SQL Free Interview Questions and Cheatsheet

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3 Completely Free SQL resource
– 100 SQL Interview Questions
– SQL Cheatsheet
– 35 SQL Important topic videos

SQL Free Interview Questions and Cheatsheet

Why SQL?

SQL is the bread and butter of the Analytics domain, you can do wonders with only SQL in hand. SQL will help you get a good job in Analytics and you will be able to provide values to your organisation.

There are multiple ways to build your expertise in SQL:-
– The Data Monk 250 questions e-book
– The Data Monk 35 most asked topic explanation video on Youtube
– Hacker Rank SQL queries
– w3school SQL basics
– Leetcode SQL questions

– 1 hour Mock Interview with The Data Monk

SQL (Structured Query Language) plays a crucial role in analytics for several reasons:

1. Data Retrieval: SQL is primarily used to extract data from databases. In analytics, data retrieval is a fundamental step to access the information needed for analysis. SQL allows users to write queries to retrieve specific data sets or perform complex joins and aggregations, enabling analysts to extract the relevant information efficiently.
2. Data Transformation: Analytics often involves transforming raw data into a more suitable format for analysis. SQL provides powerful capabilities for data transformation tasks such as filtering, sorting, grouping, and aggregating data. By utilizing SQL, analysts can manipulate the data to prepare it for further analysis.
3. Data Aggregation and Summarization: Aggregating and summarizing data are crucial aspects of analytics. SQL offers various functions like SUM, AVG, COUNT, MAX, and MIN that enable analysts to aggregate and summarize data across different dimensions. These operations are vital for generating insights, creating reports, and making data-driven decisions.
4. Data Integration: Analytics frequently involves working with multiple data sources, such as databases, data warehouses, or data lakes. SQL provides mechanisms for integrating data from different sources through joins, unions, and subqueries. By leveraging SQL’s data integration capabilities, analysts can combine and consolidate data from various systems, facilitating comprehensive analysis.
5. Data Quality and Cleansing: Ensuring data quality is critical for accurate and reliable analysis. SQL allows analysts to identify and handle data quality issues, such as missing values, duplicates, and inconsistencies. SQL’s data manipulation capabilities can be used to clean and preprocess data, improving its quality and suitability for analysis.
6. Ad Hoc Analysis: SQL enables analysts to perform ad hoc analysis by allowing them to write queries on the fly. This flexibility allows analysts to explore data interactively, ask ad hoc questions, and gain insights in real-time. SQL’s interactive nature makes it a powerful tool for exploratory analysis and hypothesis testing.
7. Performance Optimization: Efficient query execution is crucial for handling large volumes of data in analytics. SQL provides mechanisms for optimizing query performance, such as creating indexes, using appropriate join strategies, and writing efficient queries. By optimizing SQL queries, analysts can achieve faster and more efficient data retrieval and analysis.
8. Reporting and Visualization: SQL is often used in conjunction with reporting and visualization tools to generate meaningful reports and visualizations. Analysts can write SQL queries to extract the required data and feed it into reporting or visualization tools for creating dashboards, charts, and graphs. SQL’s ability to retrieve and manipulate data makes it an essential component in generating insights and communicating findings effectively.

2200 Questions to Crack ANY Analytics Interview

Crack ANY Analytics interview from Fresher Analyst to Analytics Manager by going through this book with 2200 Interview Questions (75% off as of now)

The Data Monk Product and Services

1. Youtube Channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
   Link – The Data Monk Youtube Channel
   
2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
   Link – The Data Monk website
   
3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions
   Link – The Data E-shop Page
   
4. Mock Interviews
   Book a slot on Top Mate
   
5. Career Guidance/Mentorship
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6. Resume-making and review
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The Data Monk e-book Bundle 

1.For Fresher to 7 Years of Experience
2000+ interview questions on 12 ML Algorithm,AWS, PCA, Data Preprocessing, Python, Numpy, Pandas, and 100s of case studies

2. For Fresher to 1-3 Years of Experience
Crack any analytics or data science interview with our 1400+ interview questions which focus on multiple domains i.e. SQL, R, Python, Machine Learning, Statistics, and Visualization

3.For 2-5 Years of Experience
1200+ Interview Questions on all the important Machine Learning algorithms (including complete Python code) Ada Boost, CNN, ANN, Forecasting (ARIM