company name: JP Morgan Chase
role: sde-1
batch: 2024/23 passouts

link:https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/job/210667642

S&P Global is hiring Data Analyst 🚀🔥

Experience : 0-6 Months
Location : Bangalore

Apply link : https://careers.spglobal.com/jobs/319690?lang=en-us&utm_source=linkedin

Data Engineering Roadmap for Beginners (2025)

> Language → Python + SQL.
> OS Basics → Linux + Bash + Git.
> Data Modeling → Normalization + Star/Snowflake Schema.
> Databases → PostgreSQL + MySQL + MongoDB.
> Data Warehousing → Snowflake + BigQuery + Redshift.
> Data Processing → Apache Spark + PySpark.
> Workflow Orchestration → Airflow + Prefect.
> Data Lakes → Delta Lake + Apache Hudi + Iceberg.

> Streaming → Kafka + Flink
> Cloud Platforms → AWS (S3, Glue, EMR) / GCP (GCS, Dataflow, BigQuery) / Azure (Data Factory, Synapse).
> Data Quality/Validation → Great Expectations.
> Containerization → Docker + Kubernetes.
> Infra as Code → Terraform.
> Visualization → dbt + Looker/PowerBI/Tableau.

✅ Step-by-Step Approach to Learn Data Analytics 📈🧠

➊ Excel Fundamentals:
✔ Master formulas, pivot tables, data validation, charts, and graphs.

➋ SQL Basics:
✔ Learn to query databases, use SELECT, FROM, WHERE, JOIN, GROUP BY, and aggregate functions.

➌ Data Visualization:
✔ Get proficient with tools like Tableau or Power BI to create insightful dashboards.

➍ Statistical Concepts:
✔ Understand descriptive statistics (mean, median, mode), distributions, and hypothesis testing.

➎ Data Cleaning & Preprocessing:
✔ Learn how to handle missing data, outliers, and data inconsistencies.

➏ Exploratory Data Analysis (EDA):
✔ Explore datasets, identify patterns, and formulate hypotheses.

➐ Python for Data Analysis (Optional but Recommended):
✔ Learn Pandas and NumPy for data manipulation and analysis.

➑ Real-World Projects:
✔ Analyze datasets from Kaggle, UCI Machine Learning Repository, or your own collection.

➒ Business Acumen:
✔ Understand key business metrics and how data insights impact business decisions.

➓ Build a Portfolio:
✔ Showcase your projects on GitHub, Tableau Public, or a personal website. Highlight the impact of your analysis.

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SQL Basics for Data Analysts

SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.

1️⃣ Understanding Databases & Tables

Databases store structured data in tables.

Tables contain rows (records) and columns (fields).

Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).

2️⃣ Basic SQL Commands

Let's start with some fundamental queries:

🔹 SELECT – Retrieve Data

SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns 

🔹 WHERE – Filter Data

SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary 

🔹 ORDER BY – Sort Data

SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first) 

🔹 LIMIT – Restrict Number of Results

SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees 

🔹 DISTINCT – Remove Duplicates

SELECT DISTINCT department FROM employees; -- Show unique departments 

Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.

You can find free SQL Resources here
👇👇
https://t.me/codingdidi

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Hope it helps :)

#sql

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Medibuddy is hiring Business Analyst 🚀🔥

Experience : 1+ Year
Location : Bangalore

Apply link : https://MediBuddy.hire.trakstar.com/jobs/fk0px9z?pjb_hash=Y7bAmjMPq4

All the best 👍👍

🚨 SQL Interview Challenge (Most Candidates Get This Wrong!)

Ques:

Can you write a query to find employees who earn more than the average salary of their own department?

👀 Sounds simple… but this is where many people slip.

Ans:

SELECT e.*
FROM employees e
JOIN (
SELECT department_id, AVG(salary) AS avg_salary
FROM employees
GROUP BY department_id
) d
ON e.department_id = d.department_id
WHERE e.salary > d.avg_salary;

📌 Why interviewers love this:

It tests your understanding of correlated logic, aggregation, and joins.

💡 Key insight:

The comparison is done within each department, not across the entire table.

👍 If this clarified a tricky concept, react with 👍🔥

📲 Follow this channel for more advanced, query-based SQL interview questions 🚀

Qualcomm is hiring Associate Engineer

For 2023, 2024, 2025 gards
Location: Hyderabad

https://careers.qualcomm.com/careers/job/446715365662-engineer-associate-hyderabad-telangana-india?domain=qualcomm.com

✅ Machine Learning Interview Questions & Answers 🎯

1. What is the difference between supervised and unsupervised learning
Answer:
Supervised learning uses labeled data to learn a mapping from inputs to outputs (e.g., predicting house prices). Unsupervised learning finds hidden patterns or groupings in unlabeled data (e.g., customer segmentation using K-Means).

2. How do you handle missing values during feature engineering
Answer:
Common strategies include:
– Imputation: Fill missing values with mean, median, or mode
– Deletion: Remove rows or columns with excessive missing data
– Model-based: Use predictive models to estimate missing values

3. What is the bias-variance tradeoff
Answer:
Bias refers to error due to overly simplistic assumptions; variance refers to error due to model sensitivity to small fluctuations in training data. A good model balances both to avoid underfitting (high bias) and overfitting (high variance).

4. Explain how Random Forest reduces overfitting
Answer:
Random Forest uses bagging (bootstrap aggregation) and builds multiple decision trees on random subsets of data and features. It averages their predictions, reducing variance and improving generalization.

5. What is the role of cross-validation in model selection
Answer:
Cross-validation (e.g., k-fold) splits data into multiple training/testing sets to evaluate model performance more reliably. It helps prevent overfitting and ensures the model generalizes well to unseen data.

6. How does XGBoost differ from traditional boosting methods
Answer:
XGBoost uses gradient boosting with regularization (L1 and L2), tree pruning, and parallel processing. It’s faster and more accurate than traditional boosting algorithms like AdaBoost.

7. What is the difference between L1 and L2 regularization
Answer:
– L1 (Lasso): Adds absolute value of weights to loss function, promoting sparsity
– L2 (Ridge): Adds squared value of weights, penalizing large weights and improving stability

8. How would you deploy a trained ML model
Answer:
– Serialize the model using pickle or joblib
– Create a REST API using Flask or FastAPI
– Monitor performance using metrics like latency, accuracy drift, and feedback loops

9. What is the difference between precision and recall
Answer:
– Precision: True Positives / (True Positives + False Positives)
– Recall: True Positives / (True Positives + False Negatives)
Precision focuses on correctness of positive predictions; recall focuses on capturing all actual positives.

10. What is the Q-value in reinforcement learning
Answer:
Q-value represents the expected cumulative reward of taking an action in a given state and following a policy thereafter. It’s central to Q-learning algorithms.

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Capco
Position: Financial Accounting
Qualifications: Bachelor’s/ Master’s Degree
Experience: Freshers/ Experienced
Location: Across India

📌Apply Now: https://www.capco.com/en/Careers/Job%20Search/Office%20Detail?gh_jid=7427797&location=36f0686bfe4b44afa75f218f838e0fdc&department=&keywords=

Complete DSA Roadmap

|-- Basic_Data_Structures
| |-- Arrays
| |-- Strings
| |-- Linked_Lists
| |-- Stacks
| └─ Queues
|
|-- Advanced_Data_Structures
| |-- Trees
| | |-- Binary_Trees
| | |-- Binary_Search_Trees
| | |-- AVL_Trees
| | └─ B-Trees
| |
| |-- Graphs
| | |-- Graph_Representation
| | | |- Adjacency_Matrix
| | | └ Adjacency_List
| | |
| | |-- Depth-First_Search
| | |-- Breadth-First_Search
| | |-- Shortest_Path_Algorithms
| | | |- Dijkstra's_Algorithm
| | | └ Bellman-Ford_Algorithm
| | |
| | └─ Minimum_Spanning_Tree
| | |- Prim's_Algorithm
| | └ Kruskal's_Algorithm
| |
| |-- Heaps
| | |-- Min_Heap
| | |-- Max_Heap
| | └─ Heap_Sort
| |
| |-- Hash_Tables
| |-- Disjoint_Set_Union
| |-- Trie
| |-- Segment_Tree
| └─ Fenwick_Tree
|
|-- Algorithmic_Paradigms
| |-- Brute_Force
| |-- Divide_and_Conquer
| |-- Greedy_Algorithms
| |-- Dynamic_Programming
| |-- Backtracking
| |-- Sliding_Window_Technique
| |-- Two_Pointer_Technique
| └─ Divide_and_Conquer_Optimization
| |-- Merge_Sort_Tree
| └─ Persistent_Segment_Tree
|
|-- Searching_Algorithms
| |-- Linear_Search
| |-- Binary_Search
| |-- Depth-First_Search
| └─ Breadth-First_Search
|
|-- Sorting_Algorithms
| |-- Bubble_Sort
| |-- Selection_Sort
| |-- Insertion_Sort
| |-- Merge_Sort
| |-- Quick_Sort
| └─ Heap_Sort
|
|-- Graph_Algorithms
| |-- Depth-First_Search
| |-- Breadth-First_Search
| |-- Topological_Sort
| |-- Strongly_Connected_Components
| └─ Articulation_Points_and_Bridges
|
|-- Dynamic_Programming
| |-- Introduction_to_DP
| |-- Fibonacci_Series_using_DP
| |-- Longest_Common_Subsequence
| |-- Longest_Increasing_Subsequence
| |-- Knapsack_Problem
| |-- Matrix_Chain_Multiplication
| └─ Dynamic_Programming_on_Trees
|
|-- Mathematical_and_Bit_Manipulation_Algorithms
| |-- Prime_Numbers_and_Sieve_of_Eratosthenes
| |-- Greatest_Common_Divisor
| |-- Least_Common_Multiple
| |-- Modular_Arithmetic
| └─ Bit_Manipulation_Tricks
|
|-- Advanced_Topics
| |-- Trie-based_Algorithms
| | |-- Auto-completion
| | └─ Spell_Checker
| |
| |-- Suffix_Trees_and_Arrays
| |-- Computational_Geometry
| |-- Number_Theory
| | |-- Euler's_Totient_Function
| | └─ Mobius_Function
| |
| └─ String_Algorithms
| |-- KMP_Algorithm
| └─ Rabin-Karp_Algorithm
|
|-- OnlinePlatforms
| |-- LeetCode
| |-- HackerRank


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🧠 Scenario-Based SQL Interview Question (Asked Often)

📌 Scenario:

You’re a Data Analyst at an e-commerce company.
There’s an orders table with these columns:

order_id, customer_id, order_date, order_amount

👉 Interview Question:

Find customers who placed more than 1 order on the same day, and show the total amount they spent on that day.

⏳ Take 10 seconds. How would you think?

✅ SQL Approach

1️⃣ Group data by customer_id and order_date
2️⃣ Count orders per day
3️⃣ Sum total order value
4️⃣ Filter customers with more than one order

💡 SQL Query

SELECT
customer_id,
order_date,
COUNT(order_id) AS total_orders,
SUM(order_amount) AS total_spent
FROM orders
GROUP BY customer_id, order_date
HAVING COUNT(order_id) > 1;

Drop a 🔥 if you want tougher SQL questions next

🐍 PYTHON TRICK #1


❌ Wrong way:

squares = [ ]
for num in [1,2,3,4,5]:
squares.append(num**2)

✅ Right way:

squares = [num**2 for num in [1,2,3,4,5]]

💡 5 lines → 1 line!



In the first (wrong/long) way, we are using a traditional loop. Python reads each number one by one, performs the square operation, and then manually adds the result into a list using append(). This works perfectly fine, but it’s longer, slower to write, and less “Pythonic.” When code grows bigger, these extra lines make programs harder to read.

Now look at the second (right) way — this is called a List Comprehension. It combines loop + expression + list creation into a single clean line:

👉 squares = [num**2 for num in [1,2,3,4,5]]

Python is designed to read almost like English. This line literally means:
“For every number in the list, square it, and store the result in a new list.”

🚀 Why List Comprehensions are powerful:

✔️ Shorter code
✔️ Easier to read once you practice
✔️ Faster execution in many cases
✔️ Used heavily in Data Science & AI
✔️ Makes you look like a pro Python developer

This is the difference between just writing code and writing smart Python code 💻🔥

Start using this habit in small problems, and soon it’ll become your natural style.

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💰 EARNING TIP #1

I recently shared about this on my instagram channel, So here's the process

Reddit se freelance kaam milta hai! Yahan start karo:

🎯 r/forhire
🎯 r/slavelabour

Process:
1️⃣ Account banao
2️⃣ Karma build karo (help people)
3️⃣ Browse daily
4️⃣ Reply fast
5️⃣ Get paid directly!

Per project: $20-100 (₹1,700-8,400)

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SQL Interview Trap 🚨 Consecutive Orders Logic

You have a table:
orders

order_id | customer_id | order_date | amount

👉 Question:

Find customers who placed orders on 3 or more consecutive days,
but return only the first date of each such streak per customer.

⚠️ No temp tables.
⚠️ Assume multiple orders per day are possible.

🧠 Most candidates fail because they:

- Forget to handle multiple orders on the same day
- Misuse ROW_NUMBER()
- Miss the date gap logic

✅ Correct SQL Solution:

WITH distinct_orders AS (
SELECT DISTINCT customer_id, order_date
FROM orders
),
grp AS (
SELECT
customer_id,
order_date,
order_date - INTERVAL '1 day' *
ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS grp_id
FROM distinct_orders
)
SELECT
customer_id,
MIN(order_date) AS streak_start_date
FROM grp
GROUP BY customer_id, grp_id
HAVING COUNT(*) >= 3;

💡 Why this works (Interview Gold):

- DISTINCT removes same-day duplicates
- ROW_NUMBER() creates a sequence
- Date minus row number groups consecutive dates
- HAVING COUNT(*) >= 3 filters valid streaks

🔥 React with 🔥 if this bent your brain

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📌 Follow the channel for REAL interview-level SQL Content

✅ Essential Tools for Data Analytics 📊🛠

🔣 1️⃣ Excel / Google Sheets
• Quick data entry & analysis
• Pivot tables, charts, functions
• Good for early-stage exploration

💻 2️⃣ SQL (Structured Query Language)
• Work with databases (MySQL, PostgreSQL, etc.)
• Query, filter, join, and aggregate data
• Must-know for data from large systems

🐍 3️⃣ Python (with Libraries)
• Pandas – Data manipulation
• NumPy – Numerical analysis
• Matplotlib / Seaborn – Data visualization
• OpenPyXL / xlrd – Work with Excel files

📊 4️⃣ Power BI / Tableau
• Create dashboards and visual reports
• Drag-and-drop interface for non-coders
• Ideal for business insights & presentations

📁 5️⃣ Google Data Studio
• Free dashboard tool
• Connects easily to Google Sheets, BigQuery
• Great for real-time reporting

🧪 6️⃣ Jupyter Notebook
• Interactive Python coding
• Combine code, text, and visuals in one place
• Perfect for storytelling with data

🛠 7️⃣ R Programming (Optional)
• Popular in statistical analysis
• Strong in academic and research settings

☁️ 8️⃣ Cloud & Big Data Tools
• Google BigQuery, Snowflake – Large-scale analysis
• Excel + SQL + Python still work as a base

💡 Tip:
Start with Excel + SQL + Python (Pandas) → Add BI tools for reporting.

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