🚀 𝗧𝗼𝗽 𝟱 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 | 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 😍

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“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford

https://datasimplifier.com/best-data-analyst-projects-for-freshers/

https://toolbox.google.com/datasetsearch

https://www.kaggle.com/datasets

http://mlr.cs.umass.edu/ml/

https://www.visualdata.io/

https://guides.library.cmu.edu/machine-learning/datasets

https://www.data.gov/

https://nces.ed.gov/

https://www.ukdataservice.ac.uk/

https://datausa.io/

https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

https://www.kaggle.com/xiuchengwang/python-dataset-download

https://www.quandl.com/

https://data.worldbank.org/

https://www.imf.org/en/Data

https://markets.ft.com/data/

https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0

https://www.aeaweb.org/resources/data/us-macro-regional

http://xviewdataset.org/#dataset

http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php

http://image-net.org/

http://cocodataset.org/

http://visualgenome.org/

https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1

http://vis-www.cs.umass.edu/lfw/

http://vision.stanford.edu/aditya86/ImageNetDogs/

http://web.mit.edu/torralba/www/indoor.html

http://www.cs.jhu.edu/~mdredze/datasets/sentiment/

http://ai.stanford.edu/~amaas/data/sentiment/

http://nlp.stanford.edu/sentiment/code.html

http://help.sentiment140.com/for-students/

https://www.kaggle.com/crowdflower/twitter-airline-sentiment

https://hotpotqa.github.io/

https://www.cs.cmu.edu/~./enron/

https://snap.stanford.edu/data/web-Amazon.html

https://aws.amazon.com/datasets/google-books-ngrams/

http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm

https://code.google.com/archive/p/wiki-links/downloads

http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/

https://www.yelp.com/dataset

https://t.me/DataPortfolio/2

https://archive.ics.uci.edu/ml/datasets/Spambase

https://bdd-data.berkeley.edu/

http://apolloscape.auto/

https://archive.org/details/comma-dataset

https://www.cityscapes-dataset.com/

http://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset

http://www.vision.ee.ethz.ch/~timofter/traffic_signs/

http://cvrr.ucsd.edu/LISA/datasets.html

https://hci.iwr.uni-heidelberg.de/node/6132

http://www.lara.prd.fr/benchmarks/trafficlightsrecognition

http://computing.wpi.edu/dataset.html

https://mimic.physionet.org/

✅ Best Telegram channels to get free coding & data science resources
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🔅SQL Revision Notes for Interview💡

𝐒𝐭𝐚𝐫𝐭 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 — 𝟏𝟎𝟎% 𝐅𝐫𝐞𝐞 & 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲😍

Want to dive into data analytics but don’t know where to start?🧑‍💻✨️

These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.📊📌

𝐋𝐢𝐧𝐤👇:-

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No prior experience needed — just curiosity✅️

Please go through this top 5 SQL projects with Datasets that you can practice and can add in your resume

🚀1. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)

🚀2. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)

📌3. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)

🚀4. Inventory Management:
(https://www.kaggle.com/code/govindji/inventory-management)


🚀 5. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)

Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.

Hope this piece of information helps you

Join for more -> https://t.me/addlist/4q2PYC0pH_VjZDk5

ENJOY LEARNING 👍👍

📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗶𝗻 𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱/𝗣𝘂𝗻𝗲 😍

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𝐁𝐞𝐬𝐭 𝐖𝐚𝐲 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐒𝐐𝐋 𝐢𝐧 𝟐𝟎𝟐𝟓 — 𝐅𝐫𝐞𝐞 𝐂𝐨𝐮𝐫𝐬𝐞𝐬, 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐒𝐢𝐭𝐞𝐬 & 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐏𝐫𝐞𝐩 😍

Whether you’re aiming for a data analytics career or preparing for top tech interviews, SQL is a non-negotiable skill🧑‍🎓✨️

With the right roadmap, you can go from absolute beginner to confident pro—without spending a single rupee.💰💥

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All The Best 🎊

Complete 3-months roadmap to learn Artificial Intelligence (AI) 👇👇

### Month 1: Fundamentals of AI and Python

Week 1: Introduction to AI
- Key Concepts: What is AI? Categories (Narrow AI, General AI, Super AI), Applications of AI.
- Reading: Research papers and articles on AI.
- Task: Watch introductory AI videos (e.g., Andrew Ng's "What is AI?" on Coursera).

Week 2: Python for AI
- Skills: Basics of Python programming (variables, loops, conditionals, functions, OOP).
- Resources: Python tutorials (W3Schools, Real Python).
- Task: Write simple Python scripts.

Week 3: Libraries for AI
- Key Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
- Task: Install libraries and practice data manipulation and visualization.
- Resources: Documentation and tutorials on these libraries.

Week 4: Linear Algebra and Probability
- Key Topics: Matrices, Vectors, Eigenvalues, Probability theory.
- Resources: Khan Academy (Linear Algebra), MIT OCW.
- Task: Solve basic linear algebra problems and write Python functions to implement them.

---

### Month 2: Core AI Techniques & Machine Learning

Week 5: Machine Learning Basics
- Key Concepts: Supervised, Unsupervised learning, Model evaluation metrics.
- Algorithms: Linear Regression, Logistic Regression.
- Task: Build basic models using Scikit-learn.
- Resources: Coursera’s Machine Learning by Andrew Ng, Kaggle datasets.

Week 6: Decision Trees, Random Forests, and KNN
- Key Concepts: Decision Trees, Random Forests, K-Nearest Neighbors (KNN).
- Task: Implement these algorithms and analyze their performance.
- Resources: Hands-on Machine Learning with Scikit-learn.

Week 7: Neural Networks & Deep Learning
- Key Concepts: Artificial Neurons, Forward and Backpropagation, Activation Functions.
- Framework: TensorFlow, Keras.
- Task: Build a simple neural network for a classification problem.
- Resources: Fast.ai, Coursera Deep Learning Specialization by Andrew Ng.

Week 8: Convolutional Neural Networks (CNN)
- Key Concepts: Image classification, Convolution, Pooling.
- Task: Build a CNN using Keras/TensorFlow to classify images (e.g., CIFAR-10 dataset).
- Resources: CS231n Stanford Course, Fast.ai Computer Vision.

---

### Month 3: Advanced AI Techniques & Projects

Week 9: Natural Language Processing (NLP)
- Key Concepts: Tokenization, Embeddings, Sentiment Analysis.
- Task: Implement text classification using NLTK/Spacy or transformers.
- Resources: Hugging Face, Coursera NLP courses.

Week 10: Reinforcement Learning
- Key Concepts: Q-learning, Markov Decision Processes (MDP), Policy Gradients.
- Task: Solve a simple RL problem (e.g., OpenAI Gym).
- Resources: Sutton and Barto’s book on Reinforcement Learning, OpenAI Gym.

Week 11: AI Model Deployment
- Key Concepts: Model deployment using Flask/Streamlit, Model Serving.
- Task: Deploy a trained model using Flask API or Streamlit.
- Resources: Heroku deployment guides, Streamlit documentation.

Week 12: AI Capstone Project
- Task: Create a full-fledged AI project (e.g., Image recognition app, Sentiment analysis, or Chatbot).
- Presentation: Prepare and document your project.
- Goal: Deploy your AI model and share it on GitHub/Portfolio.

### Tools and Platforms:
- Python IDE: Jupyter, PyCharm, or VSCode.
- Datasets: Kaggle, UCI Machine Learning Repository.
- Version Control: GitHub or GitLab for managing code.

Free Books and Courses to Learn Artificial Intelligence👇👇

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Artificial Intelligence: Foundations of Computational Agents Free Book

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Amazing AI Reverse Image Search

By following this roadmap, you’ll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.

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ENJOY LEARNING 👍👍

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

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𝟓 𝐅𝐫𝐞𝐞 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐀𝐠𝐞𝐧𝐭𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐂𝐨𝐝𝐢𝐧𝐠😍

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Important Pandas topics for a data analysis interviews

👉 DataFrame and Series: Understand the fundamental data structures in pandas. A DataFrame is a 2-dimensional labeled data structure, while a Series is a 1-dimensional labeled array.

👉 Data Cleaning and Manipulation: Be able to clean and preprocess data using functions like drop, fillna, replace, and apply. Know how to filter and select specific rows and columns using conditions.
👉 Indexing and Slicing: Understand how to use various indexing techniques like label-based indexing (loc) and position-based indexing (iloc). Practice slicing data for specific rows and columns.

👉 Grouping and Aggregation: Know how to use the groupby function to group data based on certain columns and perform aggregation functions like sum, mean, count, etc.
👉 Merging and Joining: Be familiar with methods to combine multiple DataFrames using merge and join operations. Understand the different types of joins (inner, outer, left, right) and when to use them.
👉 Reshaping Data: Learn about techniques to reshape data using functions like pivot, melt, and stack/unstack. Understand the concept of wide and long data formats.
👉 Data Visualization: While not exclusive to pandas, you might need to use pandas to prepare data for visualization. Familiarize yourself with plotting functions and libraries like Matplotlib and Seaborn.
👉 Handling Dates and Time: Be comfortable working with date and time data using pandas' datetime functionality. This includes date parsing, date arithmetic, and resampling time series data.
👉 Handling Missing Data: Learn techniques to identify and handle missing data, such as using functions like isna, fillna, and considering strategies for imputation.
👉 Performance Optimization: Understand ways to optimize performance when working with large datasets, such as using vectorized operations and avoiding unnecessary loops.
👉 Reading and Writing Data: Know how to read data from various file formats (CSV, Excel, SQL databases) into pandas DataFrames and write DataFrame data back to these formats.
👉 Exploratory Data Analysis (EDA): Practice using pandas to perform basic exploratory data analysis tasks like summarizing data, calculating basic statistics, and identifying trends or patterns.

Free Resources to learn Pandas
👇👇

https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course

https://t.me/DataAnalystInterview/55?single

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https://bit.ly/3DFMgDY

https://t.me/learndataanalysis/30

Remember, the depth of your understanding in each topic will depend on the specific requirements of the interview and the role you're applying for. Practice by working on real datasets and solving data analysis problems using pandas to build your proficiency in these areas.

ENJOY LEARNING 👍👍

𝗔𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🚀

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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.

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟳 𝗗𝗮𝘆𝘀: 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗙𝗿𝗲𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗚𝗲𝘁 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆😍

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Perfect for students, freshers, and aspiring data analysts.✅️

🚀 How to Land a Data Analyst Job Without Experience?

Many people asked me this question, so I thought to answer it here to help everyone. Here is the step-by-step approach i would recommend:

✅ Step 1: Master the Essential Skills

You need to build a strong foundation in:

🔹 SQL – Learn how to extract and manipulate data
🔹 Excel – Master formulas, Pivot Tables, and dashboards
🔹 Python – Focus on Pandas, NumPy, and Matplotlib for data analysis
🔹 Power BI/Tableau – Learn to create interactive dashboards
🔹 Statistics & Business Acumen – Understand data trends and insights

Where to learn?
📌 Google Data Analytics Course
📌 SQL – Mode Analytics (Free)
📌 Python – Kaggle or DataCamp


✅ Step 2: Work on Real-World Projects

Employers care more about what you can do rather than just your degree. Build 3-4 projects to showcase your skills.

🔹 Project Ideas:

✅ Analyze sales data to find profitable products
✅ Clean messy datasets using SQL or Python
✅ Build an interactive Power BI dashboard
✅ Predict customer churn using machine learning (optional)

Use Kaggle, Data.gov, or Google Dataset Search to find free datasets!


✅ Step 3: Build an Impressive Portfolio

Once you have projects, showcase them! Create:
📌 A GitHub repository to store your SQL/Python code
📌 A Tableau or Power BI Public Profile for dashboards
📌 A Medium or LinkedIn post explaining your projects

A strong portfolio = More job opportunities! 💡


✅ Step 4: Get Hands-On Experience

If you don’t have experience, create your own!
📌 Do freelance projects on Upwork/Fiverr
📌 Join an internship or volunteer for NGOs
📌 Participate in Kaggle competitions
📌 Contribute to open-source projects

Real-world practice > Theoretical knowledge!


✅ Step 5: Optimize Your Resume & LinkedIn Profile

Your resume should highlight:
✔️ Skills (SQL, Python, Power BI, etc.)
✔️ Projects (Brief descriptions with links)
✔️ Certifications (Google Data Analytics, Coursera, etc.)

Bonus Tip:
🔹 Write "Data Analyst in Training" on LinkedIn
🔹 Start posting insights from your learning journey
🔹 Engage with recruiters & join LinkedIn groups


✅ Step 6: Start Applying for Jobs

Don’t wait for the perfect job—start applying!
📌 Apply on LinkedIn, Indeed, and company websites
📌 Network with professionals in the industry
📌 Be ready for SQL & Excel assessments

Pro Tip: Even if you don’t meet 100% of the job requirements, apply anyway! Many companies are open to hiring self-taught analysts.

You don’t need a fancy degree to become a Data Analyst. Skills + Projects + Networking = Your job offer!

🔥 Your Challenge: Start your first project today and track your progress!

Share with credits: https://t.me/sqlspecialist

Hope it helps :)

𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 - 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗧𝗲𝗰𝗵 𝗝𝗼𝗯😍

Curriculum designed and taught by Alumni from IITs & Leading Tech Companies.

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Dataset Name: Fruit Detection Dataset
Basic Description: Multilabel Fruits Detection

📖 FULL DATASET DESCRIPTION:
==================================
The dataset includes 8479 images of 6 different fruits(Apple, Grapes, Pineapple, Orange, Banana, and Watermelon). Fruits are annotated in YOLOv8 format.
The following pre-processing was applied to each image:
The following augmentation was applied to create 3 versions of each source image:
The following transformations were applied to the bounding boxes of each image:

📥 DATASET DOWNLOAD INFORMATION
==================================

🔴 Dataset Size: Download dataset as zip (525 MB)

🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/lakshaytyagi01/fruit-detection

📊 Additional information:
==================================
Total files: 17,000
Views: 26,500
Downloads: 4,298

📚 RELATED NOTEBOOKS:
==================================
1. 🍍🍌🍓 YOLO-NAS 🏎💨 Fruit Detection 🍇🍒🍊 | Upvotes: 163
   URL: https://www.kaggle.com/code/harpdeci/yolo-nas-fruit-detection

2. K-Fold Cross Validation and YoloV8 | Upvotes: 58
   URL: https://www.kaggle.com/code/tataganesh/k-fold-cross-validation-and-yolov8

3. Fruits_objectdetection 🍍🍎 | Upvotes: 44
   URL: https://www.kaggle.com/code/maryamayman20/fruits-objectdetection

4. Comprehensive Fruit Image Dataset | Upvotes: 13
   URL: https://www.kaggle.com/datasets/evilspirit05/comprehensive-fruit-image-dataset

5. Fruit Infection Disease Dataset | Upvotes: 11
   URL: https://www.kaggle.com/datasets/nikitkashyap/fruit-infection-disease-dataset

============================

Essential SQL Topics for Data Analysts

SQL for Data Analysts Free Resources -> https://t.me/sqlanalyst

- Basic Queries: SELECT, FROM, WHERE clauses.
- Sorting and Filtering: ORDER BY, GROUP BY, HAVING.
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN.
- Aggregation Functions: COUNT, SUM, AVG, MIN, MAX.
- Subqueries: Embedding queries within queries.
- Data Modification: INSERT, UPDATE, DELETE.
- Indexes: Optimizing query performance.
- Normalization: Ensuring efficient database design.
- Views: Creating virtual tables for simplified queries.
- Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many.

Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include:

- ROW_NUMBER(): Assigns a unique number to each row based on a specified order.
- RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently.
- LAG() and LEAD(): Access data from preceding or following rows within a partition.
- SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows.

Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz

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🤔Are you looking for some new project ideas to include in your Portfolio❓

👉 Here are 3 unique ideas for you:

1️⃣ Summer Olympics
Dataset : https://www.kaggle.com/datasets/divyansh22/summer-olympics-medals

2️⃣ Food Nutrition
Dataset : https://www.kaggle.com/datasets/utsavdey1410/food-nutrition-dataset/data

3️⃣ Mental health
Dataset : https://www.kaggle.com/datasets/programmerrdai/mental-health-dataset/data

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.

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Quick SQL functions cheat sheet for beginners

Aggregate Functions

COUNT(*): Counts rows.

SUM(column): Total sum.

AVG(column): Average value.

MAX(column): Maximum value.

MIN(column): Minimum value.


String Functions

CONCAT(a, b, …): Concatenates strings.

SUBSTRING(s, start, length): Extracts part of a string.

UPPER(s) / LOWER(s): Converts string case.

TRIM(s): Removes leading/trailing spaces.


Date & Time Functions

CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.

EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).

DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.


Numeric Functions

ROUND(num, decimals): Rounds to a specified decimal.

CEIL(num) / FLOOR(num): Rounds up/down.

ABS(num): Absolute value.

MOD(a, b): Returns the remainder.


Control Flow Functions

CASE: Conditional logic.

COALESCE(val1, val2, …): Returns the first non-null value.


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Here are the questions With Answers ✨

1. Write a query to get the EmpFname from the EmployeeInfo table in the upper case using the alias name as EmpName.

[
SELECT UPPER(EmpFname) AS EmpName FROM EmployeeInfo;
]

2. Write a query to get the number of employees working in the department ‘HR’.

[
SELECT COUNT(*) FROM EmployeeInfo WHERE Department = 'HR';
]

3. What query will you write to fetch the current date?

[
-- For SQL Server:
SELECT GETDATE();

-- For MySQL:
SELECT SYSDATE();
]

4. Write a query to fetch only the place name (string before brackets) from the Address column of the EmployeeInfo table.

[
-- Using MID function in MySQL:
SELECT MID(Address, 1, LOCATE('(', Address) - 1) FROM EmployeeInfo;

-- Using SUBSTRING function:
SELECT SUBSTRING(Address, 1, CHARINDEX('(', Address) - 1) FROM EmployeeInfo;
]

5. Write a query to create a new table whose data and structure are copied from another table.

[
-- Using SELECT INTO in SQL Server:
SELECT * INTO NewTable FROM EmployeeInfo WHERE 1 = 0;

-- Using CREATE TABLE AS in MySQL:
CREATE TABLE NewTable AS SELECT * FROM EmployeeInfo;
]

6. Write a query to display the names of employees that begin with ‘S’.

[
SELECT * FROM EmployeeInfo WHERE EmpFname LIKE 'S%';
]

7. Write a query to retrieve the top N records.

[
-- Using TOP in SQL Server:
SELECT TOP N * FROM EmployeePosition ORDER BY Salary DESC;

-- Using LIMIT in MySQL:
SELECT * FROM EmployeePosition ORDER BY Salary DESC LIMIT N;
]

8. Write a query to obtain relevant records from the EmployeeInfo table ordered by Department in ascending order and EmpLname in descending order.

[
SELECT * FROM EmployeeInfo ORDER BY Department ASC, EmpLname DESC;
]

9. Write a query to get the details of employees whose EmpFname ends with ‘A’.

[
SELECT * FROM EmployeeInfo WHERE EmpFname LIKE '%A';
]

10. Create a query to fetch details of employees having “DELHI” as their address.

[
SELECT * FROM EmployeeInfo WHERE Address LIKE '%DELHI%';
]

11. Write a query to fetch all employees who also hold the managerial position.

[
SELECT E.EmpFname, E.EmpLname, P.EmpPosition
FROM EmployeeInfo E
INNER JOIN EmployeePosition P ON E.EmpID = P.EmpID
WHERE P.EmpPosition = 'Manager';
]

12. Create a query to generate the first and last records from the EmployeeInfo table.

[
-- First record:
SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MIN(EmpID) FROM EmployeeInfo);

-- Last record:
SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MAX(EmpID) FROM EmployeeInfo);
]

13. Create a query to check if the passed value to the query follows the EmployeeInfo and EmployeePosition tables’ date format.

[
SELECT ISDATE('01/04/2020') AS "MM/DD/YY";
]

14. Create a query to obtain display employees having salaries equal to or greater than 150000.

[
SELECT EmpName FROM EmployeePosition WHERE Salary >= 150000;
]

15. Write a query to fetch the year using a date.

[
SELECT YEAR(GETDATE()) AS "Year";
]

16. Create an SQL query to fetch EmpPosition and the total salary paid for each employee position.

[
SELECT EmpPosition, SUM(Salary) FROM EmployeePosition GROUP BY EmpPosition;
]

17. Write a query to find duplicate records from a table.

[
SELECT EmpID, EmpFname, Department, COUNT(*)
FROM EmployeeInfo
GROUP BY EmpID, EmpFname, Department
HAVING COUNT(*) > 1;
]

18. Create a query to fetch the third-highest salary from the EmpPosition table.

[
SELECT TOP 1 Salary
FROM (
SELECT TOP 3 Salary
FROM EmpPosition
ORDER BY Salary DESC
) AS ThirdHighestSalary
ORDER BY Salary ASC;
]

19. Write an SQL query to find even and odd records in the EmployeeInfo table.

[
-- Even records:
SELECT EmpID FROM (SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo) AS T1 WHERE MOD(rowno, 2) = 0;

-- Odd records:
SELECT EmpID FROM (SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo) AS T1 WHERE MOD(rowno, 2) = 1;
]

20. Create a query to fetch the list of employees of the same department.

[
SELECT DISTINCT E1.EmpID, E1.EmpFname, E1.Department
FROM EmployeeInfo E1
INNER JOIN EmployeeInfo E2 ON E1.Department = E2.
]

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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.me/datalemur

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