Lee McFarling
Before 365:
Lab technician | CDPHE
After 365:
Data Analyst | Astreya
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Online Course
ChatGPT for Data Science
with
Elitsa Kaloyanova
Boost your productivity with ChatGPT Advanced Data Analysis: Solve data science problems with ChatGPT
2837 students already have enrolled
Skill level:
Intermediate
Duration:
2 hours
-
Lessons (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
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
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
Course OVERVIEW
Description
CPE Credits: 2.5
Field of Study: Specialized Knowledge
Delivery Method: QAS Self Study
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
Curriculum
34 lessons
1 exam
- 2. Data Science Use Cases59 MinThis 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 FreeFirst attempt at machine learning with ChatGPTAnalyzing a client database with ChatGPT in PythonAnalyzing a client database with ChatGPT in Python – analyzing top productsAnalyzing a client database with ChatGPT in Python – analyzing top clients, RFM analysisExploratory data analysis (EDA) with ChatGPT - histogram and scatter plotExploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detectionComprehensive Report on Dataset AnalysisHypothesis testing with ChatGPTMarvels comic book database: Intro to Regular Expressions (RegEx)Decoding comic book data: Python Regular Expressions and ChatGPTAdvanced Analysis of Comic Book Database Using Regular ExpressionsAlgorithm recommendation: Movie Database Analysis with ChatGPTAlgorithm recommendation: recommendation engine for movies with ChatGPTEnhancing the Movie Database Recommendation EngineEthical principles in data and AI utilizationUsing ChatGPT for ethical considerations
- 3. Intro to the Case Study24 MinIn 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.
- 4. Case Study User Reviews29 MinIn 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 PreprocessingOptimizing User Reviews: Data Preprocessing & EDAReg Ex for Analyzing Text Review DataUnderstanding Differences between Multinomial and Bernouilli Naive BayesMachine Learning with Naïve Bayes (First Attempt)Machine Learning with Naïve Bayes – converting the problem to a binary oneTesting the model on new data
- 5. Course exam15 Min
Free preview
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
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ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
Certificates are included with the Self-Study learning plan.
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