Which of the following is not a window function in SQL?
Anonymous Quiz
7%
RANK()
24%
ROW_NUMBER
59%
RANKIFY()
10%
DENSE_RANK()
😁27👍16
Which of the following is a SQL constraint?
Anonymous Quiz
8%
NOTIFY
27%
SELECT
9%
WRONG
57%
NOT NULL
👍17❤4👏3👌1
Which of the following is DML command in SQL?
Anonymous Quiz
66%
INSERT
22%
CREATE
7%
NOTIFY
6%
NULL
👍17🥰2
Which of the following function is used to return length of the string?
Anonymous Quiz
79%
LEN()
3%
LEFT()
1%
RIGHT()
17%
STRINGLENGTH()
👍11👏2😁1
Useful websites to practice and enhance your Data Analytics skills
👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlspecialist/232?single
2. Python
https://www.learnpython.org/
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👇👇
1. SQL
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlspecialist/232?single
2. Python
https://www.learnpython.org/
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://www.w3schools.com/EXCEL/index.php
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👍46❤11👏5🥰2🎉2🔥1
Data Analytics
Nice to see amazing response for SQL, Power BI, Statistics, Python & Excel :)
Data Analysis with Excel
👇👇
https://t.me/excel_analyst/2
Power BI DAX Functions
👇👇
https://t.me/PowerBI_analyst/2
All about SQL
👇👇
https://t.me/sqlanalyst/29
Python for data analysis
👇👇
https://t.me/pythonanalyst/26
Statistics Book and other useful resources
👇👇
https://t.me/DataAnalystInterview/34
Join channel as per your interest :)
👇👇
https://t.me/excel_analyst/2
Power BI DAX Functions
👇👇
https://t.me/PowerBI_analyst/2
All about SQL
👇👇
https://t.me/sqlanalyst/29
Python for data analysis
👇👇
https://t.me/pythonanalyst/26
Statistics Book and other useful resources
👇👇
https://t.me/DataAnalystInterview/34
Join channel as per your interest :)
👍51❤15👏7🔥2🥰2😁1
Do you also need data analysis projects and more free courses to be shared in this channel?
Anonymous Poll
98%
Yes
2%
Not needed as of now
👍33👏5
Which of the following function is used to capitalize first letter in SQL?
Anonymous Quiz
53%
UPPER()
29%
INITCAP()
1%
LOWER()
17%
FIRSTLETTERCAPITAL()
👍32❤1🔥1
Forwarded from Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert
🔟 Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
https://t.me/sqlspecialist/379
ENJOY LEARNING 👍👍
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
https://t.me/sqlspecialist/379
ENJOY LEARNING 👍👍
👍48❤12🔥2🥰1👏1
1. What is concurrency control in DBMS?
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking
2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.
3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.
4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.
5. What is a Recursive Stored Procedure?
A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking
2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.
3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.
4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.
5. What is a Recursive Stored Procedure?
A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.
👍25❤10🔥1👏1
Which of the following form should not have any transitive dependency in SQL?
Anonymous Quiz
41%
1st normal form
30%
2nd normal form
28%
3rd normal form
😁12👍9👏6
1. What are the different subsets of SQL?
Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.
2. List the different types of relationships in SQL.
There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.
3. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
4. What is Normalization and what are the advantages of it?
Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are:
Better Database organization
More Tables with smaller rows
Efficient data access
Greater Flexibility for Queries
Quickly find the information
Easier to implement Security
Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.
2. List the different types of relationships in SQL.
There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.
3. How to create empty tables with the same structure as another table?
To create empty tables:
Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active.
4. What is Normalization and what are the advantages of it?
Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are:
Better Database organization
More Tables with smaller rows
Efficient data access
Greater Flexibility for Queries
Quickly find the information
Easier to implement Security
👍38❤10🔥1
TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
👍71❤7🔥2
Coding and Aptitude Round before interview
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.
Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.
Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.
Resources for Prep:
For algorithms and data structures prep,Leetcode and Hackerrank are good resources.
For aptitude prep, you can refer to IndiaBixand Practice Aptitude.
With respect to data science challenges, practice well on GLabs and Kaggle.
Brilliant is an excellent resource for tricky math and statistics questions.
For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.
Things to Note:
Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!
In case, you are finished with the test before time, recheck your answers and then submit.
Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.
👍25❤5👏2
Career Path for a Data Analyst
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)
👍48❤18🍌5👨💻3🔥2🤔2