Data Science Techniques

SQL Interview Questions with Answers

1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.

2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.

3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.

4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY

React ❤️ for more

If you’re just starting out in Data Analytics, it’s super important to build the right habits early.

Here’s a simple plan for beginners to grow both technical and problem-solving skills together:

If You Just Started Learning Data Analytics, Focus on These 5 Baby Steps:

1. Don’t Just Watch Tutorials — Build Small Projects

After learning a new tool (like SQL or Excel), create mini-projects:

- Analyze your expenses

- Explore a free dataset (like Netflix movies, COVID data)


2. Ask Business-Like Questions Early

Whenever you see a dataset, practice asking:

- What problem could this data solve?

- Who would care about this insight?


3. Start a ‘Data Journal’

Every day, note down:

- What you learned

- One business question you could answer with data (Helps you build real-world thinking!)


4. Practice the Basics 100x

Get very comfortable with:

- SELECT, WHERE, GROUP BY (SQL)

- Pivot tables and charts (Excel)

- Basic cleaning (Power Query / Python pandas)


_Mastering basics > learning 50 fancy functions._

5. Learn to Communicate Early

Explain your mini-projects like this:

- What was the business goal?

- What did you find?

- What should someone do based on it?

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ENJOY LEARNING 👍👍

Complete Data Science Roadmap
👇👇

1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)

2. Mathematics and Statistics
- Probability and Distributions
- Denoscriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics

3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD

4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering

5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)

6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation

7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics

8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data

9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)

10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data

11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models

12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)

13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)

14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models

15. Tools for Data Science
- Jupyter, Git, Docker

16. Career Path & Certifications
- Building a Data Science Portfolio

Like if you need similar content 😄👍

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝘀. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘃𝘀. 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁

Think of them as data detectives.
→ 𝐅𝐨𝐜𝐮𝐬: Identifying patterns and building predictive models.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Machine learning, statistics, Python/R.
→ 𝐓𝐨𝐨𝐥𝐬: Jupyter Notebooks, TensorFlow, PyTorch.
→ 𝐆𝐨𝐚𝐥: Extract actionable insights from raw data.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Creating a recommendation system like Netflix.

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿

The architects of data infrastructure.
→ 𝐅𝐨𝐜𝐮𝐬: Developing data pipelines, storage systems, and infrastructure. → 𝐒𝐤𝐢𝐥𝐥𝐬: SQL, Big Data technologies (Hadoop, Spark), cloud platforms.
→ 𝐓𝐨𝐨𝐥𝐬: Airflow, Kafka, Snowflake.
→ 𝐆𝐨𝐚𝐥: Ensure seamless data flow across the organization.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Designing a pipeline to handle millions of transactions in real-time.

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁

Data storytellers.
→ 𝐅𝐨𝐜𝐮𝐬: Creating visualizations, dashboards, and reports.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Excel, Tableau, SQL.
→ 𝐓𝐨𝐨𝐥𝐬: Power BI, Looker, Google Sheets.
→ 𝐆𝐨𝐚𝐥: Help businesses make data-driven decisions.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Analyzing campaign data to optimize marketing strategies.

𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿

The connectors between data science and software engineering.
→ 𝐅𝐨𝐜𝐮𝐬: Deploying machine learning models into production.
→ 𝐒𝐤𝐢𝐥𝐥𝐬: Python, APIs, cloud services (AWS, Azure).
→ 𝐓𝐨𝐨𝐥𝐬: Kubernetes, Docker, FastAPI.
→ 𝐆𝐨𝐚𝐥: Make models scalable and ready for real-world applications. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Deploying a fraud detection model for a bank.

𝗪𝗵𝗮𝘁 𝗣𝗮𝘁𝗵 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗖𝗵𝗼𝗼𝘀𝗲?

☑ Love solving complex problems?
→ Data Scientist
☑ Enjoy working with systems and Big Data?
→ Data Engineer
☑ Passionate about visual storytelling?
→ Data Analyst
☑ Excited to scale AI systems?
→ ML Engineer

Each role is crucial and in demand—choose based on your strengths and career aspirations.

What’s your ideal role?

Join our WhatsApp channel

There are dedicated resources only for WhatsApp users
👇👇
https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z

🚀👉Data Analytics skills and projects to add in a resume to get shortlisted

1. Technical Skills:
Proficiency in data analysis tools (e.g., Python, R, SQL).
Data visualization skills using tools like Tableau or Power BI.
Experience with statistical analysis and modeling techniques.

2. Data Cleaning and Preprocessing:
Showcase skills in cleaning and preprocessing raw data for analysis.
Highlight expertise in handling missing data and outliers effectively.

3. Database Management:
Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation.

4. Machine Learning:
If applicable, include knowledge of machine learning algorithms and their application in data analytics projects.

5. Data Storytelling:
Emphasize your ability to communicate insights effectively through data storytelling.

6. Big Data Technologies:
If relevant, mention experience with big data technologies such as Hadoop or Spark.

7. Business Acumen:
Showcase an understanding of the business context and how your analytics work contributes to organizational goals.

8. Problem-Solving:
Highlight instances where you solved business problems through data-driven insights.

9. Collaboration and Communication:
Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders.

10. Projects:
List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making.

11. Certifications:
Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics.

12. Continuous Learning:
Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field.

💼Tailor your resume to the specific job denoscription, emphasizing the skills and experiences that align with the requirements of the position you're applying for.