Here are 10 popular programming languages based on versatile, widely-used, and in-demand languages:

1. Python – Ideal for beginners and professionals; used in web development, data analysis, AI, and more.

2. Java – A classic language for building enterprise applications, Android apps, and large-scale systems.

3. C – The foundation for many other languages; great for understanding low-level programming concepts.

4. C++ – Popular for game development, competitive programming, and performance-critical applications.

5. C# – Widely used for Windows applications, game development (Unity), and enterprise software.

6. Go (Golang) – A modern language designed for performance and scalability, popular in cloud services.

7. Rust – Known for its safety and performance, ideal for system-level programming.

8. Kotlin – The preferred language for Android development with modern features.

9. Swift – Used for developing iOS and macOS applications with simplicity and power.

10. PHP – A staple for web development, powering many websites and applications.

Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio:

1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.

2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.

3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.

4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.

5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.

6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.

7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.

8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.

By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.

𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:

Join for more: https://t.iss.one/sqlanalyst

1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/

2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/

3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT

4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv

5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf

6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG

7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7

8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8

I have curated the list of useful Coding Channels on WhatsApp:

1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L


2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17


3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502


4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X


5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s


6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32


7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z


8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E


9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y


10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D


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


12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43

ENJOY LEARNING 👍👍

Amazon Interview Process for Data Scientist position

📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.

After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).

📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.

📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.

📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.

📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.

📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.

So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)

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

ENJOY LEARNING 👍👍

Here are some essential SQL tips for beginners 👇👇

◆ Primary Key = Unique Key + Not Null constraint
◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’
◆ LIKE operator is for string data type
◆ COUNT(*), COUNT(1), COUNT(0) all are same
◆ All aggregate functions ignore the NULL values
◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
◆ For row level filtration use WHERE and aggregate level filtration use HAVING
◆ UNION ALL will include duplicates where as UNION excludes duplicates 
◆ If the results will not have any duplicates, use UNION ALL instead of UNION
◆ We have to alias the subquery if we are using the columns in the outer select query
◆ Subqueries can be used as output with NOT IN condition.
◆ CTEs look better than subqueries. Performance wise both are same.
◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
◆ Window functions work at ROW level.
◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.

Like for more 😄😄

Complete Roadmap to learn Generative AI in 2 months 👇👇

Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.

Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.

Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.

Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.

Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.

Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.

2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.

Best Resources to learn Generative AI 👇👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Unlock the power of Generative AI Models

Machine Learning with Python Free Course

Deep Learning Nanodegree Program with Real-world Projects

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

Artificial Intelligence isn't easy!

It’s the cutting-edge field that enables machines to think, learn, and act like humans.

To truly master Artificial Intelligence, focus on these key areas:

0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.


1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.


2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.


3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.


4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).


5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.


6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.


7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.


8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.


9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms.



Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.

💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.

⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems!

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 😊

Recent Interview Question for Data Analyst Role

Question 1) You have two tables:

Employee:-
Columns: EID (Employee ID), ESalary (Employee Salary)

empdetails:-
Columns: EID (Employee ID), EDOB (Employee Date of Birth)

Your task is to:
1) Identify all employees whose salary (ESalary) is an odd number?
2) Retrieve the date of birth (EDOB) for these employees from the empdetails table.

How would you write a SQL query to achieve this?

SELECT e.EID, ed.EDOB
FROM (
SELECT EID
FROM Employee
WHERE ESalary % 2 <> 0
) e
JOIN empdetails ed ON e.EID = ed.EID;

Explanation of the query :-

Filter Employees with Odd Salaries:

The subquery SELECT EID FROM Employee WHERE ESalary % 2 <> 0 filters out Employee IDs (EID) where the salary (ESalary) is an odd number. The modulo operator % checks if ESalary divided by 2 leaves a remainder (<>0).

Merge with empdetails:

The main query then takes the filtered Employee IDs from the subquery and performs a join with the empdetails table using the EID column. This retrieves the date of birth (EDOB) for these employees.

Join this channel to learn everything about Data Analytics 👇
https://t.iss.one/sqlspecialist

Hope this helps you 😊

Machine Learning Algorithm:

1. Linear Regression:
   - Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets.

2. Decision Trees:
   - Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers.

3. Random Forest:
   - Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision.

4. Support Vector Machines (SVM):
   - Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them.

5. k-Nearest Neighbors (kNN):
   - Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one.

6. Naive Bayes:
   - Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit.

7. K-Means Clustering:
   - Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another.

8. Hierarchical Clustering:
   - Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities.

9. Principal Component Analysis (PCA):
   - Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily.

10. Neural Networks (Deep Learning):
    - Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors.

11. Gradient Boosting algorithms:
    - Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.

Share with credits: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

10 AI Side Hustles You Can Start Today

✅ Prompt Engineering Services – Craft prompts for businesses using ChatGPT or Claude
✅ AI-Powered Resume Writer – Help people optimize resumes using GPT + design tools
✅ YouTube Script Generator – Offer scriptwriting using LLMs for creators & influencers
✅ AI Course Creation – Build and sell niche courses powered by AI tools (ChatGPT + Canva)
✅ Copywriting & SEO Services – Use AI to generate blog posts, ad copy, and product descriptions
✅ Newsletter Curation – Launch an AI-generated niche newsletter using curated content
✅ Chatbot Development – Build custom AI chatbots for small businesses
✅ Voiceover Generator – Convert scripts into realistic voiceovers for YouTube shorts or reels
✅ AI Art & Merch Store – Design AI-generated art and sell it on print-on-demand platforms
✅ Data Labeling & AI Testing – Offer manual or semi-automated labeling to startups training models

React if you’re thinking of monetizing your AI skills!

#aiskills