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LETS UPGARDE TO TEQV WIL, v WILL ROCK THIS.

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For AI ML Batch-1. :: Request Reminder to shoieb &All moderator , Appriceat if Tutorial transcript provided , from Day 7 To Day 13.Thnx

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Glad to inform Achieved Data science essentials certificate.&Many Thanks to Darshan sir,Sai sir &Lu team.cheersđđš

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DATA SCIENCE INSPIRATION

This article is for those who fall into one of the following categories:

You donât have a post-secondary degree, but youâre interested in data science.You donât have a STEM-related degree, but youâre interested in data science.Youâre working in a field completely unrelated to data science, but youâre interested in data science.Youâre simply interested in data science and want to learn more about it.

Youâre probably wondering, âDo I even have a chance?â

The answer is, âYes, itâs possible.â

And the good news is that youâve already passed the first step, which is thatÂ youâre interested in data science.Â Now itâs not going to be an easy journey because you are an underdog, but use that as fuel to motivate yourself every day.

On top of that, Iâm going to give you my advice that I wish I had when I started out.

First, a little bit about myselfâŚ

I have a business degree, but Iâve been interested in machine learning since my second year of university. And so, I self-learned most of what I know today, and Iâve been fortunate to work in a few data analyst/data science jobs.

Why am I telling you this? I want to make it clear that I was once in a similar position as you!

Remember that this is a long-term goal, and thus you should expect results in the long-term. If and when youâre willing to commit 100% of yourself, I would give it at least a year before you decide whether to continue or not.

With thatâs said, letâs dive into it:

Getting into data science comes down to two things,Â growingÂ andÂ showingÂ your skills.

Â

1) Growing your data science skills

Â

Not too long ago, I wrote an article, âHow Iâd Learn Data Science if I Could Start Over.â In this article, I segmented what to learn by subject, i.e., statistics & mathematics, programming fundamentals, and machine learning.

In this article, Iâm going to segment what you should learn by your level of understanding.

Level 0: Fundamentals

You have to start with the basics, the building blocks, whatever you want to call it. But trust me when I say this, the better your fundamentals are, the smoother your data science journey will be.

Particularly, I recommend that you build your fundamentals in the following topics: statistics & probability, mathematics, and programming.

Statistics and Probability:Â if youâve read my previous articles, then youâve probably heard this for the MILLIONth time, but a data scientist is really just a modern statistician.

If you have little to no exposure to statistics and math, I recommendÂ Khan Academyâs course on statistics and probability.However, if you have some knowledge of calculus and integrals, I strongly recommend that you go through Georgia Techâs course called âStatistical Methods.â It is a little more difficult as it goes through more proofs, but it will help you understand the intricacies of each idea.

Mathematics:Â Depending on how much attention you paid in high school will determine how much time you need to spend learning basic mathematics. There are three areas that you should learn:Â calculus, integrals, and linear algebra:

Calculus is essential when it comes to anything related to optimization (which is quite relevant in data science). I recommendÂ Khan Academyâs course on calculusÂ for this.Integrals are essential when it comes to probability distributions and hypothesis testing. I recommendÂ Khan Academyâs course on integrals.Linear Algebra is especially important if you want to get into deep learning, but even then, itâs good to know for other fundamental machine learning concepts, like principal component analysis and recommendation systems. Surprise surprise, you can guess what course I recommend for this. The link is providedÂ here.

Programming:Â Just as having a basic understanding of math and stats is important, knowing the core fundamentals in programming will make your life much easier, especially when it comes to implementation. Therefore, I recommend that you take the time to learn basicÂ SQLÂ andÂ PythonÂ before diving into machine learning algorithms.

If youâre completely new to SQL, I recommend going throughÂ Modeâs SQL tutorials, as itâs very succinct and thorough.Similarly, if youâre completely new to Python,Â CodecademyÂ is a good resource to familiarize yourself with Python.

Level 1: Specialize

Once youâve learned the fundamentals, youâre ready to specialize. At this point, itâs up to you whether you want to focus on machine learning algorithms, deep learning, natural language processing, computer vision, etcâŚ

If you want to learn more about machine learning algorithms and implementation, I would check outÂ Kaggleâs Intro to Machine Learning,Â Stanfordâs course on Machine Learning, orÂ Machine Learning A-Z on Udemy. Check them out and see what suits your preferences best!If you want to learn more about deep learning, check outÂ aiâs specialization here. Itâs worth the money!If you want to learn more about NLP, here areÂ 5 free Natural Language Processing courses From Top Universities, like Stanford and Oxford.

There are so many more things that you can specialize in, so please explore more before you make a decision!

Level 2: Practice

Like anything else, you have to practice what you learn because you lose what you donât use! Here are 3 resources that I recommend to practice and refine your skills.

LeetcodeÂ is a great resource that has helped me learn skills and neat tricks that I never thought was possible. Itâs something that I leveraged significantly while job searching, and it is a resource that I will always go back to. The best part of it is that they normally have recommended solutions and discussion boards, so you can learn about more efficient solutions and techniques.Pandas practice problems:Â this resourceÂ is a repository full of practice problems specifically for Pandas. By completing these practice problems, youâll know how to: filter and sort data, aggregate data, use .apply() to manipulate data, and more.KaggleÂ is one of the worldâs largest data science communities with hundreds of datasets that you can choose from. With Kaggle, you can compete in competitions or simply take advantage of the datasets available to create your own machine learning models.

Â

2) Showing your data science skills

Â

Learning data science is one thing, but something that people commonly forget is marketing themselves â youâll eventually want to show what youâve learned. This isÂ especially importantÂ for you if you donât have a degree related to data science.

Once youâve completed a couple of personal data science projects, below are several ways for you to showcase them and market yourself:

Your resume

First, leverage your resume to showcase your data science projects. I recommend creating a section called âPersonal Projects,â where you can list two to three projects that youâve completed.

Similarly, you can add these projects in the âProjectsâ section on LinkedIn.

Github repository

I strongly recommend that you create a Github repository if you havenât already.Â And while weâre on the topic of Github, it would be a good idea to learn Git.Â Here, you can include all of your data science projects, and more importantly, you can share your code with others to see.

If you have a Kaggle account and create notebooks on Kaggle, this serves as a good alternative as well.

Once you have an active Kaggle or Github account, make sure that your account URL is available on your resume, your LinkedIn, and your website if you have one.

Personal website

Speaking of a website, I strongly recommend building a data science portfolio in the form of a website as well. HTML and CSS are very simple to learn, and it would be a fun project! If you donât have the time, something like Squarespace will work well too.

Blogging on Medium

Iâm biased because this has worked well for me, but that doesnât mean that I canât recommend blogging! With a platform like Medium, you can write project walkthroughs, like mine onÂ Wine Quality Prediction.

Non-Profit Opportunities

Lastly, take advantage of non-profit data science opportunities. I came across aÂ resourceful article written by Susan Currie Sivek,Â which provides several organizations where you can get the opportunity to work on real-life data science projects.

Original. Reposted with permission.

Related:

Essential data science skills that no one talks aboutAdvice for Aspiring Data ScientistsHow to become a Data Scientist: a step-by-step guide

This article is for those who fall into one of the following categories:

You donât have a post-secondary degree, but youâre interested in data science.You donât have a STEM-related degree, but youâre interested in data science.Youâre working in a field completely unrelated to data science, but youâre interested in data science.Youâre simply interested in data science and want to learn more about it.

Youâre probably wondering, âDo I even have a chance?â

The answer is, âYes, itâs possible.â

And the good news is that youâve already passed the first step, which is thatÂ youâre interested in data science.Â Now itâs not going to be an easy journey because you are an underdog, but use that as fuel to motivate yourself every day.

On top of that, Iâm going to give you my advice that I wish I had when I started out.

First, a little bit about myselfâŚ

I have a business degree, but Iâve been interested in machine learning since my second year of university. And so, I self-learned most of what I know today, and Iâve been fortunate to work in a few data analyst/data science jobs.

Why am I telling you this? I want to make it clear that I was once in a similar position as you!

Remember that this is a long-term goal, and thus you should expect results in the long-term. If and when youâre willing to commit 100% of yourself, I would give it at least a year before you decide whether to continue or not.

With thatâs said, letâs dive into it:

Getting into data science comes down to two things,Â growingÂ andÂ showingÂ your skills.

Â

1) Growing your data science skills

Â

Not too long ago, I wrote an article, âHow Iâd Learn Data Science if I Could Start Over.â In this article, I segmented what to learn by subject, i.e., statistics & mathematics, programming fundamentals, and machine learning.

In this article, Iâm going to segment what you should learn by your level of understanding.

Level 0: Fundamentals

You have to start with the basics, the building blocks, whatever you want to call it. But trust me when I say this, the better your fundamentals are, the smoother your data science journey will be.

Particularly, I recommend that you build your fundamentals in the following topics: statistics & probability, mathematics, and programming.

Statistics and Probability:Â if youâve read my previous articles, then youâve probably heard this for the MILLIONth time, but a data scientist is really just a modern statistician.

If you have little to no exposure to statistics and math, I recommendÂ Khan Academyâs course on statistics and probability.However, if you have some knowledge of calculus and integrals, I strongly recommend that you go through Georgia Techâs course called âStatistical Methods.â It is a little more difficult as it goes through more proofs, but it will help you understand the intricacies of each idea.

Mathematics:Â Depending on how much attention you paid in high school will determine how much time you need to spend learning basic mathematics. There are three areas that you should learn:Â calculus, integrals, and linear algebra:

Calculus is essential when it comes to anything related to optimization (which is quite relevant in data science). I recommendÂ Khan Academyâs course on calculusÂ for this.Integrals are essential when it comes to probability distributions and hypothesis testing. I recommendÂ Khan Academyâs course on integrals.Linear Algebra is especially important if you want to get into deep learning, but even then, itâs good to know for other fundamental machine learning concepts, like principal component analysis and recommendation systems. Surprise surprise, you can guess what course I recommend for this. The link is providedÂ here.

Programming:Â Just as having a basic understanding of math and stats is important, knowing the core fundamentals in programming will make your life much easier, especially when it comes to implementation. Therefore, I recommend that you take the time to learn basicÂ SQLÂ andÂ PythonÂ before diving into machine learning algorithms.

If youâre completely new to SQL, I recommend going throughÂ Modeâs SQL tutorials, as itâs very succinct and thorough.Similarly, if youâre completely new to Python,Â CodecademyÂ is a good resource to familiarize yourself with Python.

Level 1: Specialize

Once youâve learned the fundamentals, youâre ready to specialize. At this point, itâs up to you whether you want to focus on machine learning algorithms, deep learning, natural language processing, computer vision, etcâŚ

If you want to learn more about machine learning algorithms and implementation, I would check outÂ Kaggleâs Intro to Machine Learning,Â Stanfordâs course on Machine Learning, orÂ Machine Learning A-Z on Udemy. Check them out and see what suits your preferences best!If you want to learn more about deep learning, check outÂ aiâs specialization here. Itâs worth the money!If you want to learn more about NLP, here areÂ 5 free Natural Language Processing courses From Top Universities, like Stanford and Oxford.

There are so many more things that you can specialize in, so please explore more before you make a decision!

Level 2: Practice

Like anything else, you have to practice what you learn because you lose what you donât use! Here are 3 resources that I recommend to practice and refine your skills.

LeetcodeÂ is a great resource that has helped me learn skills and neat tricks that I never thought was possible. Itâs something that I leveraged significantly while job searching, and it is a resource that I will always go back to. The best part of it is that they normally have recommended solutions and discussion boards, so you can learn about more efficient solutions and techniques.Pandas practice problems:Â this resourceÂ is a repository full of practice problems specifically for Pandas. By completing these practice problems, youâll know how to: filter and sort data, aggregate data, use .apply() to manipulate data, and more.KaggleÂ is one of the worldâs largest data science communities with hundreds of datasets that you can choose from. With Kaggle, you can compete in competitions or simply take advantage of the datasets available to create your own machine learning models.

Â

2) Showing your data science skills

Â

Learning data science is one thing, but something that people commonly forget is marketing themselves â youâll eventually want to show what youâve learned. This isÂ especially importantÂ for you if you donât have a degree related to data science.

Once youâve completed a couple of personal data science projects, below are several ways for you to showcase them and market yourself:

Your resume

First, leverage your resume to showcase your data science projects. I recommend creating a section called âPersonal Projects,â where you can list two to three projects that youâve completed.

Similarly, you can add these projects in the âProjectsâ section on LinkedIn.

Github repository

I strongly recommend that you create a Github repository if you havenât already.Â And while weâre on the topic of Github, it would be a good idea to learn Git.Â Here, you can include all of your data science projects, and more importantly, you can share your code with others to see.

If you have a Kaggle account and create notebooks on Kaggle, this serves as a good alternative as well.

Once you have an active Kaggle or Github account, make sure that your account URL is available on your resume, your LinkedIn, and your website if you have one.

Personal website

Speaking of a website, I strongly recommend building a data science portfolio in the form of a website as well. HTML and CSS are very simple to learn, and it would be a fun project! If you donât have the time, something like Squarespace will work well too.

Blogging on Medium

Iâm biased because this has worked well for me, but that doesnât mean that I canât recommend blogging! With a platform like Medium, you can write project walkthroughs, like mine onÂ Wine Quality Prediction.

Non-Profit Opportunities

Lastly, take advantage of non-profit data science opportunities. I came across aÂ resourceful article written by Susan Currie Sivek,Â which provides several organizations where you can get the opportunity to work on real-life data science projects.

Original. Reposted with permission.

Related:

Essential data science skills that no one talks aboutAdvice for Aspiring Data ScientistsHow to become a Data Scientist: a step-by-step guide

Post

LETS UPGARDE TO TEQ

To,

LU Team,

This is to simply confirm that i have submitted all Assignment & Project on Data science Essentials, till date which really make our base ,foundation strong on regular practice as well best learning from great tutors,simply awaiting Result.Thanks

LU Team,

This is to simply confirm that i have submitted all Assignment & Project on Data science Essentials, till date which really make our base ,foundation strong on regular practice as well best learning from great tutors,simply awaiting Result.Thanks

Post

LETS UPGARDE TO TEQ

Appriceat Assignment. Solution to be circulated after all session of data science over to simply justifying correct Answer.Thnx

Post

LETS UPGARDE TO TEQ

@Moderator ,pls send Schedule for next exam &Project submission Date . whole list upto final certificate.Thnxđ

Post

LETS UPGARDE TO TEQ

@Sai sir,

I highly appreciate &Request if Python Numpy &Panda session ( Basic), can be Brush up by Darshan sir or recap Conducted by him as use to of his Teaching style .Thnx

I highly appreciate &Request if Python Numpy &Panda session ( Basic), can be Brush up by Darshan sir or recap Conducted by him as use to of his Teaching style .Thnx

Post

LETS UPGARDE TO TEQ

Dear sai sir,

congratulations n appriceat for coming up with all upmarket demanded courses to upskill.

But now Confusion is which to be attain ??â¤ Heart willing to attain all but Brain &Time don't permit to grasp all @Time.

As you aware that AI Ml session going on ,so other small courses like Aws,Cybersecurity, Robotic Automation, which prefee to join on Weekend..as admire that u declare to check on recorded YouTube.But if one can spare particular time on wkend ...&willing to appear for exam. ???what probability for that???

So Do we have any provision like udemy as one can write exam as per convenient time????

Coz can't focus Ai ml with Cybersecurity ,

which become too much tough to remmber all.

So pls guide me what to be done???

Make sure some1,intrested to attain alll courses, willing to be Rawdy of letsupgrade. By keeping that in mind ,pls Guideđšđđ˛

congratulations n appriceat for coming up with all upmarket demanded courses to upskill.

But now Confusion is which to be attain ??â¤ Heart willing to attain all but Brain &Time don't permit to grasp all @Time.

As you aware that AI Ml session going on ,so other small courses like Aws,Cybersecurity, Robotic Automation, which prefee to join on Weekend..as admire that u declare to check on recorded YouTube.But if one can spare particular time on wkend ...&willing to appear for exam. ???what probability for that???

So Do we have any provision like udemy as one can write exam as per convenient time????

Coz can't focus Ai ml with Cybersecurity ,

which become too much tough to remmber all.

So pls guide me what to be done???

Make sure some1,intrested to attain alll courses, willing to be Rawdy of letsupgrade. By keeping that in mind ,pls Guideđšđđ˛

Post

LETS UPGARDE TO TEQ

Appriceat Enthusiasm & sportsmanship of LU team as coming up with most Demanded courses during this pandamic but now thinking on which to be attain on priority as Topic are as such don't wona miss,Time to be schedule in manner that two course don't crashed