📍Tata Technologies is hiring for AI Engineer

Qualification: Bachelors / Masters Degree

Experience: 0-2 years 

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹3-7 LPA

Job Location: PUNE

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/tata-technologies-off-campus-hiring-ai-engineer/

The GROUP BY clause in SQL is used to arrange identical data into groups. This is particularly useful when combined with aggregate functions like COUNT(), SUM(), AVG(), MIN(), and MAX(). The GROUP BY clause groups rows that have the same values in specified columns into summary rows.

▎Basic Syntax

SELECT column1, aggregate_function(column2)
FROM table_name
WHERE condition
GROUP BY column1;

▎Example 1: Counting Rows

Suppose you have a table called employees with the following structure:

| id | department | salary |
|----|------------|--------|
| 1  | HR         | 50000  |
| 2  | IT         | 60000  |
| 3  | HR         | 55000  |
| 4  | IT         | 70000  |
| 5  | Sales      | 65000  |

To find out how many employees are in each department, you can use:

SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department;

Result:

| department | employee_count |
|------------|----------------|
| HR         | 2              |
| IT         | 2              |
| Sales      | 1              |

▎Example 2: Summing Salaries

To calculate the total salary paid to employees in each department, you can use:

SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department;

Result:

| department | total_salary |
|------------|--------------|
| HR         | 105000       |
| IT         | 130000       |
| Sales      | 65000        |

▎Example 3: Average Salary

To find the average salary of employees in each department:

SELECT department, AVG(salary) AS average_salary
FROM employees
GROUP BY department;

Result:

| department | average_salary |
|------------|----------------|
| HR         | 52500          |
| IT         | 65000          |
| Sales      | 65000          |

▎Example 4: Grouping by Multiple Columns

You can also group by multiple columns. For instance, if you had another column for job_title:

| id | department | job_title | salary |
|----|------------|-----------|--------|
| 1  | HR         | Manager   | 50000  |
| 2  | IT         | Developer | 60000  |
| 3  | HR         | Assistant | 55000  |
| 4  | IT         | Manager   | 70000  |
| 5  | Sales      | Executive | 65000  |

To count employees by both department and job_title:

SELECT department, job_title, COUNT(*) AS employee_count
FROM employees
GROUP BY department, job_title;

Result:

| department | job_title | employee_count |
|------------|-----------|----------------|
| HR         | Manager   | 1              |
| HR         | Assistant | 1              |
| IT         | Developer | 1              |
| IT         | Manager   | 1              |
| Sales      | Executive | 1              |

▎Important Notes

1. Aggregate Functions: Any column in the SELECT statement that is not an aggregate function must be included in the GROUP BY clause.
 
2. HAVING Clause: You can filter groups using the HAVING clause, which is similar to the WHERE clause but is used for aggregated data. For example:

  

   SELECT department, COUNT(*) AS employee_count
   FROM employees
   GROUP BY department
   HAVING COUNT(*) > 1;
   

   This would return only departments with more than one employee.

▎Conclusion

The GROUP BY clause is a powerful tool in SQL for summarizing data. It allows you to analyze and report on your datasets effectively by grouping similar data points and applying aggregate functions.

𝗦𝗤𝗟 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 📊

Whether you're writing daily queries or preparing for interviews, understanding these subtle SQL differences can make a big impact on both performance and accuracy.

🧠 Here’s a powerful visual that compares the most commonly misunderstood SQL concepts — side by side.

📌 𝗖𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘀𝗻𝗮𝗽𝘀𝗵𝗼𝘁:
🔹 RANK() vs DENSE_RANK()
🔹 HAVING vs WHERE
🔹 UNION vs UNION ALL
🔹 JOIN vs UNION
🔹 CTE vs TEMP TABLE
🔹 SUBQUERY vs CTE
🔹 ISNULL vs COALESCE
🔹 DELETE vs DROP
🔹 INTERSECT vs INNER JOIN
🔹 EXCEPT vs NOT IN

React ♥️ for detailed post with examples

📍Honeywell is hiring for Software Engr I

Qualification: Bachelors Degree

Experience: 0-1 years 

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹6 - 13 LPA

Job Location: Madurai

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/honeywell-off-campus-hiring-software-engineer/

📍Allianz is hiring for GEN AI Fullstack Engineer

Qualification: Bachelors / Masters Degree

Experience: 0-1 years 

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹6 LPA

Job Location: Pune

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/allianz-hiring-ai-fullstack-engineer/

📍Sprinklr is hiring for Software Development Engineer, QA

Qualification: B.E/B.Tech

Experience: 0-1 years 

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹17 LPA

Job Location: Gurgaon

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/sprinklr-hiring-sde-qa-freshers/

📍Thomson Reuters is hiring for Software Engineer

Qualification: Bachelors / Masters Degree

Experience: 0-1 years 

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹5 LPA

Job Location: Hyderabad

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/thomson-reuters-hiring-software-engineer/

📍Morgan Stanley is hiring for UI Developer (React)

Qualification: Bachelors / Masters Degree

Experience: Fresher

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹7-28 LPA

Job Location: Bengaluru

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/morgan-stanley-hiring-ui-developer-react/

📍PwC is hiring for Cyber R&R - ER&CS - Data Analytics

Qualification: Bachelors / Masters Degree

Experience: Fresher

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹7-10 LPA

Job Location: Bangalore,Mumbai, Kolkata, Hyderabad India

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/pwc-off-campus-hiring-data-analytics-2/

📍Accenture is hiring for Technology Platform Engineer

Qualification: Any Graduation

Experience: 0 – 2 years

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹4 LPA

Job Location: Gurugram 

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/accenture-hiring-tech-platform-engineer/

Python Roadmap for 2025: Complete Guide

1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.

2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.

3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.

4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).

5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.

6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.

7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).

8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).

9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.

10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.

11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.

12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.

13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).

14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.

15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.

16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.

16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.

💬 Double Tap ❤️ for more! 🧠💻

📍Microsoft is hiring for Software Engineer

Qualification: Bachelors / Masters Degree

Experience: Fresher (0-1 years)

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹15 – 30 LPA

Job Location: Multiple Locations

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/microsoft-hiring-software-engineers/

📍Zebra is hiring for Cloud Engineer

Qualification: Bachelors Degree

Experience: 0 to 1 years

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹10 LPA

Job Location: Pune

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/zebra-off-campus-hiring-cloud-engineer/

📍Innovaccer is hiring for Apprentice-Data Ops Engineer

Qualification: Bachelors / Masters Degree

Experience: Fresher 

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹7-9 LPA

Job Location: Noida

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/innovaccer-hiring-freshers-data-ops/

📍Motorola Solutions is hiring for Data Engineer

Qualification: Bachelors / Masters Degree

Experience: 0 - 2 years

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹6-10 LPA

Job Location: Bangalore

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/motorola-solutions-hiring-data-engineers/

📍Netomi AI is hiring for SDE I (Frontend)

Qualification: Bachelors / Masters Degree

Experience: 0-1 years

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹12- 16 LPA

Job Location: Gurugram

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/netomi-hiring-sde-i-frontend/

📍LTTS is hiring for Embedded SW Developer

Qualification: Bachelors / Masters Degree

Experience: Fresher 

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹5.5 LPA

Job Location: Bangalore

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/ltts-hiring-embedded-sw-developer/

📍Unisys is hiring for Jr Eng Software Eng

Qualification: Bachelors / Masters Degree

Experience: Fresher

Batch: 2025 / 2024 / 2023 / 2022

Salary: up to ₹5 LPA

Job Location: Bangalore

🔗 𝗔𝗽𝗽𝗹𝘆 𝗛𝗲𝗿𝗲: https://techcompreviews.in/unisys-off-campus-hiring-eng-software-eng/

Machine Learning – Essential Concepts 🚀

1️⃣ Types of Machine Learning

Supervised Learning – Uses labeled data to train models.

Examples: Linear Regression, Decision Trees, Random Forest, SVM


Unsupervised Learning – Identifies patterns in unlabeled data.

Examples: Clustering (K-Means, DBSCAN), PCA


Reinforcement Learning – Models learn through rewards and penalties.

Examples: Q-Learning, Deep Q Networks



2️⃣ Key Algorithms

Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).

Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).

Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).

Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).


3️⃣ Model Training & Evaluation

Train-Test Split – Dividing data into training and testing sets.

Cross-Validation – Splitting data multiple times for better accuracy.

Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.


4️⃣ Feature Engineering

Handling missing data (mean imputation, dropna()).

Encoding categorical variables (One-Hot Encoding, Label Encoding).

Feature Scaling (Normalization, Standardization).


5️⃣ Overfitting & Underfitting

Overfitting – Model learns noise, performs well on training but poorly on test data.

Underfitting – Model is too simple and fails to capture patterns.

Solution: Regularization (L1, L2), Hyperparameter Tuning.


6️⃣ Ensemble Learning

Combining multiple models to improve performance.

Bagging (Random Forest)

Boosting (XGBoost, Gradient Boosting, AdaBoost)



7️⃣ Deep Learning Basics

Neural Networks (ANN, CNN, RNN).

Activation Functions (ReLU, Sigmoid, Tanh).

Backpropagation & Gradient Descent.


8️⃣ Model Deployment

Deploy models using Flask, FastAPI, or Streamlit.

Model versioning with MLflow.

Cloud deployment (AWS SageMaker, Google Vertex AI).

Double Tap ❤️ for more 🧠