If you are a data science aspirant, you must understand one thing that you are into this field to leverage data and the underlying tools and applications to generate meaningful and usable insights from them and help in informed and better decision-making for the business. So, instead of suggesting a new library or tool or some interview and resume tips and tricks, you must channel your entire focus on deciding what kind of a data scientist you want to become.

This is important since there are many different subfields of data science and because employers prefer to work with people who have particular skill sets rather than hiring generalists or jacks-of-all-trades "data scientists."

Just visualize yourself as a business looking to recruit a data scientist to see why. You already have a rather particular problem in mind that needs to be solved, and it will call for fairly specialized technical knowledge and subject matter experience. For instance, some businesses don't utilize (traditional) models at all, while others use basic models on massive datasets, complicated models on short datasets, models that need to be trained on the fly, etc.

Interestingly, advice given to aspiring data scientists is frequently so general- Master Python, work on classification or regression or clustering projects, develop your resume, prepare for the interview and start looking for jobs. Each of these requires an entirely distinct skill set.

This might make aspiring data scientists lose focus on particular issue classes and instead become jacks of all trades, which can make it more difficult for them to stand out or breakthrough in a market that is already flooded with generalists.

However, it can be challenging to avoid being a generalist if you are unaware of the prevalent problem classes in which you could specialize. So, here are some posts that are put together under the data science profile.

Top Designations that Come Under the Data Science Umbrella

The following are the top posts that come under the data science heading.

1.      Data Engineer

Handling data pipelines for businesses that work with vast amounts of data will be your responsibility. This entails making sure that your data is effectively gathered, fetched from its source as requested, cleaned, and preprocessed.

It may be difficult to grasp why there are people whose full-time profession is to create and maintain data pipelines if you have only ever worked with relatively tiny datasets saved in.csv or.txt files. Here are a few justifications:

a. You usually need to find other means to input data into your model because a 50 Gb dataset can't squeeze into your computer's memory,

b. Processing 50 Gb of data can take an insanely long time and frequently necessitates redundant storage. It requires advanced technical knowledge to manage the storage.

2.      Data Analyst

Data conversion into valuable business insights will be your responsibility. You'll frequently serve as a liaison between technical teams and teams responsible for business strategy, sales, or marketing. Your daily activities will involve a lot of data visualization.

Even though data analysts are crucial, highly technical people frequently struggle to comprehend why. To allow for the development of business strategies based on them, someone must transform a trained and tested model and mountains of user data into an easily comprehensible format. Data analysts assist in preventing data science teams from wasting time on issues that don't have any bearing on the bottom line.

3.      Data Scientist

Making forecasts that add value to the business will be your responsibility as you clean up and investigate datasets. Training, enhancing, and frequently putting models into production will be part of your daily tasks.  You need a method for gleaning consumable insights from a mountain of data that is too large for a human to analyze but too valuable to be disregarded. A data scientist must transform datasets into understandable conclusions because it is their primary responsibility.

4.      ML Engineer

Building, improving, and implementing machine learning models in real-world settings will be your responsibility. The majority of the time, you'll be using machine learning models as APIs or parts that you plug into a full-stack application or some hardware, but you might also be asked to create your own models.

5.      ML Researcher

Your task in data science and deep learning will be to devise fresh approaches to solving complex challenges. You will create your own solutions rather than using pre-made ones when working.

There are situations in which the five posts explained above cannot stand alone. For instance, a data scientist at a young firm might also need to be a data engineer or analyst. However, most occupations will more easily fit into one of these categories than the others, and the bigger the organization, the more frequently these categories will likely apply.

In general, keep in mind that you'll typically be better off developing a more specialized skill set to get employed. For example, if you want to work as a machine learning researcher, don't put a high priority on learning Pyspark or TensorFlow.

Decide what kind of value you would like to assist businesses in creating, then practise providing that value well. That is how you will survive and be successful in the long run.

Final Words

With this we reach the final part of the article. To summarize, we discussed how each of the designations mentioned above require specialized knowledge at times and you need to wear different hats at various points in your professional life. So, to conclude, you should strive to be a specialist and understand that advanced knowledge of one domain goes well beyond having some knowledge in all domains. However, it’s not bad to have knowledge of all domains because knowledge is always good for your professional and personal life.

So, if you are a data science aspirant with plans to go into any of the above mentioned domains, you are at the right spot. Skillslash is considered to be the best data science institute in the country with project certifications in store for you, and lucrative job opportunities. The Data Science course in Bangalore  and Full Stack Developer Course In Bangalore with placement guarantee will ensure you become a T-shaped professional, i.e., a specialist in one subject and a generalist with respect to others. To know more, Get in Touch with the support team. Good luck.