Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
👍11❤1
1. What is RDBMS? How is it different from DBMS?
RDBMS stands for Relational Database Management System that stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables.
2.What is ETL in SQL?
ETL stands for Extract, Transform and Load. It is a three-step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into the tidy format, which will come in the second phase.Finally, we would have to load this tidy data into tools which would help us to find insights.
3. What is a kernel function in SVM?
In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
4. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
RDBMS stands for Relational Database Management System that stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables.
2.What is ETL in SQL?
ETL stands for Extract, Transform and Load. It is a three-step process, where we would have to start off by extracting the data from sources. Once we collate the data from different sources, what we have is raw data. This raw data has to be transformed into the tidy format, which will come in the second phase.Finally, we would have to load this tidy data into tools which would help us to find insights.
3. What is a kernel function in SVM?
In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
4. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
👍4❤1
©How fresher can get a job as a data scientist?©
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
👍13❤2
New developers: whenever you work on something interesting, write it down in a document which you keep updating. This will be very helpful when you need to create a resume or have to talk about your achievements in an interview. (Or for college essays.)
I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.
I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.
👍16❤7
👍2
👍4❤2
Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
👍21❤8
This post is for beginners who decided to learn Data Science. I want to tell you that becoming a data scientist is a journey (6 months - 1 year at least) and not a 1 month thing where u do some courses and you are a data scientist. There are different fields in Data Science that you have to first get familiar and strong in basics as well as do hands-on to get the abilities that are required to function in a full time job opportunity. Then further delve into advanced implementations.
There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:
Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.
This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.
You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - check & search in this channel with #freecourses
Data Science - https://365datascience.pxf.io/q4m66g
Python - https://bit.ly/45rlWZE
Kaggle - https://www.kaggle.com/learn
There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:
Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.
This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.
You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - check & search in this channel with #freecourses
Data Science - https://365datascience.pxf.io/q4m66g
Python - https://bit.ly/45rlWZE
Kaggle - https://www.kaggle.com/learn
👍43❤16😁1🤔1
👍10❤2
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
👍16❤9
Free courses to learn Data Science in 2023
👇👇
https://www.linkedin.com/posts/sql-analysts_programming-computerscience-datascience-activity-7126408061472112641-agJe
Like this Linkedin post so that it reaches to more data aspirants ❤️
👇👇
https://www.linkedin.com/posts/sql-analysts_programming-computerscience-datascience-activity-7126408061472112641-agJe
Like this Linkedin post so that it reaches to more data aspirants ❤️
👍4
Essential Data Science Skills for data scientists
👇👇
https://www.linkedin.com/posts/sql-analysts_datascience-dataskills-transferableskills-activity-7128372157230809089-GHRF
Add in the comments if anything missed in the post 😄
👇👇
https://www.linkedin.com/posts/sql-analysts_datascience-dataskills-transferableskills-activity-7128372157230809089-GHRF
Add in the comments if anything missed in the post 😄
👍6❤5👏1
ChatGPT for Data Scientist
👇👇
https://www.linkedin.com/posts/sql-analysts_chatgpt-for-data-science-activity-7128583314378043393-kYzL
👇👇
https://www.linkedin.com/posts/sql-analysts_chatgpt-for-data-science-activity-7128583314378043393-kYzL
👍1
❤5
👍4
Data Science & Machine Learning
Do you want Harvard Resume and CV Career Guide?
Nice to 200+ people interested in this
Here is a free resume guide from Harvard
👇👇
https://www.linkedin.com/posts/sql-analysts_harvard-resume-and-cv-career-guide-activity-7129694373688070144-RS2m
Like and comment on this post so that it reaches more jobseekers 😄👍
Save it for your future reference
Here is a free resume guide from Harvard
👇👇
https://www.linkedin.com/posts/sql-analysts_harvard-resume-and-cv-career-guide-activity-7129694373688070144-RS2m
Like and comment on this post so that it reaches more jobseekers 😄👍
Save it for your future reference
👍5❤4
👍4