If you're a data science beginner, Python is the best programming language to get started.

Here are 7 Python libraries for data science you need to know if you want to learn:

- Data analysis
- Data visualization
- Machine learning
- Deep learning

NumPy

NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Pandas

Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.

Matplotlib

Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.

Scikit-learn

Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.

Seaborn

Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.

TensorFlow or PyTorch

TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.

SciPy

Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.

Enjoy 😄👍

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Step-by-Step Roadmap to Learn Data Science in 2025:

Step 1: Understand the Role
A data scientist in 2025 is expected to:

Analyze data to extract insights

Build predictive models using ML

Communicate findings to stakeholders

Work with large datasets in cloud environments


Step 2: Master the Prerequisite Skills

A. Programming

Learn Python (must-have): Focus on pandas, numpy, matplotlib, seaborn, scikit-learn

R (optional but helpful for statistical analysis)

SQL: Strong command over data extraction and transformation


B. Math & Stats

Probability, Descriptive & Inferential Statistics

Linear Algebra & Calculus (only what's necessary for ML)

Hypothesis testing


Step 3: Learn Data Handling

Data Cleaning, Preprocessing

Exploratory Data Analysis (EDA)

Feature Engineering

Tools: Python (pandas), Excel, SQL


Step 4: Master Machine Learning

Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost

Unsupervised Learning: K-Means, Hierarchical Clustering, PCA

Deep Learning (optional): Use TensorFlow or PyTorch

Evaluation Metrics: Accuracy, AUC, Confusion Matrix, RMSE


Step 5: Learn Data Visualization & Storytelling

Python (matplotlib, seaborn, plotly)

Power BI / Tableau

Communicating insights clearly is as important as modeling


Step 6: Use Real Datasets & Projects

Work on projects using Kaggle, UCI, or public APIs

Examples:

Customer churn prediction

Sales forecasting

Sentiment analysis

Fraud detection



Step 7: Understand Cloud & MLOps (2025+ Skills)

Cloud: AWS (S3, EC2, SageMaker), GCP, or Azure

MLOps: Model deployment (Flask, FastAPI), CI/CD for ML, Docker basics


Step 8: Build Portfolio & Resume

Create GitHub repos with well-documented code

Post projects and blogs on Medium or LinkedIn

Prepare a data science-specific resume


Step 9: Apply Smartly

Focus on job roles like: Data Scientist, ML Engineer, Data Analyst → DS

Use platforms like LinkedIn, Glassdoor, Hirect, AngelList, etc.

Practice data science interviews: case studies, ML concepts, SQL + Python coding


Step 10: Keep Learning & Updating

Follow top newsletters: Data Elixir, Towards Data Science

Read papers (arXiv, Google Scholar) on trending topics: LLMs, AutoML, Explainable AI

Upskill with certifications (Google Data Cert, Coursera, DataCamp, Udemy)

Free Resources to learn Data Science

Kaggle Courses: https://www.kaggle.com/learn

CS50 AI by Harvard: https://cs50.harvard.edu/ai/

Fast.ai: https://course.fast.ai/

Google ML Crash Course: https://developers.google.com/machine-learning/crash-course

Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998

Data Science Books: https://t.iss.one/datalemur

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From data science and AI to web development and cloud computing, checkout Top 5 Websites for Free Tech Certification Courses in 2025

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🎯 Want to break into Machine Learning but don’t know where to start?✨️

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This list is for anyone ready to start learning ML from scratch✅️

Want to get started with System design interview preparation, start with these 👇

1. Learn to understand requirements
2. Learn the difference between horizontal and vertical scaling.
3. Study latency and throughput trade-offs and optimization techniques.
4. Understand the CAP Theorem (Consistency, Availability, Partition Tolerance).
5. Learn HTTP/HTTPS protocols, request-response lifecycle, and headers.
6. Understand DNS and how domain resolution works.
7. Study load balancers, their types (Layer 4 and Layer 7), and algorithms.
8. Learn about CDNs, their use cases, and caching strategies.
9. Understand SQL databases (ACID properties, normalization) and NoSQL types (key–value, document, graph).
10. Study caching tools (Redis, Memcached) and strategies (write-through, write-back, eviction policies).
11. Learn about blob storage systems like S3 or Google Cloud Storage.
12. Study sharding and horizontal partitioning of databases.
13. Understand replication (leader–follower, multi-leader) and consistency models.
14. Learn failover mechanisms like active-passive and active-active setups.
15. Study message queues like RabbitMQ, Kafka, and SQS.
16. Understand consensus algorithms such as Paxos and Raft.
17. Learn event-driven architectures, Pub/Sub models, and event sourcing.
18. Study distributed transactions (two-phase commit, sagas).
19. Learn rate-limiting techniques (token bucket, leaky bucket algorithms).
20. Study API design principles for REST, GraphQL, and gRPC.
21. Understand microservices architecture, communication, and trade-offs with monoliths.
22. Learn authentication and authorization methods (OAuth, JWT, SSO).
23. Study metrics collection tools like Prometheus or Datadog.
24. Understand logging systems (e.g., ELK stack) and tracing tools (OpenTelemetry, Jaeger).
25.Learn about encryption (data at rest and in transit) and rate-limiting for security.
26. And then practise the most commonly asked questions like URL shorteners, chat systems, ride-sharing apps, search engines, video streaming, and e-commerce websites

Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

Python interview questions

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Want to break into Data Science but don’t know where to begin?👨‍💻📌

You’re not alone. Data Science is one of the most in-demand fields today, but with so many courses online, it can feel overwhelming.💫📲

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Have you ever thought about this?... 🤔

When you think about the data scientist role, you probably think about AI and fancy machine learning models. And when you think about the data analyst role, you probably think about good-looking dashboards with plenty of features and insights.

Well, this all looks good until you land a job, and you quickly realize that you will spend probably 60-70% of your time doing something that is called DATA CLEANING... which I agree, it’s not the sexiest topic to talk about.

The thing is that logically, if we spend so much time preparing our data before creating a dashboard or a machine learning model, this means that data cleaning becomes arguably the number one skill for data specialists. And this is exactly why today we will start a series about the most important data cleaning techniques that you will use in the workplace.

So, here is why we need to clean our data 👇🏻

1️⃣ Precision in Analysis: Clean data minimizes errors and ensures accurate results, safeguarding the integrity of the analytical process.
2️⃣ Maintaining Professional Credibility: The validity of your findings impacts your reputation in data science; unclean data can jeopardize your credibility.
3️⃣ Optimizing Computational Efficiency: Well-formatted data streamlines analysis, akin to a decluttered workspace, making processes run faster, especially with advanced algorithms.

𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄😍

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𝗝𝗮𝘃𝗮  :- https://pdlink.in/4dWkAMf

𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j

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𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :-  https://pdlink.in/4dFem3o

𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw

Get Your Dream Tech Job In Your Dream Company💫

If you want to grow, keep these 5 tips in mind:

1. Understand that real change takes time—stay patient.

2. Make learning a daily habit, even if it’s just a little.

3. Choose friends who push you to improve, not just those who agree.

4. Reflect on your progress—celebrate every step forward.

5. Be mindful of your daily habits—they shape who you become.

Free Programming and Data Analytics Resources 👇👇

✅ Data science and Data Analytics Free Courses by Google

https://developers.google.com/edu/python/introduction

https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field

https://cloud.google.com/data-science?hl=en

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

https://t.iss.one/datasciencefun/1371

🔍 Free Data Analytics Courses by Microsoft

1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/

2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/

3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

🤖 Free AI Courses by Microsoft

1. Fundamentals of AI by Microsoft

https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

2. Introduction to AI with python by Harvard.

https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python

📚 Useful Resources for the Programmers

Data Analyst Roadmap
https://t.iss.one/sqlspecialist/94

Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019

Interactive React Native Resources
https://fullstackopen.com/en/part10

Python for Data Science and ML
https://t.iss.one/datasciencefree/68

Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3

Unity Documentation
https://docs.unity3d.com/Manual/index.html

Advanced Javascript concepts
https://t.iss.one/Programming_experts/72

Oops in Java
https://nptel.ac.in/courses/106105224

Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction

Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76

Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em

Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single

🍻 Free Programming Courses by Microsoft

❯ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/

❯ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/

❯ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07

Join @free4unow_backup for more free resources.

ENJOY LEARNING 👍👍

Core data science concepts you should know:

🔢 1. Statistics & Probability

Descriptive statistics: Mean, median, mode, standard deviation, variance

Inferential statistics: Hypothesis testing, confidence intervals, p-values, t-tests, ANOVA

Probability distributions: Normal, Binomial, Poisson, Uniform

Bayes' Theorem

Central Limit Theorem


📊 2. Data Wrangling & Cleaning

Handling missing values

Outlier detection and treatment

Data transformation (scaling, encoding, normalization)

Feature engineering

Dealing with imbalanced data


📈 3. Exploratory Data Analysis (EDA)

Univariate, bivariate, and multivariate analysis

Correlation and covariance

Data visualization tools: Matplotlib, Seaborn, Plotly

Insights generation through visual storytelling


🤖 4. Machine Learning Fundamentals

Supervised Learning: Linear regression, logistic regression, decision trees, SVM, k-NN

Unsupervised Learning: K-means, hierarchical clustering, PCA

Model evaluation: Accuracy, precision, recall, F1-score, ROC-AUC

Cross-validation and overfitting/underfitting

Bias-variance tradeoff


🧠 5. Deep Learning (Basics)

Neural networks: Perceptron, MLP

Activation functions (ReLU, Sigmoid, Tanh)

Backpropagation

Gradient descent and learning rate

CNNs and RNNs (intro level)


🗃️ 6. Data Structures & Algorithms (DSA)

Arrays, lists, dictionaries, sets

Sorting and searching algorithms

Time and space complexity (Big-O notation)

Common problems: string manipulation, matrix operations, recursion


💾 7. SQL & Databases

SELECT, WHERE, GROUP BY, HAVING

JOINS (inner, left, right, full)

Subqueries and CTEs

Window functions

Indexing and normalization


📦 8. Tools & Libraries

Python: pandas, NumPy, scikit-learn, TensorFlow, PyTorch

R: dplyr, ggplot2, caret

Jupyter Notebooks for experimentation

Git and GitHub for version control


🧪 9. A/B Testing & Experimentation

Control vs. treatment group

Hypothesis formulation

Significance level, p-value interpretation

Power analysis


🌐 10. Business Acumen & Storytelling

Translating data insights into business value

Crafting narratives with data

Building dashboards (Power BI, Tableau)

Knowing KPIs and business metrics

React ❤️ for more

𝟳 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 & 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍

💻 You don’t need to spend a rupee to master Python!🐍

Whether you’re an aspiring Data Analyst, Developer, or Tech Enthusiast, these 7 completely free platforms help you go from zero to confident coder👨‍💻📌

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Enjoy Learning ✅️