At a Glance

## How to Become a Data Scientist

- Acquire data science knowledge via courses or degrees

- Practice with real-world data science projects

- Secure a recognized data science certification

- Network and pursue internships for job entry

- Continue learning to progress in your career

- Upskill with advanced degrees for leadership roles

Working as a Data Scientist

## What Do Data Scientists Do?

The term data science has been causing quite a buzz over the last few years. Having earned the title of “sexiest job of the 21st century”, many people these days are considering a career in the field.

Data scientists are the mediators between the raw data collected and the various stakeholders who need to learn something from it. They act as translators, interpreting the business questions into digital commands that extract clear information from databases; and vice versa – presenting clear information from the mountains of data that they harvest.

Their tools are a combination of mathematical skills, statistical proficiency, and programming language proficiency. But to really excel, data scientists need some crucial soft skills: curiosity, the ability to ask questions, the patience to keep looking for new ways to interpret data, the willingness to constantly learn new skills and topics, and – maybe most importantly - good communication skills.

The better a data scientist is at presenting their learnings in a clear and simple manner, the easier it is for stakeholders and managers to understand what to do with those learnings. That’s why the ability to work well with others, and communicate clearly, is the secret ingredient to a successful data science career.

Differentiation between

## Data Scientist vs Data Analyst vs Data Engineer

If you’re somewhat familiar with data science, you might have heard about data analysis and engineering, as well. These are three different, distinct roles, all related to data collection and analysis, but each one dealing with different aspects of it.

Because data science is still a relatively young field, the distinction between all three is sometimes a little blurry. But to make things clearer, here’s a breakdown of the differences:

### Data Analysts

The role where data scientists often start in, data analysts are responsible for the more basic aspects of harvesting and interpreting data. They usually work as part of bigger business units, like sales or marketing, and are given specific tasks or questions to focus on.

### Data Scientists

Usually more experienced than data analysts, data scientists are tasked not only with processing data, but also with automating data harvesting tasks, and exploring new options for gaining insights from the database they work with. They tend to work in a more independent manner, and report directly to company leaders.

### Data Engineers

Tend to come from more of a programming/computer science background. They're the ones responsible for the infrastructure that lets data scientists and analysts work their magic, building and maintaining a solid foundation for complex databases.

The role you’ll end up working in depends on your background, passion, experience, and interests, as well as in the needs of your employer. Considering how dynamic the data research field is, and the various opportunities to constantly develop new skills within it, your role is likely to change and evolve as your career progresses.

That’s what’s so great about data science: the possibilities are almost endless.

5 Steps To Start

## 1. Study Data Science — Learn On Campus

The first step to becoming a data scientist is… (drumroll…) Learning data science.

Kidding aside, there’s no way around it – data science is its own unique discipline, with a specified set of tools, and regardless of your background or existing knowledge, if you come from outside the field, you will need to pick up a few new skills.

Due to its current popularity, there’s a wide range of educational options out there. Depending on your preference, you can take an online course, join a bootcamp or go the old-fashioned way and pick up some computer science books.

Another option, favoured by many, is to earn a recognised university degree in data science or a related field. At IU, we offer a Master's in computer science with specialisation in data science, that you can pursue attending face-to-face teaching by our experienced professors and lecturers, supplemented by self-study with our digital tools.

Discover our Master in Computer Science with a Data Science Specialisation:
Discover more

## 2. Practice, Practice, Practice

You don't have to wait for the end of your studies or training to start working on data science project. In order for you to really absorb all of these new terms and techniques, practical experience is key.

So start working on data science projects as early as you can, to see how things work outside of the classroom or virtual course. There are plenty of ways to do this – either through work exercises as part of your studies, free online projects, and online exercise boards.

By trying to apply what you learn from an early stage in your learning process, you can save valuable time – and avoid frustration – because it'll help you find out what works best for you. Is studying on your own efficient for you? Or do you need more support and advice? Maybe you'd like to collaborate with other aspiring data scientists?

If you'll wait for the end of your education process to start practicing what you've learned, you might realise that you've spent a lot of time working the wrong way for you. So do yourself a favour: overcome your insecurities, and practice, practice, practice!

Here is a list of some of the tools a data scientist needs to succeed:

- Web scraping tools: Essential for extracting data from websites, playing crucial roles in market research and customer analysis, foundational for aspiring data analysts.

- Machine learning tools: Indispensable for constructing models, applied in areas like fraud detection and personalized recommendations, a key component for those aspiring to specialize in machine learning.

- Data visualization tools: Crucial for creating insightful graphs and charts, aiding effective communication and trend identification, vital for those focusing on exploratory data analysis and data visualization.

- Business intelligence tools: Streamline data collection, storage, and analysis, critical for performance tracking and issue identification, particularly for business intelligence analysts.

- Instant metrics tools: Offer real-time insights, facilitating agile decision-making, essential for professionals in the fast-paced realm of data science.

- Insights tools: Extract meaningful patterns, aiding trend identification and problem-solving, a must-have for those seeking to interpret complex data sets and perform advanced data analysis.

## 3. Earn Your Certificate

Once you’re done with your studies, and completed your course or degree, you’ll have a certificate or diploma
that you can add to your CV or LinkedIn profile and share with potential employers.

Though not necessarily indicative of your actual skills, having a certificate from a successfully completed course or degree shows employers that you have the basic knowledge and tools for working in the industry. It opens doors and makes it easier to find the right opportunity for an internship – or even a junior position.

That’s another advantage of studying data science in in a structured format: if you study independently, you’ll have to work harder in order to prove your skills to potential employers and create a portfolio of your work to make up for not having a recognised certificate.

## 4. Starting Out: Networking and Internships

- Learning data science – done.

- Gaining experience by working on personal projects – check.

- Earning a certificate – in the bag.

Now what? How do you go about finding your first job, and having the title “data scientist” attached to your resume?

In today’s job market, where competition is fierce but opportunities are available on a global scale, you need to be proactive when looking to build your career. Simply relying on sending out countless applications is not enough; you need to find a way to stand out from the crowd.

That’s where having a good professional network comes into the picture. Being connected to people who work in data science, or in similar roles in companies you’d like to work for, is a great asset to use. Don’t worry if you don’t know anyone in the field personally – you can start by finding data scientists in your area, reaching out to them, and building a relationship online.

Surround yourself with people who work in the industry, so when opportunities to apply for an internship or work on a project as a junior pop up, you’ll hear about them at an early stage – and have people in your network who can recommend you to potential employers.

And be ready for those opportunities: have a portfolio of projects that you can share and take on an internship or two. Build your experience out in the real world, form connections and get valuable feedback from mentors at an early stage in your career.

Invest in your skills, knowledge, and connections on your first steps, and reap the rewards later on.

## 5. Level Up and Advance: Career Prospects

Powered through your internship and landed your first job? Well done. You are now a full-on data scientist (or analyst), ready to reap the benefits of having completed one of the most sought-after career paths in the tech industry.

But the work doesn’t stop there. The data science field is constantly expanding and developing, and if you really want to move ahead in your career, you’ll need to develop as well.

Always stay curious to new ideas, technologies, and ways to improve. Never stop learning new techniques and methods. As you gain experience and build a better understanding of what aspects of the industry interest more, you can plan your next steps. Maybe you’d like to work more on infrastructure projects, and pivot to engineering role? Or maybe you’re more inclined to management responsibilities?

Whatever you choose, keep in mind that building a career a proactive, lifelong process. Don’t wait for opportunities to come your way: make your own chances. Invest in yourself, upskill, and make your skills future-proof.

Maybe even pursue a Master’s degree in computer science with specialisation in data science, or, if you’re interested in moving to a managerial position, an MBA with a specialisation in Business Analytics can take your career to exciting new heights.

FAQ

## Becoming a Data Scientist

A Data Scientist’s role is to make sense of a company’s business data: to get answers about how customers interact with websites or products, what trends can be identified in transactions and what can be improved, and countless of other options.

Data Scientists help companies make better business decision, because they give them clear insights to analyse and work with. They play a role in product development, sales, marketing, and any other field that works closely with, or produces, large quantities of data.

o become a Data Scientist, you first need to develop the right skills. You need to pick the right study course for you: independently, through bootcamps or upskilling, or through a university.

During your studies, you’ll need to pick or improve your mathematical and statistical knowledge, and also learn some programming languages for data analysis and visualisation.

You’ll then need to build a portfolio of work, get an internship, and build enough experience to get hired for your first full-fledged job.

That’s not always the course; sometimes you can find work without doing an internship, or even before you complete your studies. But this is the usual process for most new Data Scientists, so it’s good to be prepared.

To become a Data Scientist, you first need to develop the right skills. You need to pick the right study course for you: independently, through bootcamps or upskilling, or through a university.

During your studies, you’ll need to pick or improve your mathematical and statistical knowledge, and also learn some programming languages for data analysis and visualisation.

You’ll then need to build a portfolio of work, get an internship, and build enough experience to get hired for your first full-fledged job.

That’s not always the course; sometimes you can find work without doing an internship, or even before you complete your studies. But this is the usual process for most new Data Scientists, so it’s good to be prepared.

Start building your skills and start building your profile. Look for a study path, like a degree or an upskilling course, that focuses on practical skills, so you can build a compelling portfolio of works even before you graduate.

Another important tool is your network, so try to build it as early as possible. Connect with other Data Scientists, people who work at companies you want to work for, your teachers, fellow students… use all of the resources you have.

And finally, be patient but consistent. The demand for Data Scientists is high, so if you have the knowledge, abilities and portfolio of works that prove it, you will find a great job.

Becoming a proficient data scientist is a journey that varies based on individual backgrounds and dedication. Factors influencing the timeline include prior knowledge of computer science, programming languages, and statistical analysis.

Enrolling in structured data science programs can accelerate the learning process, offering intensive courses in machine learning techniques, data analysis, and deep learning data visualization. Entry-level data scientists may benefit from learning data wrangling and exploring the latest data science trends. On average, the process may take several months to a couple of years, depending on the depth of expertise desired. Continuous learning, adaptation to the latest technologies, and staying informed about the evolving data science roadmap are crucial for a successful transition into a data scientist's role.

To be a successful data scientist, you need a mix of skills. First, you should be good at using programming languages like Python and R to work with data. Understanding statistics is important for making sense of the information you find.

Being skilled in machine learning is a big part of the job. It means you can create models that predict things. Using tools to show data in graphs is crucial for explaining findings, and dealing with big sets of data requires some know-how.

Apart from these technical skills, softer skills matter too. Things like drawing useful conclusions from data, working well with others, and organizing data efficiently are key. Learning is ongoing, and getting a degree in computer science can help build a strong foundation.

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