Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?

In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.

Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.

Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.

Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.

Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.

I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.

𝟰 𝗛𝗶𝗴𝗵-𝗜𝗺𝗽𝗮𝗰𝘁 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

These globally recognized certifications from platforms like Google, IBM, Microsoft, and DataCamp are beginner-friendly, industry-aligned, and designed to make you job-ready in just a few weeks

𝐋𝐢𝐧𝐤👇:-

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These courses help you gain hands-on experience — exactly what top MNCs look for!✅️

Today let's understand the fascinating world of Data Science from start.

## What is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In simpler terms, data science involves obtaining, processing, and analyzing data to gain insights for various purposes¹².

### The Data Science Lifecycle

The data science lifecycle refers to the various stages a data science project typically undergoes. While each project is unique, most follow a similar structure:

1. Data Collection and Storage:
- In this initial phase, data is collected from various sources such as databases, Excel files, text files, APIs, web scraping, or real-time data streams.
- The type and volume of data collected depend on the specific problem being addressed.
- Once collected, the data is stored in an appropriate format for further processing.

2. Data Preparation:
- Often considered the most time-consuming phase, data preparation involves cleaning and transforming raw data into a suitable format for analysis.
- Tasks include handling missing or inconsistent data, removing duplicates, normalization, and data type conversions.
- The goal is to create a clean, high-quality dataset that can yield accurate and reliable analytical results.

3. Exploration and Visualization:
- During this phase, data scientists explore the prepared data to understand its patterns, characteristics, and potential anomalies.
- Techniques like statistical analysis and data visualization are used to summarize the data's main features.
- Visualization methods help convey insights effectively.

4. Model Building and Machine Learning:
- This phase involves selecting appropriate algorithms and building predictive models.
- Machine learning techniques are applied to train models on historical data and make predictions.
- Common tasks include regression, classification, clustering, and recommendation systems.

5. Model Evaluation and Deployment:
- After building models, they are evaluated using metrics such as accuracy, precision, recall, and F1-score.
- Once satisfied with the model's performance, it can be deployed for real-world use.
- Deployment may involve integrating the model into an application or system.

### Why Data Science Matters

- Business Insights: Organizations use data science to gain insights into customer behavior, market trends, and operational efficiency. This informs strategic decisions and drives business growth.

- Healthcare and Medicine: Data science helps analyze patient data, predict disease outbreaks, and optimize treatment plans. It contributes to personalized medicine and drug discovery.

- Finance and Risk Management: Financial institutions use data science for fraud detection, credit scoring, and risk assessment. It enhances decision-making and minimizes financial risks.

- Social Sciences and Public Policy: Data science aids in understanding social phenomena, predicting election outcomes, and optimizing public services.

- Technology and Innovation: Data science fuels innovations in artificial intelligence, natural language processing, and recommendation systems.

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

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

Like if you need similar content 😄👍

Hope this helps you 😊

Top 7 Must-Prepare Topics for Coding Interviews (2025 Edition)

✅ Arrays & Strings – Master problems on rotation, sliding window, two pointers, etc.
✅ Linked Lists – Practice reversal, cycle detection, and merging lists
✅ Hashing & Maps – Use hash tables for fast lookups and frequency-based problems
✅ Recursion & Backtracking – Solve problems like permutations, subsets, and Sudoku
✅ Dynamic Programming – Understand memoization, tabulation, and classic patterns
✅ Trees & Graphs – Cover traversal (BFS/DFS), shortest paths, and tree operations
✅ Stacks & Queues – Solve problems involving monotonic stacks, parentheses, and sliding windows

These are the essentials to crack FAANG-level interviews or product-based companies.

𝟭𝟬𝟬𝟬+ 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗜𝗻𝗳𝗼𝘀𝘆𝘀 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄, 𝗦𝘂𝗰𝗰𝗲𝗲𝗱!😍

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Save this blog, sign up, and start your upskilling journey today!✅️

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 😄👍

𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: 𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍

🚀 Want to break into tech or data analytics but don’t know how to start?📌✨️

Python is the #1 most in-demand programming language, and Scaler’s free Python for Beginners course is a game-changer for absolute beginners📊✔️

𝐋𝐢𝐧𝐤👇:-

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No coding background needed!✅️

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

React ❤️ for more

𝟭𝟬𝟬% 𝗙𝗿𝗲𝗲 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4e76jMX

Enroll For FREE & Get Certified!✅️

𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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

You don’t need a fancy degree or expensive course to begin your ML journey📊

𝐋𝐢𝐧𝐤👇:-

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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

𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀: 𝟱 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍

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.💫📲

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3SU5FJ0

No prior experience needed!✅️

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.