🔥 𝗦𝘁𝗼𝗽 𝗪𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀.

𝗦𝘁𝗮𝗿𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗶𝗻𝗴 𝗟𝗶𝗸𝗲 𝗮 𝗥𝗲𝗮𝗹 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿.

If you want 𝗷𝗼𝗯-𝗿𝗲𝗮𝗱𝘆 𝗦𝗤𝗟, 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝘆𝗦𝗽𝗮𝗿𝗸, 𝗔𝘇𝘂𝗿𝗲 & 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 skills,

Here’s where to practice and what exactly to practice because these are mainly expected in all the companies especially in EY, PwC, KPMG & Deloitte 👇

1️⃣ 𝗦𝗤𝗟 — 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗟𝗲𝘃𝗲𝗹

LeetCode (SQL): https://lnkd.in/gudFeUbZ
HackerRank (SQL): https://lnkd.in/g9hpE6vQ
SQLZoo: https://sqlzoo.net/
• JOINs (INNER, LEFT, RIGHT)
• GROUP BY & HAVING
• Window functions (ROW_NUMBER, RANK)
• CTEs (WITH clause)
• Query optimization logic

2️⃣ 𝗣𝘆𝘁𝗵𝗼𝗻 — 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗙𝗼𝗰𝘂𝘀

LeetCode (Python): https://lnkd.in/gaEvhsvi
HackerRank (Python): https://lnkd.in/gGHkAE47
Exercism (Python): https://lnkd.in/gAuvZmwZ
• Functions & modules
• File handling (CSV, JSON)
• Data structures (list, dict)
• Error handling & logging
• Clean, readable code

3️⃣ 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 — 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻

Databricks Community: https://lnkd.in/gpDTBDpq
SparkByExamples: https://lnkd.in/gfjnQ7Ud
Kaggle Notebooks: https://lnkd.in/gm7YU7Fp
• DataFrames & transformations
• Joins & aggregations
• Partitioning & caching
• Handling large datasets
• Performance tuning basics

4️⃣ 𝗔𝘇𝘂𝗿𝗲 — 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

Azure Free Account: https://lnkd.in/gk_Dpb9v
Microsoft Learn: https://lnkd.in/gb8nTnBf
Azure Data Factory: https://lnkd.in/ggpsYk7X
• Data ingestion using ADF
• ADLS Gen2 storage layers
• Parameterized pipelines
• Incremental data loads
• Monitoring & debugging

5️⃣ 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 — 𝗥𝗲𝗮𝗹 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴

Snowflake Trial: https://lnkd.in/g2dHRA9f
Sample Data: https://lnkd.in/grsV2X47
Snowflake Learn: https://lnkd.in/gVpiNKHF

• Data Loading and Unloading
• Fact & dimension modeling
• ELT inside Snowflake
• Query Profile analysis
• Cost & performance tuning

📊 Data Science Essentials: What Every Data Enthusiast Should Know!

1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.

2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.

3️⃣ Use Descriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.

4️⃣ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.

5️⃣ Learn SQL for Efficient Data Extraction
Write optimized queries (SELECT, JOIN, GROUP BY, WHERE) to retrieve relevant data from databases.

6️⃣ Build Strong Programming Skills
Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis.

7️⃣ Understand Machine Learning Basics
Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.

8️⃣ Learn Dashboarding & Storytelling
Power BI and Tableau help convert raw data into actionable insights for stakeholders.

🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy!

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

DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL!

Top 5 Case Studies for Data Analytics: You Must Know Before Attending an Interview

1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.

2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.

3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.

4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.

5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.

Like if it helps 😄

🚀 Roadmap to Master Data Visualization in 30 Days! 📊🎨

📅 Week 1: Fundamentals
🔹 Day 1–2: What is Data Visualization? Importance real-world impact
🔹 Day 3–5: Types of charts – bar, line, pie, scatter, heatmaps
🔹 Day 6–7: When to use what? Choosing the right chart for your data

📅 Week 2: Tools Techniques
🔹 Day 8–9: Excel/Google Sheets – basic charts formatting
🔹 Day 10–12: Tableau – dashboards, filters, actions
🔹 Day 13–14: Power BI – visuals, slicers, interactivity

📅 Week 3: Python Design Principles
🔹 Day 15–17: Matplotlib, Seaborn – plots in Python
🔹 Day 18–20: Plotly – interactive visualizations
🔹 Day 21: Data-Ink ratio, color theory, accessibility in design

📅 Week 4: Real-World Projects Portfolio
🔹 Day 22–24: Create visuals for business KPIs (sales, marketing, HR)
🔹 Day 25–27: Redesign poor visualizations (fix misleading graphs)
🔹 Day 28–30: Build publish your own portfolio dashboard

💡 Tips:
• Always ask: “What story does the data tell?”
• Avoid clutter. Label clearly. Keep it actionable.
• Share your work on Tableau Public, GitHub, or Medium

💬 Tap ❤️ for more!

✅ Math for Artificial Intelligence 🧠

Mathematics is the foundation of AI. It helps machines "understand" data, make decisions, and learn from experience.

Here are the must-know math concepts used in AI (with simple examples):

1️⃣ Linear Algebra
Used for image processing, neural networks, word embeddings.

✅ Key Concepts: Vectors, Matrices, Dot Product

import numpy as np  
a = np.array([1, 2])  
b = np.array([3, 4])  
dot = np.dot(a, b)    # Output: 11

✍️ AI Use: Input data is often stored as vectors/matrices. Model weights and activations are matrix operations.

2️⃣ Statistics & Probability
Helps AI models make predictions, handle uncertainty, and measure confidence.

✅ Key Concepts: Mean, Median, Standard Deviation, Probability

import statistics  
data = [2, 4, 4, 4, 5, 5, 7]  
mean = statistics.mean(data)     # Output: 4.43

✍️ AI Use: Probabilities in Naive Bayes, confidence scores, randomness in training.

3️⃣ Calculus (Basics)
Needed for optimization — especially in training deep learning models.

✅ Key Concepts: Derivatives, Gradients

✍️ AI Use: Used in backpropagation (to update model weights during training).

4️⃣ Logarithms & Exponentials
Used in functions like Softmax, Sigmoid, and in loss functions like Cross-Entropy.

import math  
x = 2  
print(math.exp(x))     # e^2 ≈ 7.39  
print(math.log(10))    # log base e

✍️ AI Use: Activation functions, probabilities, loss calculations.

5️⃣ Vectors & Distances
Used to measure similarity or difference between items (images, texts, etc.).

✅ Example: Euclidean distance

from scipy.spatial import distance  
a = [1, 2]  
b = [4, 6]  
print(distance.euclidean(a, b))   # Output: 5.0

✍️ AI Use: Used in clustering, k-NN, embeddings comparison.

You don’t need to be a math genius — just understand how the core concepts power what AI does under the hood.

💬 Double Tap ♥️ For More!

✅ SQL Interview Challenge – Filter Top N Records per Group 🧠💾

🧑‍💼 Interviewer: How would you fetch the top 2 highest-paid employees per department?

👨‍💻 Me: Use ROW_NUMBER() with a PARTITION BY clause—it's a window function that numbers rows uniquely within groups, resetting per partition for precise top-N filtering.

🔹 SQL Query:

SELECT *
FROM (
  SELECT name, department, salary,
         ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn
  FROM employees
) AS ranked
WHERE rn <= 2;

✔ Why it works:
– PARTITION BY department resets row numbers (starting at 1) for each dept group, treating them as mini-tables.
– ORDER BY salary DESC ranks highest first within each partition.
– WHERE rn <= 2 grabs the top 2 per group—subquery avoids duplicates in complex joins!

💡 Pro Tip: Swap to RANK() if ties get equal ranks (e.g., two at #1 means next is #3, but you might get 3 rows); DENSE_RANK() avoids gaps. For big datasets, this scales well in SQL Server or Postgres.

💬 Tap ❤️ for more!