If you want to invest in the future, invest in:

• Machine Learning
• Water Technology
• Quantum Computing
• Internet of Things (IoT)
• Augmented Reality (AR)
• Quantum Information Science
• Agri-tech and Food Technology
• Next-Gen Telecommunications
• Autonomous Vehicles and Robotics
• Genomics and Personalized Medicine
• Advanced Materials and Manufacturing

What would you add?

Comment the answer 🤔

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.

Classes That SHOULD Be Mandatory in High School:

• Taxes
• Investing
• Real Estate
• Negotiating
• Basic coding
• Building credit
• Microsoft Excel
• Personal Finance
• Entrepreneurship
• Time Management
• Money Management

What would you add to the list?

FREE Resources to learn Statistics
👇👇

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

Cold email template for Freshers 👇

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}

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 — You got this.

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.

Just do it nothing is impossible ☺️

How to Apply for Jobs in European Countries or Abroad Without an Agent
👇👇
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?

Udemy free courses with certificate
👇👇
https://t.iss.one/udemy_free_courses_with_certi

Learn Data Science in 2024

𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚

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.

𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡

There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” 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 don’t often read about in courses and books.

So, find a mentor who can teach you practical knowledge in data science.

𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️

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 👍👍

6 rules for daily happiness:
1. Start each day with gratitude—write it down.
2. Let go of grudges; they weigh you down.
3. Pursue what excites you, not what’s safe.
4. Spend time with people who lift you up.
5. Do something kind for others—daily.
6. Find joy in the little things; they add up.

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. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Can you translate your findings into easily digestible insights for non-technical stakeholders?

2. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴: Is your work focused on solving actual business problems, and are you able to pick the most efficient approach to solve them?

3. 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Are you building strong relationships with your stakeholders, understanding their needs, and providing them with regular updates?

4. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: The data landscape is constantly changing. Are you keeping up with new tools and trends?

5. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁/𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: 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. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗰𝘂𝗺𝗲𝗻: Can you understand the language and needs of the business and put your data work into context?

7. 𝗗𝗼𝗺𝗮𝗶𝗻 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲: 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?

My only motivation 😌

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.