Segregating The Career Path For ML Engineer and Data Scientist - Part I
Tags: datascience, machinelearning, guide, beginners
Published: 2022-01-30 19:56:19

So literally everyone in the freshers community with a zeal for **Machine Learning** and **Data Science** tend to make one common mistake. They tend to consider the two fields Data Science and Machine Learning as one and same. What's worse is that they think that the two of them are : 

> building blocks to each other that one needs to master data science to become a machine learning master and vice versa. 

Well let me explain the key difference between the two and then we would move on with some resources that I would personally recommend for each. For the link and guidance to the projects that you can undertake to affirm your chances in both the career paths you would have to follow my tailfeed blog whose link will be provided by me at the end of this article. 

**Let's try to grab the scenario with a little pic first.** 


![Data Science and Machine Learning Key Differences and Similarities](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/zwh78rm8k4rnjje6zya5.png)

Now let me explain this in a layman manner before you start to process a plethora of articles to curb your thirst for knowledge. 

## What is Data?

Data is any meaningful information that can serve as an insight for any particular topic and whose relevance can help predict further sets of data classifying the same in **category, origin and mannerism. **

Pretty long of a definition right? Well, as a veteran data scientist data is something you need to value and therefore to value something you must understand its worth. 

## So What is Data Science now?

Well, Data Science is the ology behind understanding, manipulating and reshaping data to suck the information shown, provided, concealed and obscured from normal eyes and turn it into a **plot, an idea and an insight and tell its incomplete story.**

## And what is Machine Learning?

Well Machine Learning is the mathematical inference being drawn on the manipulated or pre-mutated data to derive or fetch an outcome. Yes, both fields have a connection. That connection is the Outcome of the processes but what they do not have in **common** is the process or **path the two methodologies will follow to achieve the outcome.**

Moreover Machine Learning is all about mathematical functions and coding and Data Science is about statistical inference that may or may not need coding all the times.  

So, the next question that is definitive to arise in your minds is which of the two **has better job perspectives**. Well data scientists are hard to be found and are usually the ones with high degrees which in general means a lot more years of dedicated studies. 
This may or may not be the case with the introduction of open source contributions and a number of organizations and competitions **announcing and developing the same**. I have myself landed offers in three startups as a chief data scientist merely due to my open source contributions. 

**Machine Learning** is technical and the **mathematical concepts here require knowledge and practice** and not some high level of creativity. A Machine Learning Engineer is a man in demand but definitely his job won't even start without the data in hand. 

Now, here comes the next perspective that with **APIs and Real Time Data Fetching libraries** at the lease you barely need to focus on the deployment of a data set to begin with for a small company. ML internships have personally provided me with very boring roles as a fresher compared to what I have been offered as a data scientist. But I have to tell you that Saurabh Moody, the Chief Data Scientist at Alpha AI had mentioned that he had chosen me for my varied knowledge in both the fields and also due to my projects being full-stack applications deploying ML on mutated data. 

So, in the IT world you do need to be a Jack Of All in order to land a great role at the beginning of your career. 

Now let me do one thing to sweeten your life. I'll just plug in the links to all the free resources you need to evolve into your data science career, followed by the ML career and on the way I would drop in the links to the paid courses here on [Udemy](my.com/home/my-courses/) and my company [The Divine Academy](divineacademy.winuall.com). 

### Free : 

For theoretical knowledge read the following : 

https://www.tutorialspoint.com/python_data_science/index.htm

https://realpython.com/tutorials/data-science/

https://www.w3schools.com/python/python_ml_getting_started.asp

https://www.tutorialspoint.com/machine_learning_with_python/index.htm

### For paid courses : 

https://www.udemy.com/course-dashboard-redirect/?course_id=1754098

https://www.udemy.com/course-dashboard-redirect/?course_id=3518544


### Free Videos : 

https://www.youtube.com/watch?v=H4YcqULY1-Q

https://www.youtube.com/watch?v=-ETQ97mXXF0

https://m.youtube.com/c/DataProfessor

You can also learn Flask and Streamlit for deployment purpose. 

I will be sharing the links to all the projects that I had made and about web integrated frameworks and the JS and Python libraries in the Tailfeed Blog of mine. 

The link to that PART - II will be updated here on this very same blog in a few days so please make sure to check back, and do let me know if I should also add a full vide explanation for detailed career path reveal or something. 

Part  II  - Done  :  [Follow here  ]( https://dev.to/souvik1406/deployment-of-ml-and-data-science-apps-1nl3 )