This post is for beginners who decided to learn Data Science. I want to tell you that becoming a data scientist is a journey (6 months - 1 year at least) and not a 1 month thing where u do some courses and you are a data scientist. There are different fields in Data Science that you have to first get familiar and strong in basics as well as do hands-on to get the abilities that are required to function in a full time job opportunity. Then further delve into advanced implementations.

There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:

Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.

This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.

You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - check & search in this channel with #freecourses

Data Science - https://365datascience.pxf.io/q4m66g
Python - https://bit.ly/45rlWZE
Kaggle - https://www.kaggle.com/learn

Voice Recorder in Python

Many people pay too much to learn Python, but my mission is to break down barriers. I have shared complete learning series to learn Python from scratch.

Here are the links to the Python series

Complete Python Topics for Data Analyst: https://t.me/sqlspecialist/548

Part-1: https://t.me/sqlspecialist/562

Part-2: https://t.me/sqlspecialist/564

Part-3: https://t.me/sqlspecialist/565

Part-4: https://t.me/sqlspecialist/566

Part-5: https://t.me/sqlspecialist/568

Part-6: https://t.me/sqlspecialist/570

Part-7: https://t.me/sqlspecialist/571

Part-8: https://t.me/sqlspecialist/572

Part-9: https://t.me/sqlspecialist/578

Part-10: https://t.me/sqlspecialist/577

Part-11: https://t.me/sqlspecialist/578

Part-12:
https://t.me/sqlspecialist/581

Part-13: https://t.me/sqlspecialist/583

Part-14: https://t.me/sqlspecialist/584

Part-15: https://t.me/sqlspecialist/585

I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.

But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.

Complete SQL Topics for Data Analysts: https://t.me/sqlspecialist/523

Complete Power BI Topics for Data Analysts: https://t.me/sqlspecialist/588

I'll continue with learning series on Excel & Tableau.

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Hope it helps :)

Here is an A-Z list of essential programming terms:

1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations.

2. Boolean: A data type that represents true or false values.

3. Conditional Statement: A statement that executes different code based on a condition.

4. Debugging: The process of identifying and fixing errors or bugs in a program.

5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions.

6. Function: A block of code that performs a specific task and can be called multiple times in a program.

7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus.

8. HTML (Hypertext Markup Language): The standard markup language used to create web pages.

9. Integer: A data type that represents whole numbers without any fractional part.

10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application.

11. Loop: A programming construct that allows repeating a block of code multiple times.

12. Method: A function that is associated with an object in object-oriented programming.

13. Null: A special value that represents the absence of a value.

14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior.

15. Pointer: A variable that stores the memory address of another variable.

16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle.

17. Recursion: A programming technique where a function calls itself to solve a problem.

18. String: A data type that represents a sequence of characters.

19. Tuple: An ordered collection of elements, similar to an array but immutable.

20. Variable: A named storage location in memory that holds a value.

21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true.

Best Programming Resources: https://topmate.io/coding/898340

Join for more: https://t.me/programming_guide

ENJOY LEARNING 👍👍

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 (𝗡𝗼 𝗦𝘁𝗿𝗶𝗻𝗴𝘀 𝗔𝘁𝘁𝗮𝗰𝗵𝗲𝗱)

𝗡𝗼 𝗳𝗮𝗻𝗰𝘆 𝗰𝗼𝘂𝗿𝘀𝗲𝘀, 𝗻𝗼 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀, 𝗷𝘂𝘀𝘁 𝗽𝘂𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.

𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘:

1️⃣ Python Programming for Data Science → Harvard’s CS50P
The best intro to Python for absolute beginners:
↬ Covers loops, data structures, and practical exercises.
↬ Designed to help you build foundational coding skills.

Link: https://cs50.harvard.edu/python/

https://t.me/datasciencefun

2️⃣ Statistics & Probability → Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
↬ Clear, beginner-friendly videos.
↬ Exercises to test your skills.

Link: https://www.khanacademy.org/math/statistics-probability

https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

3️⃣ Linear Algebra for Data Science → 3Blue1Brown
↬ Learn about matrices, vectors, and transformations.
↬ Essential for machine learning models.

Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr

4️⃣ SQL Basics → Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
↬ Writing queries, joins, and filtering data.
↬ Real-world datasets to practice.

Link: https://mode.com/sql-tutorial

https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

5️⃣ Data Visualization → freeCodeCamp
Learn to create stunning visualizations using Python libraries:
↬ Covers Matplotlib, Seaborn, and Plotly.
↬ Step-by-step projects included.

Link: https://www.youtube.com/watch?v=JLzTJhC2DZg

https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34

6️⃣ Machine Learning Basics → Google’s Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
↬ Learn supervised and unsupervised learning.
↬ Hands-on coding with TensorFlow.

Link: https://developers.google.com/machine-learning/crash-course

7️⃣ Deep Learning → Fast.ai’s Free Course
Fast.ai makes deep learning easy and accessible:
↬ Build neural networks with PyTorch.
↬ Learn by coding real projects.

Link: https://course.fast.ai/

8️⃣ Data Science Projects → Kaggle
↬ Compete in challenges to practice your skills.
↬ Great way to build your portfolio.

Link: https://www.kaggle.com/

Here are 10 project ideas to work on for Data Analytics

1. Customer Churn Prediction: Predict customer churn for subscription-based services. Skills: EDA, classification models. Tools: Python, Scikit-Learn.
2. Retail Sales Forecasting: Forecast sales using historical data. Skills: Time series analysis. Tools: Python, Statsmodels.
3. Sentiment Analysis: Analyze sentiments in product reviews or tweets. Skills: Text processing, NLP. Tools: Python, NLTK.
4. Loan Approval Prediction: Predict loan approvals based on credit risk. Skills: Classification models. Tools: Python, Scikit-Learn.
5. COVID-19 Data Analysis: Explore and visualize COVID-19 trends. Skills: EDA, visualization. Tools: Python, Tableau.
6. Traffic Accident Analysis: Discover patterns in traffic accidents. Skills: Clustering, heatmaps. Tools: Python, Folium.
7. Movie Recommendation System: Build a recommendation system using user ratings. Skills: Collaborative filtering. Tools: Python, Scikit-Learn.
8. E-commerce Analysis: Analyze top-performing products in e-commerce. Skills: EDA, association rules. Tools: Python, Apriori.
9. Stock Market Analysis: Analyze stock trends using historical data. Skills: Moving averages, sentiment analysis. Tools: Python, Matplotlib.
10. Employee Attrition Analysis: Predict employee turnover. Skills: Classification models, HR analytics. Tools: Python, Scikit-Learn.

And this is how you can work on

Here’s a compact list of free resources for working on data analytics projects:

1. Datasets
• Kaggle Datasets: Wide range of datasets and community discussions.
• UCI Machine Learning Repository: Great for educational datasets.
• Data.gov: U.S. government datasets (e.g., traffic, COVID-19).
2. Learning Platforms
• YouTube: Channels like Data School and freeCodeCamp for tutorials.
• 365DataScience: Data Science & AI Related Courses
3. Tools
• Google Colab: Free Jupyter Notebooks for Python coding.
• Tableau Public & Power BI Desktop: Free data visualization tools.
4. Project Resources
• Kaggle Notebooks & GitHub: Code examples and project walk-throughs.
• Data Analytics on Medium: Project guides and tutorials.

ENJOY LEARNING ✅️✅️

#datascienceprojects