ChatGPT for Data Science
Boost your productivity with ChatGPT Advanced Data Analysis: Solve data science problems with ChatGPT
Start for Free
What you get:
- 2 hours of content
- 27 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
ChatGPT for Data Science
Start for Free
What you get:
- 2 hours of content
- 27 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Start for Free
What you get:
- 2 hours of content
- 27 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
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.
Top Choice of Leading Companies Worldwide
Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
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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
Curriculum
- 2. Data Science Use Cases17 Lessons 59 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 ChatGPT5 minFirst attempt at machine learning with ChatGPT4 minAnalyzing a client database with ChatGPT in Python4 minAnalyzing a client database with ChatGPT in Python – analyzing top products4 minAnalyzing a client database with ChatGPT in Python – analyzing top clients, RFM analysis4 minExploratory data analysis (EDA) with ChatGPT - histogram and scatter plot5 minExploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detection5 minComprehensive Report on Dataset Analysis Read now1 minHypothesis testing with ChatGPT4 minMarvels comic book database: Intro to Regular Expressions (RegEx)2 minDecoding comic book data: Python Regular Expressions and ChatGPT4 minAdvanced Analysis of Comic Book Database Using Regular Expressions Read now1 minAlgorithm recommendation: Movie Database Analysis with ChatGPT3 minAlgorithm recommendation: recommendation engine for movies with ChatGPT4 minEnhancing the Movie Database Recommendation Engine Read now1 minEthical principles in data and AI utilization3 minUsing ChatGPT for ethical considerations5 min
- 3. Intro to the Case Study6 Lessons 24 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.Intro to the case study3 minNaïve Bayes4 minTokenization and Vectorization5 minImbalanced data sets2 minOvercome imbalanced data in machine learning4 minModel performance metrics6 min
- 4. Case Study User Reviews7 Lessons 29 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 Preprocessing2 minOptimizing User Reviews: Data Preprocessing & EDA4 minReg Ex for Analyzing Text Review Data3 minUnderstanding Differences between Multinomial and Bernouilli Naive Bayes4 minMachine Learning with Naïve Bayes (First Attempt)6 minMachine Learning with Naïve Bayes – converting the problem to a binary one5 minTesting the model on new data5 min
Topics
Course Requirements
- You need to complete an introduction to Python before taking this course
- Basic skills in statistics, probability, and linear algebra are required
- It is highly recommended to take the Machine Learning in Python course first
- You will need to install the Anaconda package, which includes Jupyter Notebook
Who Should Take This Course?
Level of difficulty: Intermediate
- Aspiring data analysts, data scientists, ML engineers, and AI engineers
- Existing data analysts, data scientists, ML engineers, and AI engineers who want to boost their ChatGPT skills and improve their workflow
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Elitsa Kaloyanova is a Computational Biologist, with significant expertise in the fields of algorithms, data structures, phylogenetics, and population genetics. She has a solid academic background in Bioinformatics with publications on constructing Phylogenetic Networks and Trees. In 2021, she led 365’s effort to create practice exams and course exams for each course included in the program. Elitsa was able to successfully coordinate with several types of stakeholders and performed superior Quality Assurance.
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