Online Course
ChatGPT for Data Science

Name: ChatGPT for Data Science
Price: 36 USD

with Elitsa Kaloyanova

Boost your productivity with ChatGPT Advanced Data Analysis: Solve data science problems with ChatGPT

4.8

862 reviews on

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3,235 students already enrolled

Skill level:

Intermediate

Duration:

2 hours

Lessons (2 hours)

CPE credits:

2.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Enhance data science skills by integrating ChatGPT with Python.
Explore use cases where ChatGPT improves analytical workflows.
Acquire AI and machine learning skills to boost your resume.
Integrate AI-driven prompt engineering into daily work.
Solve real-life data problems using ChatGPT effectively.

Topics & tools

Machine LearningData VisualizationNaïve BayesRegExAIChatGPTRecommendation engineData AnalysisData ProcessingPython

Your instructor

Elitsa Kaloyanova

Senior Data Scientist at 365 Data Science | Computational Biologist | Data Visualization Expert

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Bioinformatics

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Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 2.5 Field of Study: Specialized Knowledge
Delivery Method: QAS Self Study

In this course, you will learn how to utilize the power of ChatGPT and its advanced analysis tool. We'll tackle various data science problems, including exploratory data analysis, hypothesis testing, and we’ll work with Python's regex library, as well as develop a recommendation engine. Our key project involves using company data to address a machine learning issue—training a Naïve Bayes model to detect sentiment in user course reviews. But what's the real benefit for data scientists? For me, ChatGPT for data scientists’ foremost advantage lies in its time-saving efficiency, enhancing daily productivity by conducting data science tasks, writing code and debugging it. I'm confident it will boost your efficiency, too.

Prerequisites

Access to ChatGPT
Machine Learning fundamentals are helpful but not mandatory

Advanced preparation

Machine Learning in Python

Curriculum

34 lessons 1 exam

1. Introduction to the Data Science Process and ChatGPT

11 min

This section covers traditional methods, ChatGPT's role in data science, and steps to create a ChatGPT account.

11 min

This section covers traditional methods, ChatGPT's role in data science, and steps to create a ChatGPT account.

Introduction to the course Free

Traditional data science methods and the role of ChatGPT Free

How to install ChatGPT Free

How ChatGPT can boost your productivity Free
2. Data Science Use Cases

59 min

This section—focusing on ChatGPT for data scientists—covers data preprocessing, exploratory data analysis (EDA), regular expressions, and building a recommendation engine before delving into AI ethics and detecting data biases with ChatGPT.

59 min

This section—focusing on ChatGPT for data scientists—covers data preprocessing, exploratory data analysis (EDA), regular expressions, and building a recommendation engine before delving into AI ethics and detecting data biases with ChatGPT.

Data Preprocessing with ChatGPT Free

First attempt at machine learning with ChatGPT

Analyzing a client database with ChatGPT in Python

Analyzing a client database with ChatGPT in Python – analyzing top products

Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM analysis

Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot

Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detection

Comprehensive Report on Dataset Analysis

Hypothesis testing with ChatGPT

Marvels comic book database: Intro to Regular Expressions (RegEx)

Decoding comic book data: Python Regular Expressions and ChatGPT

Advanced Analysis of Comic Book Database Using Regular Expressions

Algorithm recommendation: Movie Database Analysis with ChatGPT

Algorithm recommendation: recommendation engine for movies with ChatGPT

Enhancing the Movie Database Recommendation Engine

Ethical principles in data and AI utilization

Using ChatGPT for ethical considerations
3. Intro to the Case Study

24 min

In this theoretical section we’ll discuss topics such as imbalanced data, Naïve Bayes algorithms what a confusion matrix is, and define metrics such as precision, recall and F1 score. All these will be the foundation for our Python case study involving classifying user reviews based on text data, also known as sentiment analysis.

24 min

In this theoretical section we’ll discuss topics such as imbalanced data, Naïve Bayes algorithms what a confusion matrix is, and define metrics such as precision, recall and F1 score. All these will be the foundation for our Python case study involving classifying user reviews based on text data, also known as sentiment analysis.

Intro to the case study

Naïve Bayes

Tokenization and Vectorization

Imbalanced data sets

Overcome imbalanced data in machine learning

Model performance metrics
4. Case Study User Reviews

29 min

In the final section of this course, we’ll delve into sentiment analysis by classifying user reviews. We’ll use our own 365 data and train a Naïve Bayes algorithm to classify user reviews as good or bad. At the end of the section we’ll test our model on a new validation set.

29 min

In the final section of this course, we’ll delve into sentiment analysis by classifying user reviews. We’ll use our own 365 data and train a Naïve Bayes algorithm to classify user reviews as good or bad. At the end of the section we’ll test our model on a new validation set.

Loading the Dataset and Preprocessing

Optimizing User Reviews: Data Preprocessing & EDA

Reg Ex for Analyzing Text Review Data

Understanding Differences between Multinomial and Bernouilli Naive Bayes

Machine Learning with Naïve Bayes (First Attempt)

Machine Learning with Naïve Bayes – converting the problem to a binary one

Testing the model on new data
5. Course exam

15 min

15 min

Course exam

Free lessons

1.1 Introduction to the course

2 min

1.2 Traditional data science methods and the role of ChatGPT

5 min

1.3 How to install ChatGPT

2 min

1.4 How ChatGPT can boost your productivity

2 min

2.1 Data Preprocessing with ChatGPT

5 min

2.2 First attempt at machine learning with ChatGPT

4 min

Start for free

I'm happy to say after about 6 months, I was getting job interviews as either a business analyst or data analyst, and now I'm working downtown at a tech company as a data analyst. 365 helped me level up my Excel, SQL, and data visualization skills—and start building real projects for my employer. After years in the lab, 365 Data Science finally found the right way to help me change to AI and data science, and I'm excited for what’s next.

Lee McFarling

Before 365: Lab technician | CDPHE

After 365: Data Analyst | Astreya

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4.8

Based on 862 reviews

#1 most reviewed

AI and data learning platform on Trustpilot.

Platform worth of time! Perfect visualization, simple language, quality curriculum.

Kiram A.

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9 in 10

people walk away career-ready

with practical data and AI skills.

I began my self-training with 365 Data Science before joining a Data Science Fellowship, and it played a crucial role in advancing my skills as a Data Analyst. The courses are well-organized, practical, and detailed. Now, managing AI projects while analyzing data, I often return to 365 to learn new, job-relevant skills—like Power BI, Web Scraping, and Power Query. 365 has been an ongoing resource for my professional growth.