21.11.2024
excellent! ChatGpt can accelerate the productivity ...
ChatGPT for Data Science
with
Elitsa Kaloyanova
Boost your productivity with ChatGPT Advanced Data Analysis: Solve data science problems with ChatGPT
2 hours of content
2582 students
$99.00
Lifetime access
14-Day Money-Back Guarantee
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
A course by
Elitsa Kaloyanova
$99.00
Lifetime access
14-Day Money-Back Guarantee
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
$99.00
Lifetime access
14-Day Money-Back Guarantee
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 your data science skills by mastering the integration of ChatGPT with Python
- Explore practical data science use cases where ChatGPT can improve your analytical workflow
- Acquire cutting-edge AI and machine learning technical skills that will boost your resume and impress hiring managers
- Seamlessly integrate AI-driven prompt engineering in your daily workflow
- Maximize your analytical capabilities to the fullest by leveraging ChatGPT
- Solve real-life data problems with ChatGPT
Top Choice of Leading Companies Worldwide
Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
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.
Learn for Free
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
Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Curriculum
- 1. Introduction to the Data Science Process and ChatGPT4 Lessons 11 Min
This section covers traditional methods, ChatGPT's role in data science, and steps to create a ChatGPT account.
Introduction to the course2 minTraditional data science methods and the role of ChatGPT5 minHow to install ChatGPT2 minHow ChatGPT can boost your productivity2 min - 2. Data Science Use Cases17 Lessons 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 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 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 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 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 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
machine learningdata visualizationNaïve BayesRegExAIChatGPTRecommendation enginedata analysisData processing
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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