🔰 How to become a data scientist in 2025?

👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.


🔢 Step 1: Strengthen your math and statistics!

✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:

✅ Linear algebra: matrices, vectors, eigenvalues.

🔗 Course: MIT 18.06 Linear Algebra


✅ Calculus: derivative, integral, optimization.

🔗 Course: MIT Single Variable Calculus


✅ Statistics and probability: Bayes' theorem, hypothesis testing.

🔗 Course: Statistics 110

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🔢 Step 2: Learn to code.

✏️ Learn Python and become proficient in coding. The most important topics you need to master are:

✅ Python: Pandas, NumPy, Matplotlib libraries

🔗 Course: FreeCodeCamp Python Course

✅ SQL language: Join commands, Window functions, query optimization.

🔗 Course: Stanford SQL Course

✅ Data structures and algorithms: arrays, linked lists, trees.

🔗 Course: MIT Introduction to Algorithms

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🔢 Step 3: Clean and visualize data

✏️ Learn how to process and clean data and then create an engaging story from it!

✅ Data cleaning: Working with missing values ​​and detecting outliers.

🔗 Course: Data Cleaning

✅ Data visualization: Matplotlib, Seaborn, Tableau

🔗 Course: Data Visualization Tutorial

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🔢 Step 4: Learn Machine Learning

✏️ It's time to enter the exciting world of machine learning! You should know these topics:

✅ Supervised learning: regression, classification.

✅ Unsupervised learning: clustering, PCA, anomaly detection.

✅ Deep learning: neural networks, CNN, RNN


🔗 Course: CS229: Machine Learning

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🔢 Step 5: Working with Big Data and Cloud Technologies

✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.

✅ Big Data Tools: Hadoop, Spark, Dask

✅ Cloud platforms: AWS, GCP, Azure

🔗 Course: Data Engineering

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🔢 Step 6: Do real projects!

✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.

✅ Kaggle competitions: solving real-world challenges.

✅ End-to-End projects: data collection, modeling, implementation.

✅ GitHub: Publish your projects on GitHub.

🔗 Platform: Kaggle🔗 Platform: ods.ai

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🔢 Step 7: Learn MLOps and deploy models

✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.

✅ MLOps training: model versioning, monitoring, model retraining.

✅ Deployment models: Flask, FastAPI, Docker

🔗 Course: Stanford MLOps Course

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🔢 Step 8: Stay up to date and network

✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.

✅ Read scientific articles: arXiv, Google Scholar

✅ Connect with the data community:

🔗 Site: Papers with code
🔗 Site: AI Research at Google

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data

👨‍💻 𝟓 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐤𝐢𝐥𝐥𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐍𝐞𝐞𝐝𝐬 𝐢𝐧 𝐚𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📊

🔸𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 & 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
You need to understand two main types of machine learning: supervised learning (used for predicting outcomes, like whether a customer will buy a product) and unsupervised learning (used to find patterns, like grouping customers based on buying behavior).

🔸𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
This is about turning raw data into useful information for your model. Knowing how to clean data, fill missing values, and create new features will improve the model's performance.

🔸𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
It’s important to know how to check if a model is working well. Use simple measures like accuracy (how often the model is right), precision, and recall to assess your model’s performance.

🔸𝐅𝐚𝐦𝐢𝐥𝐢𝐚𝐫𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬
Get to know basic machine learning algorithms like Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). These are often used for solving real-world problems and can help you choose the best approach.

🔸𝐃𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
Once you’ve built a model, it’s important to know how to use it in the real world. Learn how to deploy models so they can be used by others in your organization and continue to make decisions automatically.

🔍 𝐏𝐫𝐨 𝐓𝐢𝐩: Keep practicing by working on real projects or using online platforms to improve these skills!

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

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Hope this helps you 😊

#ai #datascience

I often get asked- what's the BEST Certification for #datascience or #machinelearning?

👉My answer is: none

The reality is that certification don't matter for data science.

This is not commerce. we are not using the same techniques over and over again to solve well-defined problems.

The problems are challenging, the data is messy and numerous techniques are used.

So if you've wondering which certification you should get, Save yourself,some mental energy and stop thinking about it- they are not really matter.

👉 Instead, grab a dataset and start playing with it.

👉 Start applying what you know and trying to solve interesting problems, learn something new every day.

👉 Here are few places to grab datasets to get you started



Google: https://toolbox.google.com/datasetsearch
Kaggle: https://www.kaggle.com/datasets
US Government Dataset: www.data.gov
Quandl: https://www.quandl.com/
UCI
ML repo: http://mlr.cs.umass.edu/ml/datasets.html
World Bank🏦: https://data.worldbank.org/