How to become a data scientist in 2022: A definitive guide

Considering a career in data science is a great decision, especially with the kind of job diversity and prospects available in the industry.

The easiest way to enter the global data science industry is to enrol in a relevant programme like the MSc Data Science, AI and Digital Business programme at GISMA Business School.

Before we share more info on this course, let’s do a deep dive into what the data science industry can offer you and what kind of preparation you would need to learn data science and establish a career.

What is data science?

Simply put, data science is the study of different sets of data and information that originate from different sources like markets or business environments. Data science involves the processing of large volumes of data to derive meaningful patterns and insights that can drive business decisions.

Although an actual data science cycle can be fairly complex, you can broadly divide it into 5 primary categories:

1. Capturing data: This stage involves data scientists gathering different structured or unstructured data.
2. Maintaining data: This step involves preparing the raw data in a way that makes it possible to process.
3. Processing data: This is one of the largest steps within the cycle and involves several data processing techniques such as data cleansing and data modelling.
4. Analysing data: This involves the further refinement of the processed data to derive significant statistical insights and information.
5. Communicating data insights: In the final step, the data insights are arranged into a simpler, more consumable format for business leaders and stakeholders.

What is a data scientist?

Data scientists are the professionals who are involved in one or more stages of a data science cycle. They use different data gathering, processing and analytical techniques to deliver important insights to business stakeholders.

As a data scientist, you get to use your expertise in arithmetics, statistics and data modelling to manage huge volumes of data and uncover business solutions.

What does a data scientist do?

Most data scientists work towards developing data-driven business solutions or insights which can improve company performance or revenue. However, the exact job role of a data scientist is company-specific and can differ from one organisation to another.

These are some common duties of a data scientist:

1. Designing different data collection campaigns and deciding the data entry criteria.
2. Building and designing data processing models.
3. Using different data visualisation techniques to process the data.
4. Arranging the data insights into usable reports and simplifying complex information for the benefit of stakeholders.

What are the benefits of opting for a career in data science?

Additionally, you can also look forward to the following career benefits if you choose to become a data scientist.

1. Handsome compensation packages: Owing to the enormous impact of data science in businesses, data scientists are some of the most well-paid workers in 2022. According to a Payscale.com report, an average data scientist in Germany can earn around € 55,461 per year.
2. Job security has created a growing demand for qualified data scientists: With each consecutive year, more and more businesses are warming up to the idea of utilising data science to grow their organisations. As a result, the demand for qualified data scientists is continuously growing. This trend can be expected to continue in the future, which can contribute to job security in the data science industry.
3. Immense diversity in the availability of data science positions: The ‘data scientist’ position is a blanket term for a large number of data science roles and jobs within the industry. You can choose any data science role in line with your career aspirations and professional strengths.
4. Possibility of working on innovative and novel projects due to constant evolutions in the field: The data science field is continuously growing because of innovations in AI and machine learning. This means that you will get to work on many innovative projects as a data scientist in the future.

What kind of career opportunities can you expect as a data scientist?

Career diversity is one of the primary advantages of choosing data science as your career path. As a data scientist, you will be privy to a large number of exciting jobs and positions across different industries.

Since data science is relevant across different domains, your professional background can also make it easier to switch career domains later on.

Here are some of the most common data scientist positions that are available in the industry today:

1. Business intelligence analyst
2. Data architect
3. Senior machine learning engineer
4. Marketing analyst
5. Database administrator
6. Data engineer
7. Business statistician
8. Data analytics manager
9. Data architect

What is the future of data science?

As a data science career enthusiast, you can look forward to many exciting developments within the data science industry in the future. You can expect machine learning techniques and AI algorithms to be more mainstream within data science.

A lot of data science experts also predict that the domain will become more specialisation-driven and will sprout many new data science sub-domains. Other future trends predicted within the industry include newer data labelling techniques and faster data processing operations.

How can you become a data scientist?

With the immense career scope of data science, there’s no better time than now to join the industry. Since a data scientist career can be fairly technical, you should ensure you develop all the relevant skills and technical expertise required for the job.

There are several ways to acquire this expertise, including:

1. Regularly reading industry journals and white papers.
2. Pursuing and completing data science certifications.
3. Enrolling in a comprehensive data science and machine learning programme.
4. Interacting with industry seniors and professionals to learn from their experience.
5. Subscribing to newsletters released by different data science companies.
6. Securing an unpaid or paid internship with a data science company.

What is the most important data science skill set you need to become a data scientist?

You can divide the broad spectrum of data scientist skills into two main categories—technical skills and soft skills.

Technical skills refer to the actual industry knowledge and expertise as well as data processing and analytical techniques. They include:

1. Extensive knowledge of artificial intelligence (AI) and machine learning techniques
2. Data visualisation and data reporting
3. Risk analysis and management
4. Expertise in statistical analysis and arithmetic
5. Software programming skills
6. Knowledge of Big Data platforms, data mining, and data cleaning
7. Ability to seamlessly use cloud computing tools

Data scientists also need a wide range of soft skills to grow and excel in their careers. These include:

1. Excellent business leadership and project management skills.
2. Communication and report writing skills.
3. Aptitude for teamwork and collaboration.
4. Excellent organisational and management skills.
5. A penchant for learning new things quickly.
6. Commitment towards learning throughout the career.

What kind of data science tools should you learn to become a data scientist?

Here are some of the latest tools for data science that will be popular in 2022:

1. Alteryx
2. Tensorflow
3. PyTorch
4. DataRobot
5. Dataiku
6. H2O.ai
7. Trifacta
8. RapidMiner

How can you get started in data science?

As we mentioned earlier, enrolling in a good data science and machine learning programme can be a good way to enter the data science industry.

The MSc Data Science, AI and Digital Business programme offered by the GISMA Business School has a particularly comprehensive curriculum and is an ideal fit. The programme gives you a competitive advantage within a global data science company or an AI development organisation.

The programme equips you to work for any global company or start-up and help them grow their operations through data-backed business decisions. It can also open up career opportunities in ancillary fields like cybersecurity, Big Data and cloud computing.

Click here to enrol in this data science programme at GISMA today!

 

 

Frequently asked questions

Question 1: What are some of the most common prerequisites for the data science programme at GISMA?

Here are the entry criteria for enrolling in the data science master’s programme at GISMA:

1. A valid English proficiency test score of 6.0 in IELTS, B2 or equivalent.
2. A bachelor’s degree in a relevant subject.

Question 2: What will I learn through the data science course?

Through the data science programme at GISMA, you would be introduced to the basics of data analytics and processing. Here are some of the subjects included in the course curriculum:

1. Artificial intelligence and its applications
2. Introduction to AI and machine learning
3. Data prediction and modelling methods
4. Big Data analytics

Question 3: How long is the data science programme at GISMA?

You can complete the programme in 12 to 24 months.

Question 4: Do I need to be proficient in maths to pursue the data science programme?

While you do need a fair amount of mathematical understanding to comprehend statistical analytics, you don’t need significant expertise in maths to complete the programme.

Question 5: Do I need to know computer programming to become a data scientist?

Yes. Computer programming is an essential skill to run many data analytics platforms and tools as a data scientist. However, the course can introduce you to the relevant programming skills if you don’t have prior experience.

Question 6: What are some of the future data science qualifications I can explore after this programme?

You can look up several industry-standard post graduate certifications in data science after this programme to take your career forward.

 

 

 - This article was written by Sweha Hazari.