The Right Career in Data Science !

Nehajoshi

Published in

Catalysts Reachout

8 min readOct 2, 2022

POPULAR DATA SCIENCE JOB ROLES

Data Scientist: A Data Scientist’s primary job role is to extract consumable information from structured and unstructured data with computer programming tools and processes. Their job also includes creating methodology and blueprint to present information to stakeholders. They are also supposed to maintain databases.
Data Analyst: A Data Analyst has the responsibility of analyzing the data, identifying trends, and creating a predictive model based on data studied. Another critical responsibility of a Data Analyst is to translate findings into reports, which can be understood by the management, and help them accurately visualize the possible outcome. They are also supposed to maintain databases and data systems.
Data Engineer: Data Engineers are required to study data, develop data set processes, prepare the predictive model, and build algorithms through which stakeholders can easily consume raw data. It may include developing dashboards and reports that can be accessed and used by all stakeholders. Data Engineers need to have strong communication skills to be able to understand client’s requirements and objectives.
Data Mining Engineer: The job of a Data Mining Engineer is mainly extracting data from an extensive database and analyzing them. They are also responsible for building and maintaining software and digital infrastructure to study big chunks of data.
Data Architect: Data Architect’s role is to ensure that data used in creating a blueprint of a project is stable, secure, and available to all stakeholders at all times. The job role includes collating, organizing, centralizing, maintaining, and protecting a company or client’s data.
Data Statistician: This job role includes critical responsibilities such as extraction of data using statistical methodologies and analyzing, organizing, and contextualizing data and its subsets. A Data Statistician is supposed to conduct tests to determine the reliability and accuracy of data.
Project Manager: Data mining, extraction, testing, analysis, and application for creating a blueprint is a wide field of work that requires management to optimize the resources being used on a project. A Project Manager’s role is to oversee and guide the execution of the project. They act as a medium between the team and clients to communicate requirements and changes in the project.

TIPS TO KEEP IN MIND WHILE STARTING THE DATA SCIENCE CAREER

1. Choose the right role:

Talk to people in the industry to figure out what each of the roles entails
Take mentorship from people — request them for a small amount of time and ask relevant questions. I’m sure no one would refuse to help a person in need!
Figure out what you want and what you are good at and choose the role that suits your field of study.

2. Take up a Course and Complete it:

The next logical thing for you is to put in a dedicated effort to understand the role. This means not just going through the requirements of the role. The demand for data scientists is big so thousands of courses and studies are out there to hold your hand, you can learn whatever you want to. Finding material to learn from isn’t a hard call but learning it may become if you don’t put effort. When you take up a course, go through it actively. Follow the coursework, assignments, and all the discussions happening around the course. Now you have to diligently follow all the course material provided in the course. This also means the assignments in the course, which are as important as going through the videos.

3. Choose a Tool / Language and stick to it:

The most asked question is which language/tool should one choose?

The would be to choose any of the mainstream tools/languages there is and start your data science journey. After all, tools are just a means for implementation, but understanding the concept is more important. The gist is that start with the simplest of language or the one with which you are most familiar. If you are not as well versed with coding, you should prefer GUI based tools for now. Then as you get a grasp on the concepts, you can get your hands-on with the coding part.

4. Join a peer group:

Now that you know which role you want to opt for and are getting prepared for it, the next important thing for you to do would be to join a peer group. This is because a peer group keeps you motivated. Taking up a new field may seem a bit daunting when you do it alone, but when you have friends who are alongside you, the task seems a bit easier. The most preferable way to be in a peer group is to have a group of people you can physically interact with. Otherwise, you can either have a bunch of people over the internet who share similar goals, such as joining a Massive online course and interacting with the batch mates.

5. Focus on practical applications and not just theory:

Make sure you do all the exercises and assignments to understand the applications.
Work on a few open data sets and apply your learning. Even if you don’t understand the math behind a technique initially, understand the assumptions, what it does, and how to interpret the results. You can always develop a deeper understanding at a later stage.
Take a look at the solutions by people who have worked in the field. They would be able to pinpoint you with the right approach faster.

6. Follow the right resources:

The most useful source of this information is blogs run by the most influential Data Scientists. These Data Scientists are really active and update the followers on their findings and frequently post about the recent advancement in this field. Read about data science every day and make it a habit to be updated with the recent happenings. But there may be many resources, influential data scientists to follow, and you have to be sure that you don’t follow the incorrect practices. So it is very important to follow the right resources.

7. Work on your Communication skills:

People don’t usually associate communication skills with rejection in data science roles. They expect that if they are technically profound, they will ace the interview. This is actually a myth. Communication skills are even more important when you are working in the field. To share your ideas with a colleague or to prove your point in a meeting, you should know how to communicate efficiently.

8. Network, but don’t waste too much time on it:

A networking contact might:

Give you inside information of what’s happening in your field of interest
help you to have mentorship support
Help you search for a job, this would either be tips on job hunting through leads or possible employment opportunities directly.

9. Basic Database knowledge and SQL is a must:

SQL is the most fundamental skill for a data science professional. Knowledge of data storage techniques along with the basics of big data will make you much more favorable than a person which hi-fi words on the resume, it’s because organizations are still figuring their data science requirements. These organizations want SQL professionals that can help them with their day-to-day tasks.

10. Model Deployment is your secret sauce:

Model Deployment is not even added in many beginner-level data science roadmap and this is a pathway to disaster.

Once you have made the complete data science project, it is time for the intended user/ stakeholder to reap the benefits of the predictive power of your machine learning model. In simple words, this is model deployment. This is one of the most important steps from a business point of view but also the least taught one.

11. Keeping up with your resume game:

Make sure that you include these pointers in your next resume –

Prioritize skills according to the job role offered
Mention data science projects to prove your skills
Don’t forget to mention your GitHub profile
Skills are more important than Certifications
Update your skills and projects side-by-side and not once in a blue moon.
Overall resume counts — make sure all your fonts and format are standard all along.

12. Guidance is essential:

Coming to the most crucial one — finding the right guidance. Data Science and machine learning, data engineering, and relatively a very new field and so are its alumni. There are only a few people who have decrypted their path in this field.

SOME IMPORTANT SKILLS TO BE DEVELOPED:

Knowing how to Google. Seriously, know what and how to Google. You will inevitably get stuck and when you do, Google and Stack Overflow are your friends.

Learning on the job. This is somewhat tied to the last point. A lot of people learn on the job by Googling or talking to colleagues across the company. Every company has different databases and tools, different data cultures (not always perfect ones), workflows, and best practices; so being open and able to constantly learn on the job is essential for anyone in the data organization of companies.

Stakeholder management. All analytical efforts will eventually be used to drive business decisions. So explaining analytical results and concepts to business stakeholders and tying them back to business outcomes is an important part of data talents’ job description. Good data talents are the ones that have enough analytics knowledge and at the same time possess business acumen.

AI or Data Science?

Data Science and AI are two trending high-demand career opportunities in the market. Many aspirants aren’t sure about the decision between AI and Data Science. It is indeed confusing as AI and ML are used as the tools in Data Science and any AI related product requires strong data science skills.

Data Science and AI are not the same but they are complementary. AI techniques in extracting insights from complex and unstructured data has become an important part of the Data Science tool kit. It’s the AI which gives Data Science super powers.

Data science and AI are the two of the leading career choices and both offer a great potential for career growth. Data science job opportunities are significantly higher as it deals with mainstream data analysis providing insights for decision makers. AI job opportunities deals are fast catching up with increasing adoption of AI models in mainstream business and new AI product developments.

Artificial intelligence offers exciting opportunities in developing cutting edge AI products and solutions. AI engineers are paid higher than data science on an average with similar profiles. So if you are proficient in programming, good in machine learning and willing to spend required time and effort to learn AI skills, then AI could be a right choice for you.

But, if you are new to these technologies, it is recommended to pursue a career in data science as it is relatively easy to gain skills and provide millions of opportunities so that you can switch to data science jobs relatively easier.

So, choosing between data science and AI finally boils down to your preferences based on your skills, ambitions and interests.