9 Tips for a Seamless Transition to Data Science for Absolute Beginners!
Are you looking for a career transition to data science? Wondering how to navigate through the numerous obstacles ahead? The demand for data scientists has been on the rise and it is really important for beginners to start on the right note and the right plan. In this article, we will go through 9 of the top tips, we have for absolute beginners that will help you navigate through this obstacle-filled career path better and efficiently and begin the transition to data science effortlessly.
Table of contents
- 1. Understand if You Can Become a Data Scientist
- 2. Understand the Career Opportunities in the Data Industry
- 3. Plan your Learning Journey
- 4. Don’t Get Derailed by Ph.D.
- 5. Should I Get a Certification?
- 6. Be Good at Programming
- 7. Make sure your Statistics is strong
- 8. Start Participating in Hackathons
- 9. Hone the Necessary Soft Skills
- Data Science Roadmap 2023
- Take Your First Step to Data Science Transition
1. Understand if You Can Become a Data Scientist
Data Science is not everyone’s cup of tea. It is a culmination of various skills and preferences. The question- ” How to become a data scientist?” is a function of learning path, planning, and consistency. The real question that needs answering is should you become a data scientist or not.
So to calm your impulses and think practically, ask yourself the following questions and determine if data science is meant for you or not-
- Do you love number crunching and logical problem solving – i.e. puzzles, probabilities, and statistics?
- Do you enjoy working/handling unstructured problems?
- Do you enjoy deep research and can spend hours slicing and dicing data?
- Do you enjoy building and presenting evidence-based stories?
- Do you always find yourself questioning people’s assumptions and are always curious to know ‘Why”?
- Do you enjoy problem-solving and thrive on intellectual challenges?
The depth of the responses to these questions will help you understand whether to enter the realm of data science or not.
2. Understand the Career Opportunities in the Data Industry
A lot of folks entering the industry get confused amongst the various job roles in the data industry and think of it as a part of data science. Comprising of diverse and interrelated job roles, it is pretty easy to slip into this confusion and derail yourself from your actual career option.
Also Read: Top 8 Job Roles in Data Science Industry That You Should Know!
3. Plan your Learning Journey
Charting a path for yourself is really important to become a data scientist. And staying on the path is even more important. Many people get stuck here. They are just not able to understand to plan it ahead.
The Certified AI & ML BlackBelt Plus Program program does the charting for you. Its intense 18-month structured roadmap has been built by industry experts with relevant experience to make sure that you get a reasonable time to understand a concept and proceed at your pace.
4. Don’t Get Derailed by Ph.D.
No! You do not need a Ph.D. to become a data scientist!
To understand this, let’s broadly divide the role of a data scientist into two categories:
- Applied Data Science Role
- Research Role
It’s important to understand the distinction between these two roles. Applied Data Science is primarily about working with existing algorithms and understanding how they work. In other words, it’s all about applying these techniques in your project. You DO NOT need a Ph.D. for this role.
Most folks fit into the above category. Most of the openings and job descriptions you see or hear about are for these roles.
But if you are interested in Research, then yes, you might need a Ph.D. Creating new algorithms from scratch, researching them, writing scientific papers, etc. – these fit a Ph.D. candidate’s mindset. It also helps if the Ph.D. adds to the domain you want to work in. For example, a Ph.D. in linguistics will be immensely helpful for a career in NLP.
5. Should I Get a Certification?
Well, there is not a definite answer as there are many ways to become a data scientist.
In simple words, certifications definitely matter but because of the skills you have gained as part of the certification and not because of the certificate itself.
Over the decade, many data science certification courses have popped up leading to the generalization of these courses. Now anyone can just take up a certification and claim to be a data scientist but it doesn’t work like that when you apply for a job.
The thing to note here is, recruiters weigh your projects and skillset more than the certificate earned.
In the end, it all boils down to the interview process. The interviewer will test you from each angle possible. So make sure you practice as many projects as possible and have good clarity over the fundamentals.
Now if you go for a certification make sure you take into account the factors like- Time, Skills Taught, Mentors, Prerequisites, Mentors, and cost.
The Certified AI & ML BlackBelt Plus Program program is one of the unique courses that stress foundational understanding. The course includes 100+ hours of live classes and weekly to 1:1 mentorship sessions to make sure you understand everything easily.
6. Be Good at Programming
With multiple languages in the frame, it is really important to be a good programmer in at least one language.
If python is your language, then to be a good enough data science professional in this vast space, you must be well-practiced with base Python and its operations, its basic machine learning libraries like Pandas, NumPy, Scikit Learn.
Also, you should be able to smoothly write custom functions, generators, and so on. Even if you can’t optimize your code at this stage that is fine. You should be able to transform your well-thought operations into a code.
You don’t need to master all the languages but choose one and master it over time. If you believe that you want a holistic view of data science languages and tools you can check out the Certified AI & ML BlackBelt Plus Program program where machine learning experts teach you Excel, SQL, Python, and its libraries from simple Pandas to advanced Keras!
7. Make sure your Statistics is strong
Statistics is the grammar of Data Science.
Yes, Yes, Yes!!! You need to know statistics in order to land a data science job.
But don’t be afraid. You do not need to have a background in Statistics. But make sure the statistical topics relevant to data science is at your tips as it is a part of the foundational knowledge. Some of these topics include.
- Descriptive Statistics (mean, median, mode, variance, standard deviation)
- Inferential Statistics (hypothesis testing, z test, t-test, significance level, p-value)
- Statistical analysis (linear regression, forecasting, logistic regression)
This is a rough and basic list of topics that you must master and this won’t take much of your time if you find the right resources.
8. Start Participating in Hackathons
Data science is more about practical intuition than theoretical understanding. You need to have a notion of the best algorithm, the best data cleaning technique, etc once see the data irrespective of whether you have an in-depth theoretical understanding or not. And Hackathons help you build this intuition.
Data Science Hackathons are an excellent stepping stone in your data science journey. You get to practice your skills on a dataset, showcase it to the world, and even stand a chance to win prizes.
These hackathons and competitions have increased multi-fold in the last few years as more and more people want a piece of the data science cake. Most aspiring data science professionals include these competitions in their resumes.
Make Kaggle and other hackathon platforms your permanent playground. Also, do have a look at Analytics Vidhya’s hackathon platform- DataHack
9. Hone the Necessary Soft Skills
You cannot be more wrong if you think making a model analyze and predict the future is more than enough for you to become a top-notch data scientist. There are several soft skills surrounding this domain that needs to be honed for effective model building and usage across departments.
Let’s have a look at some of these soft skills-
- Communication Skills – We just cannot stress enough how important this skill is. The insights from your model need to be communicated effectively to all the stakeholders and a raw data science model cannot be used for communicating with non-technical people involved in business decision making.
- Storytelling Skills – The way you communicate the insights from data also determines your ability as a data scientist. One of the examples of good storytelling skills is to depict the per-day box office collection of a movie for a bar graph and not in detailed numerical form. The former one just makes it easy to analyze when the ticket sales soared and when they dropped.
- Structured Thinking – Thinking of the problem from myriad perspectives and breaking down the problem is any data scientist’s prized possession. It helps data scientists factor in the relevant thoughts about the data and the objective from multiple viewpoints.
- Curiosity – You as a data scientist are required to keep on asking questions. Questions about which algorithms, the problem, ultimate objective from the standpoint of various stakeholders, etc. This will enhance your understanding of the problem and help you better chart the model.
Data Science Roadmap 2023
Take Your First Step to Data Science Transition
In this article, we have discussed 9 of the most important tips that will seamlessly help you transition to data science. Hope this article clears some of your doubts. You can find Certified AI & ML BlackBelt Plus Program as one of the most unique courses that have practically incorporated all the above tips and serve as a single stop to help you become a data scientist.
Here are links to some additional resources that will enhance every beginner’s understanding of the data science spectrum:
- 8 Things you Absolutely Should Know Before Starting your Data Science Career
- 5 Must-Watch Talks Before your Next Data Science Hackathon (featuring SRK, Dipanjan Sarkar, and more!)
- Starting your First Data Science Project? Here are 10 Things You Must Absolutely Know
- 6 Top Tools for Analytics and Business Intelligence
In no way does this article suggest that the list of questions is exhaustive. Feel free to comment below any other data science transition tips that you think will help the community.
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