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 donโt go your way, you might have had a brain fade, it was not your day etc. Donโt 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 donโt go your way, you might have had a brain fade, it was not your day etc. Donโt worry! Shake if off for there is always a next time and this is not the end of the world.
๐19โค2
Classes That SHOULD Be Mandatory in High School:
โข Taxes
โข Investing
โข Real Estate
โข Negotiating
โข Basic coding
โข Building credit
โข Microsoft Excel
โข Personal Finance
โข Entrepreneurship
โข Time Management
โข Money Management
What would you add to the list?
โข Taxes
โข Investing
โข Real Estate
โข Negotiating
โข Basic coding
โข Building credit
โข Microsoft Excel
โข Personal Finance
โข Entrepreneurship
โข Time Management
โข Money Management
What would you add to the list?
โค35๐10๐4
FREE Resources to learn Statistics
๐๐
Khan academy:
https://www.khanacademy.org/math/statistics-probability
Khan academy YouTube:
https://www.youtube.com/playlist?list=PL1328115D3D8A2566
Statistics by Marin :
https://www.youtube.com/playlist?list=PLqzoL9-eJTNBZDG8jaNuhap1C9q6VHyVa
Statquest YouTube channel:
https://www.youtube.com/user/joshstarmer
Free Statistics Books
https://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
๐๐
Khan academy:
https://www.khanacademy.org/math/statistics-probability
Khan academy YouTube:
https://www.youtube.com/playlist?list=PL1328115D3D8A2566
Statistics by Marin :
https://www.youtube.com/playlist?list=PLqzoL9-eJTNBZDG8jaNuhap1C9q6VHyVa
Statquest YouTube channel:
https://www.youtube.com/user/joshstarmer
Free Statistics Books
https://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
๐9โค6
Cold email template for Freshers ๐
Dear {NAME},
I hope this email finds you in good health and high spirits. I am writing to express my keen interest in the internship opportunity at the {NAME} and to submit my application for your consideration.
Allow me to introduce myself. My name is Ashok Aggarwal, and I am a statistics major with a specialization in Data Science. I have been following the remarkable work conducted by {NAME} and the valuable contributions it has made to the field of biomedical research and public health. I am truly inspired by the {One USP}
Having reviewed the internship description and requirements, I firmly believe that my academic background and skills make me a strong candidate for this opportunity. I have a solid foundation in statistics and data analysis, along with proficiency in relevant software such as Python, NumPy, Pandas, and visualization tools like Matplotlib. Furthermore, my prior project on {xyz} has reinforced my passion for utilizing data-driven insights to understand {XYZ}
Joining {name} for this internship would provide me with a tremendous platform to contribute my statistical expertise and collaborate with esteemed scientists like yourself. I am eager to work closely with the research team, assist in communications campaigns, engage in community programs, and learn from the collective expertise at {Name}.
I have attached my resume and would be grateful if you could review my application. I am available for an interview at your convenience to further discuss my qualifications and how I can contribute to {NAME} initiatives. I genuinely appreciate your time and consideration.
Thank you for your attention to my application. I look forward to the possibility of joining {NAME} and making a meaningful contribution to the organization's mission. Should you require any further information or documentation, please do not hesitate to contact me.
Wishing you a productive day ahead.
Sincerely,
{Full Name}
Dear {NAME},
I hope this email finds you in good health and high spirits. I am writing to express my keen interest in the internship opportunity at the {NAME} and to submit my application for your consideration.
Allow me to introduce myself. My name is Ashok Aggarwal, and I am a statistics major with a specialization in Data Science. I have been following the remarkable work conducted by {NAME} and the valuable contributions it has made to the field of biomedical research and public health. I am truly inspired by the {One USP}
Having reviewed the internship description and requirements, I firmly believe that my academic background and skills make me a strong candidate for this opportunity. I have a solid foundation in statistics and data analysis, along with proficiency in relevant software such as Python, NumPy, Pandas, and visualization tools like Matplotlib. Furthermore, my prior project on {xyz} has reinforced my passion for utilizing data-driven insights to understand {XYZ}
Joining {name} for this internship would provide me with a tremendous platform to contribute my statistical expertise and collaborate with esteemed scientists like yourself. I am eager to work closely with the research team, assist in communications campaigns, engage in community programs, and learn from the collective expertise at {Name}.
I have attached my resume and would be grateful if you could review my application. I am available for an interview at your convenience to further discuss my qualifications and how I can contribute to {NAME} initiatives. I genuinely appreciate your time and consideration.
Thank you for your attention to my application. I look forward to the possibility of joining {NAME} and making a meaningful contribution to the organization's mission. Should you require any further information or documentation, please do not hesitate to contact me.
Wishing you a productive day ahead.
Sincerely,
{Full Name}
๐13โค4
You don't need:
- More books
- More tutorials
- More step-by-steps.
You need execution.
Instead:
- Outline a project.
- Break it down into milestones.
- Start building the damn thing.
Quit tutorial-hell.
Start now โ You got this.
- More books
- More tutorials
- More step-by-steps.
You need execution.
Instead:
- Outline a project.
- Break it down into milestones.
- Start building the damn thing.
Quit tutorial-hell.
Start now โ You got this.
๐28๐ฅ5โค4๐คฉ1
Life is better when you are happy
but life is best when other
people are happy
because of you.
Be an inspiration and always share a smile.
but life is best when other
people are happy
because of you.
Be an inspiration and always share a smile.
โค14๐13๐ฅ1๐1
How to Apply for Jobs in European Countries or Abroad Without an Agent
๐๐
https://t.iss.one/europe_russia_jobs/4
๐๐
https://t.iss.one/europe_russia_jobs/4
Today's Question :
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Learn Data Science in 2024
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join @datasciencefree for more
ENJOY LEARNING ๐๐
๐ญ. ๐๐ฝ๐ฝ๐น๐ ๐ฃ๐ฎ๐ฟ๐ฒ๐๐ผ'๐ ๐๐ฎ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐๐ ๐๐ป๐ผ๐๐ด๐ต ๐
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
๐ฎ. ๐๐ถ๐ป๐ฑ ๐ฎ ๐ ๐ฒ๐ป๐๐ผ๐ฟ โก
Thereโs a Japanese proverb that says โBetter than a thousand days of diligent study is one day with a great teacher.โ This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you donโt often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
๐ฏ. ๐๐ฒ๐น๐ถ๐ฏ๐ฒ๐ฟ๐ฎ๐๐ฒ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ๏ธ
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join @datasciencefree for more
ENJOY LEARNING ๐๐
๐16โค6
Forwarded from Health Fitness & Diet Tips - Gym Motivation ๐ช
6 rules for daily happiness:
1. Start each day with gratitudeโwrite it down.
2. Let go of grudges; they weigh you down.
3. Pursue what excites you, not whatโs safe.
4. Spend time with people who lift you up.
5. Do something kind for othersโdaily.
6. Find joy in the little things; they add up.
1. Start each day with gratitudeโwrite it down.
2. Let go of grudges; they weigh you down.
3. Pursue what excites you, not whatโs safe.
4. Spend time with people who lift you up.
5. Do something kind for othersโdaily.
6. Find joy in the little things; they add up.
๐16โค2
Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
Knowing the tools won't be enough to become a master of data analytics!
See if your soft skills are worthy of the rank of master:
1. ๐๐ผ๐บ๐บ๐๐ป๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป: Can you translate your findings into easily digestible insights for non-technical stakeholders?
2. ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ-๐ฆ๐ผ๐น๐๐ถ๐ป๐ด: Is your work focused on solving actual business problems, and are you able to pick the most efficient approach to solve them?
3. ๐ฆ๐๐ฎ๐ธ๐ฒ๐ต๐ผ๐น๐ฑ๐ฒ๐ฟ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐บ๐ฒ๐ป๐: Are you building strong relationships with your stakeholders, understanding their needs, and providing them with regular updates?
4. ๐๐ผ๐ป๐๐ถ๐ป๐๐ผ๐๐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด: The data landscape is constantly changing. Are you keeping up with new tools and trends?
5. ๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐/๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐บ๐ฒ๐ป๐: Are you aware of the life cycle of your data products? Do you have a structured approach to plan, prioritize, and track your work?
6. ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ฐ๐๐บ๐ฒ๐ป: Can you understand the language and needs of the business and put your data work into context?
7. ๐๐ผ๐บ๐ฎ๐ถ๐ป ๐๐ป๐ผ๐๐น๐ฒ๐ฑ๐ด๐ฒ: Do you know the processes, products, and challenges of your domain?
If you want to earn the rank of master in the data field, start working on your soft skills now.
What are your thoughts on the role of soft skills in the data space?
See if your soft skills are worthy of the rank of master:
1. ๐๐ผ๐บ๐บ๐๐ป๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป: Can you translate your findings into easily digestible insights for non-technical stakeholders?
2. ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ-๐ฆ๐ผ๐น๐๐ถ๐ป๐ด: Is your work focused on solving actual business problems, and are you able to pick the most efficient approach to solve them?
3. ๐ฆ๐๐ฎ๐ธ๐ฒ๐ต๐ผ๐น๐ฑ๐ฒ๐ฟ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐บ๐ฒ๐ป๐: Are you building strong relationships with your stakeholders, understanding their needs, and providing them with regular updates?
4. ๐๐ผ๐ป๐๐ถ๐ป๐๐ผ๐๐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด: The data landscape is constantly changing. Are you keeping up with new tools and trends?
5. ๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐/๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐บ๐ฒ๐ป๐: Are you aware of the life cycle of your data products? Do you have a structured approach to plan, prioritize, and track your work?
6. ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ฐ๐๐บ๐ฒ๐ป: Can you understand the language and needs of the business and put your data work into context?
7. ๐๐ผ๐บ๐ฎ๐ถ๐ป ๐๐ป๐ผ๐๐น๐ฒ๐ฑ๐ด๐ฒ: Do you know the processes, products, and challenges of your domain?
If you want to earn the rank of master in the data field, start working on your soft skills now.
What are your thoughts on the role of soft skills in the data space?
๐13๐1
Three different learning styles in machine learning algorithms:
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
๐8โค1
Forwarded from Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books
Are you part of Rat Race?
A student who got 3.8 CGPA is unhappy because another student got 4 CGPA.
The student with 4 CGPA is unhappy because he/she is not placed in a Core Company.
Student placed in a Core Company is unhappy because his colleague has more salary than him/her.
The person having the highest salary in a company is unhappy because he/she has no time at all to enjoy their life with friends and family.
This is what happens when you get trapped in the infinite rat race. You are never happy. And you will never appreciate or be grateful for the life you have.
Come out of the Rat Race.
Art by: Steve Cutts
A student who got 3.8 CGPA is unhappy because another student got 4 CGPA.
The student with 4 CGPA is unhappy because he/she is not placed in a Core Company.
Student placed in a Core Company is unhappy because his colleague has more salary than him/her.
The person having the highest salary in a company is unhappy because he/she has no time at all to enjoy their life with friends and family.
This is what happens when you get trapped in the infinite rat race. You are never happy. And you will never appreciate or be grateful for the life you have.
Come out of the Rat Race.
Art by: Steve Cutts
๐30๐10โค6๐ฅ1