It would be pointless to deny this reality; let’s accept it. Data science is still the buzzword of the day, even if other technologies are booming in the IT sector. The World Wide Web’s (WWW) creator, Tim Berners-Lee, declared, “Data is a priceless thing and will live longer than the technologies themselves.”

Despite the high demand for data scientists, there is still a startling shortage of data science talent. Data science’s potential has never been more apparent than it is now. As a result, there is a genuine need for data science experts in the sector.

According to reports, by 2023, there will be a 35% increase in the need for skilled professionals in data science. The number of jobs in data science is expected to increase by 2,720,000 in 2023 alone. Jobs in data science and analytics are expected to expand by 15%.

Aim to learn data science right away.

Every organization now has access to data or big data. Here, data scientists have a significant impact. Now that they have mastered these tools and technologies, they can quickly find answers in the obtained data and offer useful insights.

It requires more than just being able to evaluate large amounts of data to succeed as a data scientist. You also need to understand the organization’s business operations and determine how your contribution will affect the organization.

The World Economic Forum estimates that we produce the following amounts of data each day: 

• 294 billion email messages

• 500 million tweets per day or more

• WhatsApp messages totalling 65 billion

• Daily searches total 5 billion.

• 4 GB of data are generated from a single linked automobile.

• 4 PB of data were only used for Facebook.

Thus, by the time 2025 rolls around, 463 exabytes of data—equivalent to 212,765,957 DVDs—will have been produced.

From these numbers, it’s interesting to note that demand for data science skills will continue to soar as daily data production increases. As a result of this, there are many institutions providing the best data science course, for people aspiring to become data science professionals. 

It’s not as easy as many make it to be to get your first data science job after learning R and Python. Finding a career requires a lot of time, and obtaining a relevant job is considerably harder. One of the first and most important tasks a data science professional today needs to learn is how to identify the types of job opportunities based on their experience.

You can use what is provided here. Make your choice.

The three major “Ds.”

1. Data Analyst

Who are they?

Getting hired as a data analyst is the first step in entering the field of data science. Their main duty is to examine the company’s data, respond to business inquiries, and share their findings with the other team members.

For instance, the sales team may ask an analyst to review the data they obtained from a recent campaign, analyze any weaknesses, and identify the strengths. They may occasionally be asked to analyze, purify, illustrate, and communicate the data’s results.

What is the salary of a data analyst?

A data analyst’s starting salary is about USD 68,752 annually.

Top Skills needed

• R and Python programming fundamentals
• Mathematics and statistics
• SQL statements
• Data collection 
• Data Visualization

Learn these essential skills of data analysis by joining the IBM-accredited data science course in India and becoming an expert.

Career options

The majority of data scientists began their careers as data analysts. A data science job is perfect if machine learning is your area of interest. However, a data engineer is the best option if you’re interested in developing data pipelines and infrastructure.

2. Data Scientist

Who are they?

A data scientist spends time building machine learning models that aid in making the best decisions for the future based on the data gathered and cleaning the data. However, data scientists have a higher chance to develop their concepts and test them to produce useful insights.

What is the salary of a data scientist?

Annual compensation for a data scientist is around USD 128,173.

Top skills are needed as a supplement to data analyst skills.

• Statistical analysis
• Machine learning that is both supervised and unsupervised
• Data visualization tools
• R and Python programming proficiency

Career options

Once you’ve started working in the field of data science and gained some experience, you could be able to move up a level by being promoted to senior data scientist. Alternatively, you can shift your attention to a management position and become a lead data scientist. You might also project your career toward machine learning and become a machine learning engineer.

3. Data Engineer

Who are they?

The future of a data engineer’s career is predicted to be in software development and programming. Additionally, they are in charge of creating data warehouses, pipelines, and the infrastructure needed to store and access historical data.

What is the salary of a data engineer?

A data engineer can make an average of USD 132,653 annually. 

Top skills:

• SQL Advanced
• Proficient Python programming skills
• Data engineering tools

Career options

Data engineers could advance to a senior position earlier than expected. Or perhaps they will change their career to work in software development. They also have the advantage of leading engineering teams and assuming managerial positions.

So What Now? Are you prepared for a Job in data science?

You won’t become a data scientist or engineer by learning skills in those fields. Even the best employers are hesitant to hire new employees. Having said that, these applicants must make sure they can prove they possess these skills.

The best course of action is to become certified in data science. The reason for this is that you keep up with the most recent developments in the field, get to work on real-world projects, and the best part is that your skills are being acknowledged. Learnbay delivers the best data science courses in India, for data science enthusiasts. You can master the tools with the help of expert trainers, work on domain-specific projects, and gain practical experience.

Depending on your degree of knowledge, you can adopt the recommended pathway and become the next data scientist!