Introducing ChatGPT Data Science
"AI will not replace developers, but will replace developers that don't leverage AI"
Whether you're just starting to explore the fascinating realm of Data Science or you're a seasoned professional looking to elevate your skills, our Data Science Guide is the perfect companion for your journey.
For beginners, our guide gently introduces you to the field, breaking down complex topics into digestible content and guiding you through hands-on examples. Don't worry about being overwhelmed – we've curated our material to ensure a smooth learning curve, helping you build your knowledge base one step at a time.
For the experienced data scientists among us, this guide offers a wealth of advanced material designed to hone your skills further. Dive deeper into specialized areas, explore new methods and techniques, and stay updated with the latest industry trends.
With our Data Science Guide, not only will you enhance your understanding of the field, but you'll also develop practical skills that you can apply directly in your projects. Each section is coupled with interactive prompts, ensuring that you can practice as you learn. This iterative approach will keep you engaged, reinforce your knowledge, and help you constantly improve.
So why wait? Begin your journey or take your expertise to the next level with our Data Science Guide. Master the world of data science, and watch as new opportunities unfold before you.
Who is this guide for?
These prompts are ideal for Data Analysts, Data Engineers, Python Developers, Quantitative Analysts, Machine Learning Scientists, Data Scientists, and statisticians of all skill levels, regardless of whether you are a novice or an experienced prompt engineer.
What's included in this Master Edition?
Data Analyst R
- Introduction to R
- Introduction to the Tidyverse
- Data Manipulation with dplyr
- Joining Data with dplyr
- Introduction to Statistics in R
- ...
Data Scientist R
- Data Communication Concept
- Cleaning Data in R
- Working with Dates and Times in R
- Introduction to Regression in R
- Supervised Learning in R: Classification
- Supervised Learning in R: Regression
- Unsupervised Learning
- ...
Data Analyst Python
- Data Manipulation with Pandas
- Joining Data with Pandas
- Introduction to Statistics in Python
- Importing & Cleaning Data with Python
- Exploratory Data Analysis in Python
- Sampling in Python
- ...
Data Scientist Python
- Python Programming for Data Science
- Writing Functions in Python
- Python Libraries for Data Science
- Machine Learning Algorithms in Python
- Supervised Learning with scikit-learn
- Machine Learning with Tree-Based Models in Python
- Python for Data Science in the Cloud
- ...
Quantitative Analyst R
- Manipulating Time Series with xts and zoo in R
- Arima models in R
- Portfolio analysis and optimization in R
- Risk Management and Simulation with R
- Visualizing Time Series Data in R
- Bond Valuation and Analysis in R
- Financial Trading in R
- ...
Data Engineer Python
- Data Ingestion
- Data Processing
- Data Modeling
- Data Pipelines
- Data Quality and Governance
- Data Visualization and Reporting
- Performance Optimization and Scalability
- ...
Data Analyst PowerBI
- Data visualization in Power BI
- DAX (Data Analysis Expressions) in Microsoft Power BI
- Power BI Desktop features
- Power BI Query Editor
- Power BI data sources
- Power BI dashboards and reports
- Power BI integration and automation
- ...
Data Analyst Tableau
Statistician
ML Scientist
and much more
Total prompts: 3000