategies and use cases
DataCamp
6 Python Tutorial: Preparation for modeling
Python Tutorial: Preparation for modeling
DataCamp
7 Python Tutorial: Machine Learning modeling steps
Python Tutorial: Machine Learning modeling steps
DataCamp
8 R Tutorial: The prior model
R Tutorial: The prior model
DataCamp
9 R Tutorial: Data & the likelihood
R Tutorial: Data & the likelihood
DataCamp
10 R Tutorial: The posterior model
R Tutorial: The posterior model
DataCamp
11 R Tutorial: An Introduction to plotly
R Tutorial: An Introduction to plotly
DataCamp
12 R Tutorial: Plotting a single variable
R Tutorial: Plotting a single variable
DataCamp
13 R Tutorial: Bivariate graphics
R Tutorial: Bivariate graphics
DataCamp
14 Python Tutorial: Customer Segmentation in Python
Python Tutorial: Customer Segmentation in Python
DataCamp
15 Python Tutorial: Time cohorts
Python Tutorial: Time cohorts
DataCamp
16 Python Tutorial: Calculate cohort metrics
Python Tutorial: Calculate cohort metrics
DataCamp
17 Python Tutorial: Cohort analysis visualization
Python Tutorial: Cohort analysis visualization
DataCamp
18 R Tutorial: Building Dashboards with flexdashboard
R Tutorial: Building Dashboards with flexdashboard
DataCamp
19 R Tutorial: Anatomy of a flexdashboard
R Tutorial: Anatomy of a flexdashboard
DataCamp
20 R Tutorial: Layout basics
R Tutorial: Layout basics
DataCamp
21 R Tutorial: Advanced layouts
R Tutorial: Advanced layouts
DataCamp
22 Python Tutorial: Time Series Analysis in Python
Python Tutorial: Time Series Analysis in Python
DataCamp
23 Python Tutorial: Correlation of Two Time Series
Python Tutorial: Correlation of Two Time Series
DataCamp
24 Python Tutorial: Simple Linear Regressions
Python Tutorial: Simple Linear Regressions
DataCamp
25 Python Tutorial: Autocorrelation
Python Tutorial: Autocorrelation
DataCamp
26 R Tutorial: The gapminder dataset
R Tutorial: The gapminder dataset
DataCamp
27 R Tutorial: The filter verb
R Tutorial: The filter verb
DataCamp
28 R Tutorial: The arrange verb
R Tutorial: The arrange verb
DataCamp
29 R Tutorial: The mutate verb
R Tutorial: The mutate verb
DataCamp
30 R Tutorial: What is cluster analysis?
R Tutorial: What is cluster analysis?
DataCamp
31 R Tutorial: Distance between two observations
R Tutorial: Distance between two observations
DataCamp
32 R Tutorial: The importance of scale
R Tutorial: The importance of scale
DataCamp
33 R Tutorial: Measuring distance for categorical data
R Tutorial: Measuring distance for categorical data
DataCamp
34 Python Tutorial: Plotting multiple graphs
Python Tutorial: Plotting multiple graphs
DataCamp
35 Python Tutorial: Customizing axes
Python Tutorial: Customizing axes
DataCamp
36 Python Tutorial: Legends, annotations, & styles
Python Tutorial: Legends, annotations, & styles
DataCamp
37 Python Tutorial: Introduction to iterators
Python Tutorial: Introduction to iterators
DataCamp
38 Python Tutorial: Playing with iterators
Python Tutorial: Playing with iterators
DataCamp
39 Python Tutorial: Using iterators to load large files into memory
Python Tutorial: Using iterators to load large files into memory
DataCamp
40 SQL Tutorial: Introduction to Relational Databases in SQL
SQL Tutorial: Introduction to Relational Databases in SQL
DataCamp
41 SQL Tutorial: Tables: At the core of every database
SQL Tutorial: Tables: At the core of every database
DataCamp
42 SQL Tutorial: Update your database as the structure changes
SQL Tutorial: Update your database as the structure changes
DataCamp
43 Python Tutorial: Classification-Tree Learning
Python Tutorial: Classification-Tree Learning
DataCamp
44 Python Tutorial: Decision-Tree for Classification
Python Tutorial: Decision-Tree for Classification
DataCamp
45 Python Tutorial: Decision-Tree for Regression
Python Tutorial: Decision-Tree for Regression
DataCamp
46 Python Tutorial: Census Subject Tables
Python Tutorial: Census Subject Tables
DataCamp
47 Python Tutorial: Census Geography
Python Tutorial: Census Geography
DataCamp
48 Python Tutorial: Using the Census API
Python Tutorial: Using the Census API
DataCamp
49 R Tutorial: A/B Testing in R
R Tutorial: A/B Testing in R
DataCamp
50 R Tutorial: Baseline Conversion Rates
R Tutorial: Baseline Conversion Rates
DataCamp
51 R Tutorial: Designing an Experiment - Power Analysis
R Tutorial: Designing an Experiment - Power Analysis
DataCamp
52 R Tutorial: Introduction to qualitative data
R Tutorial: Introduction to qualitative data
DataCamp
53 R Tutorial: Understanding your qualitative variables
R Tutorial: Understanding your qualitative variables
DataCamp
54 R Tutorial: Making Better Plots
R Tutorial: Making Better Plots
DataCamp
55 SQL Tutorial: OLTP and OLAP
SQL Tutorial: OLTP and OLAP
DataCamp
56 SQL Tutorial: Storing data
SQL Tutorial: Storing data
DataCamp
57 SQL Tutorial: Database design
SQL Tutorial: Database design
DataCamp
58 Python Tutorial: Introduction to spaCy
Python Tutorial: Introduction to spaCy
DataCamp
59 Python Tutorial: Statistical Models
Python Tutorial: Statistical Models
DataCamp
60 Python Tutorial: Rule-based Matching
Python Tutorial: Rule-based Matching
DataCamp

In this code-along, learn how to create a financial data analysis application using ChatGPT-o1 and Python, with a focus on complex reasoning tasks and coding tasks without requiring prior coding experience. Discover how ChatGPT-o1 stands out from other large language models and how to use it for coding tasks. Build a real-world application and learn the steps involved in creating an application using ChatGPT-o1.

Key Takeaways
  1. Install required libraries and set up the environment
  2. Import necessary modules and load data
  3. Design effective prompts for ChatGPT-o1
  4. Build a financial analysis application using ChatGPT-o1 and Python
  5. Test and refine the application
💡 ChatGPT-o1 is uniquely suited for coding tasks and complex reasoning tasks, making it a powerful tool for building robust applications.

Related Reads

Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →