By Katherine Olowookere

Jan 7, 2023

What bugs the minds of most beginners in Data Science (especially those coming from a non-IT background) is the question of where to begin. The Data field is so wide that figuring out where to begin all by yourself can be very overwhelming.

So many 'coaches/experts' exaggerate the problem and scare away many newcomers by emphasizing that they cannot become data scientists until they have mastered complex statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. This is false.

Yes, at some point in your data career you might need to master some of these concepts to solve some complex problems, however, you don't need to master all of those skills to begin a career in data science.

Where exactly do you begin in your Data Science career? In this article, I have explained the entire process of how you can successfully kickstart your Data Science career.

Where to begin in DS? 5 Steps You Have to Go Through

1. Know Where You Are

The response to the question of where to start differs from person to person and also depends on several factors. To answer the question you need to look inwards and personally consider where you are currently in your journey.

You need to evaluate your knowledge, past experiences, strengths, and weaknesses to figure out where to begin. The first step is to know where you are now.

Are you coming from a technical background or a non-technical background? Do you have any preliminary knowledge about computers and programming? Do you understand Mathematics, Statistics, or Business?

Before you dash into anything, understand where you're coming from and the knowledge and experience you have with you. The answers you provide to this question will determine where you are to begin.

For example, an individual from a technical background should have a basic/general understanding of how computers work on the backend, coding, and programming.

But the situation is totally different for an individual joining the Data Science field from a non-IT background.

Understanding yourself is a vital part of getting started.  Without self-awareness, it will be difficult to make the right decisions. This self-awareness can be developed by working with a mentor who will be honest with you and can help you in evaluating your strengths and weaknesses.

Once you understand your current position, strengths and limitations, then you can then begin to look into different Data science career options that match your existing skills and experiences.

2. Know What Is Possible

Millions of businesses on the planet have been impacted by some sort of AI and/or data science in recent years. Data is quickly becoming one of the most in-demand disciplines, with applications in a wide range of sectors like healthcare, banking, gaming, advertising, etc.

You need to consider the specific field of business you want to apply Data Science and the Data Science path in which you want to specialize. The Data Science career path is complex as it combines several skills from various fields so you have to know exactly what it is you want to apply Data science to. You must take this part of the assessment early on so you don't have to waste your precious time learning things you don't need.

Some of the skills you have to learn as a beginner in Data Science include

• Hard skills: programming languages (Python or R), deep learning, data visualization, and data mining.
• Soft skills: critical thinking, adaptability and flexibility, collaboration and teamwork, communication, problem-solving.

Read more about the different Data Science career paths right here in this linked article.

3. Set Goals

As you begin your Data Science journey, begin with specific goals in mind. Setting goals is important because it gives you a framework to achieve milestones as you kick-start your career. Wanting to do or complete certain things in life is great and goal setting provides a path for you to actually do them and attain success.

Make a plan each day to study and show up. Be SMART about the goals you set. Your goals should have five key elements to make them achievable. These five elements make up what is known as the “SMART goal” framework. SMART stands for Specific, Measurable, Achievable, Relevant, and Time-based.

As you set up your SMART goals you should ask yourself the following questions:

• What am I trying to achieve?
• What are the pass/fail standards?
• How will I gauge how far I've come?
• Is this objective reasonable considering my expertise and skill level?
• Have I got the time and resources to get this done?

4. Work with Discipline: 

It is not just enough to set goals. To achieve goals you have to be disciplined. There will be days when motivation will not be enough to get you to go after those goals you have set, especially at the beginning stage when there are no results to motivate you. These moments demand discipline.

Self-discipline gives you the ability to stay on track despite obstacles, distractions, or simply not feeling like doing your work.

Cultivating discipline will allow you to show up every day, meet deadlines, and give your best effort while learning. But no one ever wakes up one day suddenly blessed with self-discipline. You need to cultivate it.

To cultivate discipline you have to

• Start small and be consistent. There is a lot to learn and practice so break down every stage into smaller steps and take your learning one day at a time. Your consistency and discipline to show up every day and complete each bit of your learning and projects is all that matters.
• Realize that there’s never a perfect time to get to work. Start today! Instead of waiting for the perfect studying or practice time create a daily study routine/schedule and religiously follow it. You won't need to rely as much on motivation when you have a solid study schedule.

Since starting your study session is already a part of your daily routine, you won't engage in an internal debate over how you should be studying.

5. Continue Learning On The Job

When it comes to getting a job, you must show employers that you have the necessary skill set. The perfect way to show your expertise and the skills you have is by doing a project.

An excellent portfolio project will enable you to demonstrate to a potential employer that you possess the skills necessary to be successful in a data science career rather than simply telling them you have passed a bunch of Data Science courses in school. Many businesses prefer candidates with practical experience.

Other ways you can show your skills are by entering into Kaggle competitions and contributing to open-source projects through GitHub and similar platforms.

Any Data Science project you choose to work on should display your interests, technical skills, and passion. The results you get after working on these projects will attract potential employers to you.

Know that Data science portfolio projects aren’t easy to finish. You’ll hit multiple walls and you’ll have to juggle other commitments. Working on something you are passionate about will help you push past your struggles and ensure you create a project you’re proud of which will also help you land a job.

From there your knowledge and abilities grow and you can advance the ladder by gaining more experience and learning more to become better.

Conclusion

Data science is a field that would require a lifetime to fully understand because there is so much to learn. However, you do not have to master everything before you can begin your career. Evaluate your current position and just start! You can begin now with our free data analytics beginner course.