Friday, May 20, 2022

Data Science VS Machine Learning: Which One To Pick For 2022!

Hi guys i'm smitha welcome back to my channel where i talk about all things machine learning and ai related with a very specific focus on how you guys can learn machine learning in the past i've made entire tutorials dedicated to how to learn machine learning including an entire .

Video which includes a roadmap on how to learn machine learning so that roadmap includes a bunch of useful resources such as courses and links so be sure to check out that video i'm going to link it in the description box below in today's video though we're going to be talking about data science .

And machine learning what exactly is data science and how does it compare to something like machine learning and which is the best for you guys to learn based on what your own goals are so let's get into it so what is data science data science is the process of .

Identifying patterns within data so it can be large amounts of data also known as big data but the entire process involves identifying patterns within data which are not easily found and these patterns which are identified are really helpful in making business decisions .

And also can be part of an entire solution for a particular problem there are quite a few similarities between machine learning and data science machine learning is a process also of identifying patterns within data but at the same time it involves a very significant difference .

And that is automation the whole point of something like machine learning is so that a program or a machine is able to learn from a set of data that it has been fed and also apply that to an entire set of data that perhaps it has never even seen before so this is the key difference there are .

Some aspects of machine learning which are definitely inside of data science but data science also includes something simple like let's say if you're looking at a data set of housing prices and purchases that have been made of people who have bought houses .

It could be as simple as just processing this data and maybe identifying that hey people with this particular amount of income tend to buy this type of house so it's answers which are already within the data but actually being able to identify it it could be something simple like that .

So that is also part of data science let's talk about some different examples of how data science is actually used within the industry so a really good example is a company like uber which uses data science to actually match riders with drivers in the area and also it uses data science to do .

Dynamic pricing that's why when you realize if there's a high demand for drivers in a particular area the price naturally goes up and if there is low demand the price naturally goes down so this entire process uber actually makes use of data science to take care .

Of that another huge industry which actually aggressively hires data scientists is the banking industry and also fintech so lots of banks and financial institutions make use of data science for example to do things like risk modeling so how risky is it to loan money .

Out to a certain type of individual or a certain type of company so all of these things actually require you to assess the risk of doing these type of financial transactions and that's why data science actually steps in it looks at a bunch of different factors and determines .

How how high is the risk to the bank to actually maybe give credit out to certain individuals or institutions so that is a huge use case for data science as well to get a better understanding of what exactly a data scientist does versus what a machine learning engineer does let's actually look at a company .

Like amazon and see the job postings of a data scientist at amazon and also a machine learning engineer at amazon and see what the differences are so this is a job posting for a level 2 data scientist at amazon let's take a look at the basic qualifications which are required .

For a data scientist at amazon a bachelor's degree in any quantitative discipline such as statistics math quantitative finance or operational research at least five years of experience working in analytics data science or forecasting environment .

Experience in working with databases and sql in a business environment demonstrated use of analytical packages and scripting languages such as r python sas or spss prior experience in design and execution of science or analytical projects or working extensively in large-scale databases and .

Data warehouse so the major difference between something like a data science position and a machine learning engineering position is that you definitely need to know sql as a data scientist and this is something which is not required or often not even asked for for from a machine learning engineer and .

There is a lot more emphasis on working with data and working with databases when it comes to becoming a data scientist in the preferred qualifications we see that machine learning is brought up you know if you are someone who is a data scientist and you .

Have some machine learning knowledge that is definitely a good thing to have now let's look at a machine learning engineer position also at level 2 at amazon and see what the major difference is so in the case of a machine learning engineer the basic qualifications are a .

Bachelor's degree in computer science or related field now right off the bat you notice that with data science they were asking for either a degree in math or statistics or some other sort but for a machine learning engineer it's very specifically computer science the next requirement is computer science fundamentals in .

Object orientated design data structures algorithm design etc programming experience with at least one modern language such as java c plus c sharp or python one plus years in contributing to the architecture and design of new and current systems two plus years of professional software development experience and experience .

Building large-scale machine learning models and also experience with machine learning data mining and statistical analysis there is a lot of emphasis on model building and also data mining etc but we focus a lot on actually building machine learning models in this case .

Whereas as a data scientist you would focus more on the data aspect although data scientists also are able to build models but machine learning engineers would focus on that way more another main difference between a data scientist and a machine learning engineer as you guys can see is that .

With machine learning engineers there is a greater emphasis on actually programming and building data structures and also knowing object oriented programming which is definitely not required as a data scientist so the main skills that a data scientist .

Requires is knowledge of how to handle databases knowledge of sql some basic knowledge of machine learning understanding multiple analytical functions being able to deploy statistical formulas within their models and also a strong knowledge of python or .

Sas r or scala meanwhile for a machine learning engineer we would definitely need to see expertise in computer fundamentals like data structures object-oriented programming we would need to see data modeling and evaluation skills knowledge of probability and statistics .

But obviously as a data scientist you probably need that a lot more and another thing that machine learning engineers definitely need to have is in-depth knowledge of programming skills so this is the major differences between a machine learning engineer and a data scientist .

So depending on the technologies that you plan to work with depending on your own programming skills knowledge you can definitely decide which one is the best for you are you more suited to be a data scientist or a machine learning engineer i hope this video was helpful let me know what you guys thought in the .

Comment section below and see you in my next video


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