Various job roles after Data Science course

In 2019, there were 97,000 analytics and data science job vacancies in India, according to latest figures. Data scientists will remain in high demand.

According to the Economic Times, by 2020, there will be over 2 lakh job vacancies in data science. Because of the advancement of data science, industries may now make careful data-driven judgements.

According to studies, the need for data scientists is increasing at an exponential rate, notably in the BFSI (Banking, Financial Services, and Insurance), energy, pharmaceutical, and e-commerce businesses. The title of "Sexiest Employment of the Twenty-First Century" has been conferred to data science.

How to get a job after pursuing Data Science

Let's take a look at how to become a data scientist and how to get work in the industry.

Bachelor's Degree - To get a head start in data science, consider pursuing a bachelor's degree in mathematics, statistics, or computer science. But, if you hold a degree in a different field of study, you may always enrol in online certification programmes and courses to gain the essential skills.

Improve Your Skills - Technological talents are essential for the type of job that a data scientist undertakes. Some of these include programming language skills, machine learning techniques, data visualisation, statistics, arithmetic, and so on. They are not, however, the only ones that are relevant. You will face difficult situations that will require extra skills such as effective communication, leadership, collaboration, and so on.

Choose a specialisation - Specialisations are typically preferable to developing a specialised area of interest. Choose a speciality, such as database administration, artificial intelligence, research, or machine learning, and focus your efforts on honing your skills in that area. If you accomplish so, both your earning potential and the quantity of opportunities will increase.

Take a Job/Internship - The best way to learn about data science is to get a job (either part-time or full-time) or an internship, ideally in an entry-level position. It will not be enough to just specialise and acquire new skills. Working is the only way to learn about data science and obtain valuable experience. You will also be able to develop a portfolio to submit to potential employers. Select a company that has opportunity for growth or a small corporation where you may do a range of roles. As a consequence, you will improve your skills.

Obtain a Degree in Data Science - The next step is to supplement your present knowledge with a formal degree. After you've shown your ability on the job, you'll know where your interests lie. You should seek a master's degree in data science now that you've honed your skills in your chosen field. Individuals who do not wish to commit for an extended period of time can always select from the several certifications available.

In India, the job market is hesitant to hire a young data scientist. Everyone out there wants at least two years of experience, but how will we get it?

Creating a portfolio is critical in this situation. As you are a freshman, I assume you acquired data science through online classes. The analytical skills required to clean data and apply machine learning algorithms to it are only learned via experience; they only teach you the essentials.

Publish all of your projects on sites like GitHub so that a recruiter can see that you have practical experience and are familiar with the fundamentals when they look at your profile. This will get you a long way. A technical portfolio will illustrate the information you have previously learned when you are a recent graduate seeking a job as a data scientist.

Job Roles in Data Science Career

Data Analyst - Data analysts are in charge of a wide range of responsibilities, including data visualisation, munging, and processing. They must also run queries on the databases on occasion. Optimization is one of the most crucial talents of a data analyst. This is due to the fact that they must develop and tweak algorithms that can be utilised to extract information from some of the world's largest databases without altering the data. SQL, R, SAS, and Python are some of the most popular data analysis technologies. As a result, accreditation in these areas can readily increase your job applications. You should also be skilled at problem solving.

Data Engineers - Data engineers create and test scalable Big Data ecosystems for organisations so that data scientists may run their algorithms on reliable and well optimised data platforms. To boost database performance, data engineers also update old systems with newer or improved versions of current technology. If you want to work as a data engineer, you should be familiar with the following technologies: Hive, NoSQL, R, Ruby, Java, C++, and Matlab. It would also be advantageous if you are familiar with major data APIs and ETL technologies.

Database Administrator - A database administrator's job description is fairly self-explanatory: they are responsible for the correct operation of all of an enterprise's databases and provide or revoke its services to the company's personnel based on their needs. They are also in charge of database backups and recovery. A database administrator's important abilities and talents include database backup and recovery, data security, data modelling and design, and so on. It's a big plus if you're adept at catastrophe management.

Machine Learning Engineer - Nowadays, machine learning engineers are in high demand. Nonetheless, the work profile is not without its difficulties. Machine learning engineers are required to do A/B testing, design data pipelines, and implement common machine learning algorithms such as classification, clustering, and so on, in addition to having in-depth understanding of some of the most powerful technologies such as SQL, REST APIs, and so on. To begin, you should be familiar with technologies such as Java, Python, and JavaScript. Second, you should be well-versed in statistics and mathematics. After you've mastered both, it'll be much easier to land a job interview.

Data Scientist - Data scientists must understand business concerns and provide the best solutions through data analysis and processing. For example, they are expected to execute predictive analysis and sift through "unstructured/disorganised" data to provide actionable insights. They can also do so by recognising trends and patterns that will assist businesses in making better judgements. To become a data scientist, you must be proficient in R, MatLab, SQL, Python, and other related technologies. It can also benefit if you have a higher degree in mathematics, computer engineering, or something similar.

Data Architect - A data architect builds data management plans so that databases may be readily connected, consolidated, and secured with the greatest security methods. They also guarantee that the data engineers have access to the best tools and technologies. A profession in data architecture necessitates knowledge of data warehousing, data modelling, extraction, transformation, and loan (ETL), among other things. You should also be familiar with Hive, Pig, and Spark.

Statistician - A statistician, as the name implies, is well-versed in statistical theories and data organisation. They not only extract and provide significant insights from data clusters, but they also contribute to the development of new approaches for engineers to use. A statistician must be passionate about reasoning. They are also proficient in a wide range of database systems, including SQL, data mining, and numerous machine learning technologies.