Most people learn SQL just enough to pull some data. But if you really understand it, you can analyze massive datasets without touching Excel or Python.

Here are 8 game-changing SQL concepts that will make you a data pro:

👇

1. Stop pulling raw data. Start pulling insights.

The biggest mistake? Running a query that gives you everything and then filtering it later.

Good analysts don’t pull raw data. They shape the data before it even reaches them.

2. “SELECT ” is a rookie move.

Pulling all columns is lazy and slow.

A pro only selects what they need.
✔️ Fewer columns = Faster queries
✔️ Less noise = Clearer insights

The more precise your query, the less time you waste cleaning data.

3. GROUP BY is your best friend.

You don’t need 100,000 rows of transactions. What you need is:
✔️ Sales per region
✔️ Average order size per customer
✔️ Number of signups per month

Grouping turns chaotic data into useful summaries.

4. Joins = Connecting the dots.

Your most important data is split across multiple tables.

Want to know how much each customer spent? You need to join:
✔️ Customer info
✔️ Order history
✔️ Payments

Joins = unlocking hidden insights.

5. Window functions will blow your mind.

They let you:
✔️ Rank customers by total purchases
✔️ Calculate rolling averages
✔️ Compare each row to the overall trend

It’s like pivot tables, but way more powerful.

6. CTEs will save you from spaghetti SQL.

Instead of writing a 50-line nested query, break it into steps.

CTEs (Common Table Expressions) make your SQL:
✔️ Easier to read
✔️ Easier to debug
✔️ Reusable

Good SQL is clean SQL.

7. Indexes = Speed.

If your queries take forever, your database is probably doing unnecessary work.

Indexes help databases find data faster.

If you work with large datasets, this is a game changer.

SQL isn’t just about pulling data. It’s about analyzing, transforming, and optimizing it.

Master these 7 concepts, and you’ll never look at SQL the same way again.

Join us on WhatsApp: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Can AI replace data scientist?

AI can automate many tasks that data scientists perform, but it is unlikely to completely replace them in the foreseeable future. Rather than replacing data scientists, AI will enhance their capabilities by automating repetitive tasks, allowing them to focus on higher-level strategy, decision-making, and ethical considerations.

What AI Can Automate in Data Science:

Data Cleaning & Preparation – AI can automate data wrangling tasks like handling missing values and detecting anomalies.

Feature Engineering – AI-driven tools can generate and select features automatically.

Model Selection & Hyperparameter Tuning – Automated Machine Learning (AutoML) can choose models, tune hyperparameters, and even optimize architectures.

Basic Data Visualization & Reporting – AI tools can generate dashboards and insights automatically.

What AI Cannot Replace:

Problem-Solving & Business Understanding – AI cannot define business problems, formulate hypotheses, or align analysis with strategic goals.

Interpretability & Decision-Making – AI-generated models can be complex, but a human expert is needed to interpret results and make decisions.

Innovation – AI lacks the ability identify new opportunities, or design novel experiments.

Ethical Considerations & Bias Handling – AI can introduce biases, and data scientists are needed to ensure fairness and ethical use.

Roadmap for Learning Machine Learning (ML)

Here’s a concise and point-wise roadmap for learning ML:

1. Prerequisites
- Learn programming basics (e.g., Python).
- Understand mathematics:
1 - Linear Algebra (vectors, matrices).
2 - Probability and Statistics (distributions, Bayes’ theorem).
3 - Calculus (derivatives, gradients).
4 - Familiarize yourself with data structures and algorithms.

2. Basics of Machine Learning
-Understand ML concepts:
Supervised, unsupervised, and reinforcement learning.
Training, validation, and testing datasets.
- Learn how to preprocess and clean data.
- Get familiar with Python libraries:
NumPy, Pandas, Matplotlib, and Seaborn.

3. Supervised Learning
- Study regression techniques:
Linear and Logistic Regression.
- Explore classification algorithms:
Decision Trees, Support Vector Machines (SVM), k-NN.
- Learn model evaluation metrics:
Accuracy, Precision, Recall, F1 Score, ROC-AUC.

4. Unsupervised Learning
- Learn clustering techniques:
k-Means, DBSCAN, Hierarchical Clustering.
- Understand Dimensionality Reduction:
PCA, t-SNE.

5. Advanced Concepts
- Explore ensemble methods:
Random Forest, Gradient Boosting, XGBoost, LightGBM.
- Learn hyperparameter tuning techniques:
Grid Search, Random Search.

6. Deep Learning (Optional for Advanced ML)
- Learn neural networks basics:
Forward and Backpropagation.
- Study Deep Learning libraries:
TensorFlow, PyTorch, Keras.
Explore CNNs, RNNs, and Transformers.

7. Hands-on Practice
- Work on small projects like:
1 - Predicting house prices.
2 - Sentiment analysis on tweets.
3 - Image classification.
4 - Explore Kaggle competitions and datasets.

8. Deployment
- Learn how to deploy ML models:
Use Flask, FastAPI, or Django.
- Explore cloud platforms: AWS, Azure, Google Cloud.

9. Keep Learning
- Stay updated with new techniques:
Follow blogs, papers, and conferences (e.g., NeurIPS, ICML).
- Dive into specialized fields:
NLP, Computer Vision, Reinforcement Learning.

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

Quick Power BI Dax Revision

1. Measures: Measures in DAX are calculations that are used in Power BI to perform aggregations, calculations, and comparisons on data. They are defined using the DEFINE MEASURE or CALCULATE functions.

2. Calculated Columns: Calculated columns are columns that are created in a table by using DAX expressions. They are calculated row by row when the data is loaded into the model.

3. DAX Functions: DAX provides a wide range of functions for data manipulation and calculation. Some common functions include SUM, AVERAGE, COUNT, FILTER, CALCULATE, RELATED, ALL, ALLEXCEPT, and many more.

4. Context: DAX calculations are performed within a context, which can be row context or filter context. Understanding how context works is crucial for writing accurate DAX expressions.

5. Relationships: Power BI data models are built on relationships between tables. DAX expressions can leverage these relationships to perform calculations across related tables.

6. Time Intelligence Functions: DAX includes a set of time intelligence functions that enable you to perform calculations based on dates and time periods. Examples include TOTALYTD, SAMEPERIODLASTYEAR, DATESBETWEEN, etc.

7. Variables: DAX allows you to declare and use variables within expressions to improve readability and performance of complex calculations.

8. Aggregation Functions: DAX provides aggregation functions like SUMX, AVERAGEX, COUNTX that allow you to iterate over a table and perform aggregations based on specified conditions.

9. Logical Functions: DAX includes logical functions such as IF, AND, OR, SWITCH that help in implementing conditional logic within calculations.

10. Error Handling: DAX provides functions like ISBLANK, IFERROR, BLANK, etc., for handling errors and missing data in calculations.

👩🏻‍💻 Why should one study Linear Algebra for ML?

👉🏼 Clearly, to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyper-parameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well.

👉🏼 Learn Linear Algebra for Machine Learning with:

Khan Academy: https://www.khanacademy.org/math/linear-algebra

Udacity: https://www.udacity.com/course/linear-algebra-refresher-course--ud953

Coursera: https://www.coursera.org/learn/linear-algebra-machine-learning

Here are some amazing freely available ebooks on the same topic:

Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf

An Introduction to Statistical Learning: https://faculty.marshall.usc.edu/gareth-james/ISL/

Happy machine learning! 🎉

Python Detailed Roadmap 🚀

📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)

📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules

📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON

📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation

📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions

📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators

📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)

📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)

📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI

📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)

📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub

📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews

Like for more ❤️💪

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This cheat sheet includes basic python required for data analysis excluding pandas, numpy & other libraries

🚀 Excel vs SQL vs Python (Pandas):

1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]

2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")

3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)

4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()

5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")

6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)

7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()

8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()

▎Essential Data Science Concepts Everyone Should Know:

1. Data Types and Structures:

• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)

• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)

• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)

2. Descriptive Statistics:

• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)

• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)

• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)

3. Probability and Statistics:

• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)

• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)

• Confidence Intervals: Estimating the range of plausible values for a population parameter

4. Machine Learning:

• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)

• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)

• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)

5. Data Cleaning and Preprocessing:

• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)

• Outlier Detection and Removal: Identifying and addressing extreme values

• Feature Engineering: Creating new features from existing ones (e.g., combining variables)

6. Data Visualization:

• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)

• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)

7. Ethical Considerations in Data Science:

• Data Privacy and Security: Protecting sensitive information

• Bias and Fairness: Ensuring algorithms are unbiased and fair

8. Programming Languages and Tools:

• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn

• R: Statistical programming language with strong visualization capabilities

• SQL: For querying and manipulating data in databases

9. Big Data and Cloud Computing:

• Hadoop and Spark: Frameworks for processing massive datasets

• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)

10. Domain Expertise:

• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis

• Problem Framing: Defining the right questions and objectives for data-driven decision making

Bonus:

• Data Storytelling: Communicating insights and findings in a clear and engaging manner

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

ENJOY LEARNING 👍👍

🤖 How Artificial Intelligence Works...

When you’re in an interview, it’s super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:

➤ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.

➤ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.

➤ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?

➤ 𝗬𝗼𝘂𝗿 𝗥𝗼𝗹𝗲:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure it’s clear whether you were leading the project, a key player, or supporting the team.

➤ 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.

➤ 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got.

➤ 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:
- Talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the team’s success?

➤ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
- Reflect on what you learned from the project. What new skills did you gain, and what would you do differently next time?

➤ 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- If there’s a pause after you describe the project, don’t hesitate to ask if they’d like more details or if there’s a specific part they’re interested in.

By preparing your project details thoroughly and understanding what the interviewer is looking for, you can talk about your experience in a way that really showcases your skills and increases your chances of getting the job.

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