Harvard University offers a ton of FREE online courses.
From Computer Science to Artificial Intelligence.
Here are 10 FREE courses you don't want to miss

1. Introduction to Computer Science
An introduction to the intellectual enterprises of computer science and the art of programming.
Check here 👇
https://pll.harvard.edu/course/cs50-introduction-computer-science?delta=0


2. Web Programming with Python and JavaScript
This course takes you deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.
Check here 👇
https://pll.harvard.edu/course/cs50s-web-programming-python-and-javascript?delta=0

3. Introduction to Programming with Scratch

A gentle introduction to programming that prepares you for subsequent courses in coding.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-programming-scratch?delta=0

4. Introduction to Programming with Python
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.
Check here 👇
https://edx.org/course/cs50s-introduction-to-programming-with-python



5. Understanding Technology
This is CS50’s introduction to technology for students who don’t (yet!) consider themselves computer persons.
Check here 👇
https://pll.harvard.edu/course/cs50s-understanding-technology-0?delta=0

6. Introduction to Artificial Intelligence with Python
Learn to use machine learning in Python in this introductory course on artificial intelligence.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python?delta=0


7. Introduction to Game Development
Learn about the development of 2D and 3D interactive games in this hands-on course, as you explore the design of games such as Super Mario Bros., Pokémon, Angry Birds, and more.
Check here 👇
https://pll.harvard.edu/course/cs50s-introduction-game-development?delta=0

8. CS50's Computer Science for Business Professionals
This is CS50’s introduction to computer science for business professionals.
Check here 👇
https://pll.harvard.edu/course/cs50s-computer-science-business-professionals-0?delta=0


9. Mobile App Development with React Native
Learn about mobile app development with React Native, a popular framework maintained by Facebook that enables cross-platform native apps using JavaScript without Java or Swift.
Check here 👇
https://pll.harvard.edu/course/cs50s-mobile-app-development-react-native?delta=0

10. Introduction to Data Science with Python
Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.
Check here 👇
https://pll.harvard.edu/course/introduction-data-science-python?delta=0

𝐈𝐁𝐌 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬😍

🚀 Dive into the world of Data Analytics with these 6 free courses by IBM!

Gain practical knowledge and stand out in your career with tools designed for real-world applications.

All courses come with expert guidance and are free to access!🎉

𝐋𝐢𝐧𝐤 👇:- 
 
https://bit.ly/4iXOmmb
 
Enroll For FREE & Get Certified 🎓

🔟 Data Analyst Project Ideas for Beginners

1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns.

2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics.

3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance.

4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights.

5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations.

6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness.

7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings.

8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings.

9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events.

10. Financial Analysis: Analyze a company’s financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends.

Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope it helps :)

If I were to start Data Analytics in 2025 💫🚀

❯ Python
https://cs50.harvard.edu/python/2022/

https://www.freecodecamp.org/learn/data-analysis-with-python/

https://t.iss.one/pythonanalyst

❯ SQL
https://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql

https://www.freecodecamp.org/learn/relational-database/

https://bit.ly/3YpMM2y

❯ Excel
https://excel-practice-online.com/

https://t.iss.one/excel_analyst

❯ Power BI
https://www.freecodecamp.org/learn/data-visualization/

https://t.iss.one/PowerBI_analyst

https://www.workout-wednesday.com/power-bi-challenges/

❯ Tableau
https://www.tableau.com/learn/training

❯ Jobs
https://t.iss.one/jobs_SQL

https://t.iss.one/jobinterviewsprep

https://t.iss.one/datasciencej

❯ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum

https://www.sherrytowers.com/cowan_statistical_data_analysis.pdf

❯ Data Science
cognitiveclass.ai/courses/data-science-101

https://kaggle.com/learn

https://t.iss.one/datasciencefun/290

❯ Machine Learning
https://developers.google.com/machine-learning/crash-course

https://www.freecodecamp.org/learn/machine-learning-with-python/

❯ Artificial Intelligence
https://imp.i115008.net/qn27PL

introtodeeplearning.com

t.iss.one/machinelearning_deeplearning/

t.iss.one/aifoundations

❯ Data Engineering
https://bit.ly/3fGRjLu

https://t.iss.one/sql_engineer

Join @free4unow_backup for more free resources

Like for more ❤️

ENJOY LEARNING👍👍

7 Useful Python One-Liners

1. Reverse a string

print("Python"[::-1]) # Output: nohtyP

2. Check for Palindrome

is_palindrome = lambda s: s == s[::-1]
print(is_palindrome("madam")) # Output: True

3. Get all even numbers from a list

print([x for x in range(20) if x % 2 == 0])

4. Flatten a nested list

print([item for sublist in [[1,2],[3,4]] for item in sublist])

5. Find factorial of a number

import math; print(math.factorial(5)) # Output: 120

6. Count frequency of elements

from collections import Counter
print(Counter("banana")) # Output: {'a': 3, 'b': 1, 'n': 2}

7. Swap two variables

a, b = 5, 10
a, b = b, a
print(a, b) # Output: 10 5

For all resources and cheat sheets, check out my Telegram channel: https://t.iss.one/pythonproz

Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a

Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

Hope it helps :)

If you're serious about getting into Data Science with Python, follow this 5-step roadmap.

Each phase builds on the previous one, so don’t rush.

Take your time, build projects, and keep moving forward.

Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.

✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).

Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.

✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.

Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.

✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.

Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.

✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.

Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.

✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()

– Final Checkpoint:

Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub

Don’t just complete tutorialsm create things.

Explain your work.
Build your GitHub.
Write a blog.

That’s how you go from “learning” to “landing a job

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

All the best 👍👍

Who is Data Scientist?

He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.

A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:

Determines correct datasets and variables.

Identifies the most challenging data-analytics problems.

Collects large sets of data- structured and unstructured, from different sources.

Cleans and validates data ensuring accuracy, completeness, and uniformity.

Builds and applies models and algorithms to mine stores of big data.

Analyzes data to recognize patterns and trends.

Interprets data to find solutions.

Communicates findings to stakeholders using tools like visualization.