I want to tell you that while the job of a data scientist might seem attractive and lucrative from the outside, starting a data science career certainly has its difficulties and challenges. Now, don’t let that bother you. I’ll help you tackle these challenges by sharing with you 10 tips for starting a data science career. Let’s start with the first one.

Tip #1: Determine the Role That Suits You Best

This is the most important tip because choosing a career path in data science is anything but straightforward. There are various roles you can choose from. Just imagine being confronted with these different choices:

Data Analyst

Data Scientist

Business Analyst

Data Statistician

Data Engineer/Data Architect

Big Data Engineer

Data and Analytics Manager

Machine Learning Engineer

Database Administrator

Business Intelligence Professional

Data Ecologist

Yes! There are various job titles in data science. But each job title entails the handling of data in a unique manner. And it’s easy to get confused about the different responsibilities in each role.

Hence, your choice of the role that suits you best depends hugely on your academic background and work experience. For instance, if you have a statistics background, you’ll find it somewhat easier if you opt for a career as a data statistician.

Get in touch with people who are already employed in a role that interests you. (In the current scenario, LinkedIn is a good bet.) Identify the roles that are in demand and find out what each one of these roles requires.

Evaluate your strengths. Be honest with yourself. Based on your strengths, identify the role that closely aligns with your specialty and interests. (If you are changing fields, be cautious. Make an in-depth analysis of the role you would be comfortable with, and that would really interest you.)

Work with a mentor or a career coach. A mentor will walk you through the steps you’ll need to take. A career coach will present you with a set of actions to take, such as taking up a course or applying for a specific role.

Mistakes to avoid:

1. Jump hastily into a career path without doing thorough research.

2. Fail to assess whether the role you choose would be a good match for your long-term (not short-term) career goals.

Tip #2: Take Up a Course That Is Relevant to Your Chosen Role and Complete It

Once you have a clear understanding of the role that suits you best, the next logical step would be to put in a dedicated effort to upgrade your skills that this role entails.

Since the demand for data science professionals is enormous, there are thousands of courses and programs out there for you to choose from. Finding the right material to learn from is easy. But you still need to make an effort to learn and complete the course/program you enroll on.

Whether you choose a free course or a paid course is irrelevant. But what is relevant is you need to complete the course you take up. Course completion will also enable you to demonstrate your expertise in your chosen area.

Make every effort to follow the course material diligently. And make a sincere effort to complete the assignments/homework/exercises, too. You’ll get a clearer picture of your chosen field only when you do a course right from the beginning to the end.

AI Sciences has a range of competitively priced courses that can get you started in the data science universe today.

Tip #3: Select One Language/Tool and Stick to It

While being proficient in more than one data science language would be great, it may not always be practical. Needless to say, the data science language/tool you choose is crucial. But be warned. This is a difficult choice, especially for a beginner.

The best way to begin your data science journey would be by picking any of the mainstream languages/tools. The top ones include Python, C++, SQL, R, MATLAB, Java, Javascript, TensorFlow, Swift, Scala, and Julia.

A language, at best, is simply a means for implementation. What’s important is you need to remain focused on understanding the concept.

Python, with its extensive library support, holds a clear edge over the others.

Tip #4: Don’t Spend Too Much Time on Theory. Focus on Practical Applications, Too.

Beginners make the mistake of focusing too hard on theoretical concepts while they undergo a training course. As a result, they fail to give equal importance to the practical applications of the theoretical concepts they are learning.

At AI Sciences, we have taken a hands-on approach to address this concern. All our courses are specially designed learning by doing courses. We give the same importance to practical applications of the theoretical concepts you learn.

The best way to hone your practical data science skills is by completing all the homework tasks/exercises/assignments at the end of each section/module. Make sure your solutions are correct. In case they are not, interact with the course instructor and clear your doubts.

Another effective approach to developing your practical data science skills is to participate in data science competitions. A data science competition will help you augment your skills even if you are a beginner.

Tip #5: Be Prepared to Discuss Projects

Are you serious about starting your data science career on the right note? Then listen carefully.

When you have projects in your portfolio, it is easy for you to answer the “How would you do this?” type of interview questions.

All AI Sciences courses have mini-projects that you need to complete. We want to ensure you’ll not be giving vague answers at an interview. Instead, you’ll show the interviewer concrete examples of how you are prepared to handle a real-world situation at the workplace.

Make sure you understand the complete data science workflow. That is, you need to learn the right way to piece everything together. This is rather simple if you complete the mini-projects in our courses that give you an opportunity to practice key steps (i.e., model training, data cleaning, etc.).

Review the mini-projects you have completed. Before you attend an interview, take the time to practice project presentations from any course you have completed.

Tip #6: You Don’t Need to Code Every Algorithm from Scratch

In any profession, it’s common for beginners to miss the forest for the trees. You don’t want to be wasting too much of your time on the nonessentials. Let me explain.

For instance, while algorithms are an essential feature of data science, the workplace reality today is that algorithms are becoming similar to commodities. Easily available cloud-based solutions and mature machine learning libraries make your job simple. You don’t need to code every algorithm from scratch.

You need to pay attention to only one aspect of algorithms. Learn how to apply the right algorithm in textbook style in the right settings. The best way to do this is to pick up versatile machine learning libraries, such as SciPy NumPy, Sci-kit-Learn, TensorFlow, and Keras in Python. Likewise, you can pick up CARAT, RPart, Random Forest, Nnet, KernLab, and Word Cloud in R.

Make an effort to understand the strengths and weaknesses of the most widely-used machine learning algorithms. Remember, this can keep changing, as data science is a highly dynamic field.

Tip #7: Develop Domain Knowledge

First up, you need to understand that data science does not exist in a vacuum. All this while, the focus was on acquiring machine learning knowledge and technical skills. That’s perfectly alright. But you can’t think like a beginner all the time.

The only way you can stand above the competition is by learning more about the specific industry you wish to enter. So, developing domain knowledge is a decisive factor in landing a job. Let me explain.

For instance, if you’re interviewing for a job in the healthcare industry, brush up on some fundamental healthcare concepts. Likewise, if you’re interviewing for a job in the legal industry, brush up on some basic legal jargon. And if you’re interviewing for a job in a startup, gain a competitive edge over other candidates by learning about its market.

Hence, taking a bit of extra initiative is one of the best things you can do in the data science profession. It can not only help you to break into the data science universe, but it can also pay you huge dividends in terms of a higher pay package.

Tip #8: Improve Your Communication Skills

This is an area in which many entry-level data science aspirants falter. In most firms, the size of their data science team is still very small. At least, this is the case when compared to their marketing team or accounting team. So, while an entry-level salesperson or accountant will often be managed by a senior marketing or accounting executive, entry-level data science professionals tend to work in a somewhat cross-functional setting.

Interviewers will typically assess your ability to communicate with coworkers—who will be from varied technical and mathematical backgrounds. This is where your communication skills play a vital role. You need to have the ability to explain complex technical concepts even to non-technical audiences.

If this is something that isn’t your natural strength, you can improve in this area by deliberate practice. Try explaining an algorithm you used to complete a project in your course to a friend. Make sure your explanation has minimal technical jargon.

The other thing you could do is prepare a list of short answers to common interview questions. Practice delivering these answers with a friend or in front of a mirror. Again, make a conscious effort to cut out technical jargon.

Another effective practice strategy is analyzing various datasets, drawing out the main insights, and presenting all your findings.

Tip #9: Avoid Using Technical Jargon in Your Resume and Tailor Your Resume to the Specific Role

This is another vital area in which you can easily trip. Remember, your resume is the passport to your dream data science job. But using too much technical jargon in your resume will not make you sound smart. In fact, suffocating your resume with technical jargon will have the opposite effect.

Use the text in your resume to paint a picture—a clear one. And use short bullet points to tell your story. Your resume should actually be a statement that communicates the impact you could bring about to the firm. Let me elaborate.

For instance, instead of simply listing the libraries or programming languages you’ve used, explain how you used them. And when you explain the results you got, give a clear hint that you are capable of delivering similar results at the workplace every day.

Less is more. Don’t list all your skills. List only the most important ones and emphasize them, too. Remove other distractions, and give your important skills the space to shine.

An important time-saving tip: Create one master template of your resume. Spin off different versions whenever the need arises. Ensure your resume is tailored to the specific role you are applying for. This will help you in keeping each version of your resume clean.

Tip #10: Update Your Knowledge Constantly

I saved this one for the end for a reason.

Data science is, without a doubt, one of the fastest evolving fields today. What was relevant just two years ago is no longer relevant. This is the reason you need to keep updating your knowledge all the time.

Data science blogs present you with the most cost-effective way to learn more about this field. You can gain an inside perspective on everything related to your job as a data science professional. You also get an opportunity to stay informed of the most recent happenings within the industry. Data science blogs offer you useful advice on learning resources, as well as opinions from authority figures and current news and trends.

Joining a peer group is another cost-effective way to update your data science knowledge. A peer group will keep you motivated. Motivation—or rather staying motivated constantly to learn more—is a decisive factor when you are keen on starting a data science career.

Honestly, it can be a bit daunting when you have to do it all alone. But an online peer group helps you stay in touch with a bunch of folks who share identical career goals. When you interact with people who are pursuing similar data science courses as yourself, learning new concepts can seem less burdensome.

The other thing is you can have compelling technical discussions over the internet. Some of the online forums that present you with this kind of environment include:


Analytics Vidhya

R-Team for Data Analysis

Data Scientists