10 creative ways to use ChatGPT to learn data science from scratch

1. Understand Core Data Science Concepts

Break down complex data science topics into simple explanations.

Prompt →
"I'm new to data science. Can you explain core concepts like data cleaning, feature engineering, and model evaluation in a beginner-friendly way?"


2. Create a Personalized Study Plan

Plan your data science learning journey with a tailored schedule.

Prompt →
"I want to master data science in 6 months while dedicating 2 hours daily. Can you create a detailed weekly study plan with resources for Python, statistics, and machine learning?"


3. Generate Coding Exercises and Solutions

Practice coding with real-world datasets and scenarios.

Prompt →
"Can you provide 10 hands-on coding exercises in Python for data cleaning and visualization, with step-by-step solutions?"


4. Simplify Machine Learning Algorithms

Learn how machine learning algorithms work with relatable analogies.

Prompt →
"Can you explain how decision trees and random forests work using a real-life analogy, like planning a family vacation?"


5. Analyze Real-World Datasets

Practice working with datasets to build skills.

Prompt →
"Can you guide me through analyzing a real-world dataset, like predicting house prices, using Python step by step?"


6. Build a Portfolio Project

Create impactful projects to showcase your skills.

Prompt →
"I want to build a data science portfolio project on customer churn prediction. Can you help me outline the steps, tools, and methods to use?"


7. Mock Data Science Interviews

Prepare for interviews with tailored questions and answers.

Prompt →
"Can you simulate a mock interview for a data science role, focusing on Python, SQL, and machine learning questions?"

8. Write Blogs or Articles on Data Science

Share knowledge by writing educational content.

Prompt →
"I want to write a blog post about the importance of feature scaling in machine learning. Can you help me draft an engaging and informative article?"


9. Visualize Data Better

Learn to create compelling data visualizations.

Prompt →
"Can you guide me on how to use Matplotlib and Seaborn to create a dashboard-like visualization for sales data?"

10. Stay Updated with the Latest Trends

Get concise summaries of the latest research and tools in data science.

Prompt →
"What are the top 5 emerging trends or tools in data science that I should explore to stay ahead in 2025?"

Share with credits: https://news.1rj.ru/str/datasciencefun

ENJOY LEARNING 👍👍

#chatgptprompts

Various types of test used in statistics for data science

T-test: used to test whether the means of two groups are significantly different from each other.

ANOVA: used to test whether the means of three or more groups are significantly different from each other.

Chi-squared test: used to test whether two categorical variables are independent or associated with each other.

Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.

Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.

Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.

Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.

Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.

Top 10 Data Science Roles with Skills & Salary details ✅

Essential Tools and Libraries for Data Science Students

1. Programming Languages:

Python

R

SQL


2. Python Libraries:

NumPy: For numerical computations.

Pandas: For data manipulation and analysis.

Matplotlib: For basic data visualization.

Seaborn: For statistical data visualization.

Scikit-learn: For machine learning models.

TensorFlow: For deep learning.

PyTorch: For advanced neural networks.


3. R Libraries:

ggplot2: For data visualization.

dplyr: For data manipulation.

caret: For machine learning.

shiny: For building interactive web apps.


4. Data Visualization Tools:

Tableau

Power BI

Google Data Studio


5. Big Data Tools:

Apache Hadoop

Apache Spark


6. Cloud Platforms:

AWS (Amazon Web Services)

Google Cloud Platform (GCP)

Microsoft Azure


7. Statistical Software:

SAS

SPSS


8. Version Control System:

Git


9. Notebook Tools:

Jupyter Notebook

Google Colab


10. Data Sources for Practice:

Kaggle Datasets

UCI Machine Learning Repository

GitHub Repositories

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

Machine Learning Algorithms

7 Free Kaggle Micro-Courses for Data Science Beginners with Certification

Python

https://www.kaggle.com/learn/python

Pandas

https://www.kaggle.com/learn/pandas

Data visualization

https://www.kaggle.com/learn/data-visualization

Intro to sql

https://www.kaggle.com/learn/intro-to-sql

Advanced Sql

https://www.kaggle.com/learn/advanced-sql

Intro to ML

https://www.kaggle.com/learn/intro-to-machine-learning

Advanced ML

https://www.kaggle.com/learn/intermediate-machine-learning

#datascienceprojects #kaggle

The Data Science skill no one talks about...

Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
    1. a dataset, and
    2. a clearly defined metric to optimize for, e.g. accuracy

But it doesn’t.

It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.

Let’s go through an example.

Example

Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber.

But why?

There are different reasons why a user would stop using Uber. For example:

   1.  “Lyft is offering better prices for that geo” (pricing problem)
   2. “Car waiting times are too long” (supply problem)
   3. “The Android version of the app is very slow” (client-app performance problem)

You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.

Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?

This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.

You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.

For example…

Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)

One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:

    The A group. No user in this group will receive any discount.

    The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.

You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell

    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem