Over the last few years, the words ‘Data Science’ and ‘Data Scientist’ have created quite a buzz. The field of data science has been termed as the future of technology, and rightly so. After all, it has been observed that about 8 in 10 companies still struggle with data quality and management.
Data science is an expansive domain that requires a multidisciplinary skill set. If you are just starting out on your data science journey, you might feel a bit overwhelmed with the number of tools and technologies that a data scientist needs or uses. You probably are also wondering which subset of skills should you learn first or prioritize.
With the help of a well-structured data science course or program, you can easily learn about all the necessary tools such as R, Python,Tableau, TensorFlow, among other things. The best data science courses cover various Machine Learning applications and about Distributed File Systems such as Apache Hadoop. Due to the growing popularity of newer solutions for working with data, it makes complete sense to learn technologies that are currently being used in the industry.
There are various job roles and functions under the umbrella of data science. For instance, you might wish to work with Cloud Computing and thus might want to learn Cloud Computing technologies such as Microsoft Azure or Amazon Web Services. Or, you might need to develop and maintain Business Intelligence solutions or work with Microsoft Power BI and Excel.
A data science certification course can definitely help you gain these skills in a more structured manner. And each role has its own set of skills to be picked up.
For example,if you were a Machine Learning engineer, you would need to know tools such as Tensorflow or Google Cloud ML Engine. Programming languages such as R or Python and libraries such as NumPy or PyTorch would also be essential for this role.
Data science is one of the fastest-growing domains globally, and the scope of data science in India is equally promising. It is estimated that there will be over 11 million job vacancies in data science by 2026.
Here are the steps you can take in your journey to becoming a data scientist before you do a Google search for “Data science jobs near me”. You will need to invest a considerable amount of time to gain the skills required to master data science.
Preparing to be a data scientist
1. Learn important skills and technologies
Data scientists require a number of technologies to carry out their day-to-day tasks. Software like Excel and Tableau are crucial for data analytics and data science.
If you wish to get involved with more hardcore statistical tasks, you might want to learn SAS. Also, learning Business Intelligence software such as Microsoft Power BI and knowing how to use the entire suite of Microsoft solutions such as Access and Azure is definitely a plus. You can also additionally learn AWS (Amazon Web Services) or GCP (Google Cloud Platform) if you wish to work with these technologies.
Data scientists who wish to work with AML (Amazon Machine Learning) technologies will definitely find it easier to get adjusted to the AWS ecosystem. Most MNCs use Azure and Fortune 500 companies ( 95% ) hire Azure professionals thus making an attractive skill to add to your profile.
When it comes to programming languages, you can choose between Python or R, the two most popular languages for data science. While R is preferred by Statisticians, Python is easier to use and very adaptive. Most industry experts prefer using Python for data science as it is easy to learn for people who come from a non-programming or non-technical educational or professional background.
As working with data is at the core of what a data scientist does, it is important to learn techniques and concepts such as data cleaning, data mining, data analysis, data warehousing, data manipulation, data visualization, data modelling, and predictive analytics.
2. Enrol in a data science online course
You may choose to enrol in a short-term data science online course, however, most of these courses won’t cover the topics in-depth. A holistic data science certification course that is spread over a longer duration of time but provides you with industry sessions and placement support is definitely a better choice.
Hero Vired’s Integrated Data Science, Machine Learning, and Artificial Intelligence Program offers all of this with an industry-recognized certificate on the successful completion of your program. The 11-month program has been developed with the Massachusetts Institute of Technology (MIT).
Additionally, you will be gaining access to workshops, industry projects, discussion forums, and the Hero Vired community. The full-time program even provides assured placements at leading and growing businesses of India. Most classes are held in the live online format and the learning experience is a truly interactive one.
The technologies that are covered in this course are Python, R, Excel, Tableau, Azure, PyTorch, Power BI, KNIME, and SQL. These are some of the most preferred industry technology combinations. Analytics, Statistics, Programming, and advanced concepts of Machine Learning are taught in this program.
3. Get involved with projects and get internships
Being involved with a number of relevant projects is absolutely essential for getting noticed at data scientist hiring drives. The best data science courses come with a set of projects for you to work on. These projects help you learn how to deploy the skills you learnt in a real-world scenario. This also helps simulate how it would be to work as a data scientist.
Other than projects, MNCs and conglomerates also look for candidates who have finished internships. Candidates who have done internships have common workplace skills such as collaboration and communication, and also require less training than fresh graduates without any workplace experience. Candidates with work experience are always preferred as their job-specific skills are more polished.
Also, internships are a great way to showcase your skills and get help with professional references from your reporting managers. Interning helps you be more focused and get better at structured thinking for problem-solving.
4. Create a professional portfolio with your work
Building a professional portfolio is also a crucial requirement. Employers are more likely to consider job applications if they see that you have a GitHub profile or if you send in samples of your work that employers can access through Google Colaboratory. Only a resume no longer cuts it in the modern competitive hiring landscape.
You should definitely take an extra step and showcase your projects, models, and datasets. If you do not wish to host your work on a third-party platform or repository, you can even build your own website with a summary of your personal information, education, skills, and accomplishments.
5. Prepare yourself for HR and technical interviews
Preparing for job interviews is extremely important. For instance, if you do not practice answering certain intrusive questions about your project, you might not be able to provide satisfactory answers to your interviewer, making it seem like you did not spend considerable effort on it.
Technical interviews require you to solve problems or answer questions in a time constraint environment.
There are many common interview questions that are not related to data science but are essential for determining if a candidate is fit to work in the company. These include testing the culture and personality fit at the organization you are interviewing for.
Mock interviews are a great way to prepare yourself and be able to better frame your responses for questions that are bound to be asked for a particular role.
Here is where the Placement Assistance Service of Hero Vired really helps.
Stay updated with the latest industry trends
Budding data scientists must stay updated with the latest technologies in data science, modern techniques of handling and analysing data, and current industry trends. You must also make sure that you are active in Data Science and programming communities. This will ensure that you are prepared to take up any opportunity that you come across.
You Must be adept in using the latest tools. It helps to participate in seminars, conferences, workshops, and events to gain a broader understanding of the field.
“Naked Statistics: Stripping the Dread from the Data”, is an excellent book by Charles Wheelan. “Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python” is another great book for statistics with the programming languages Python and R.
If you wish to focus on just R, you can go for “R for Data Science” by Garrett Grolemund and Hadley Wickham. And, if you wish to learn Python, you can check out “Python Data Science Handbook: Essential Tools for Working with Data” by Jake Vanderplas.
Geron Aurelien’s “Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems” is also a great read for budding data scientists who wish to work with Machine Learning.
The demand for data science is only increasing, and with companies willing to pay handsome remunerations to their data scientists, it is one of the most desirable fields to work in. According to Analytics India Magazine, more than 1400 professionals in data science earn more than INR. 1 crore annually in India.
The average compensation of freshers in this field is great according to industry standards. If you invest the right kind of time in this domain, your chances of making it big are definitely higher. It is best to take your time and hone all the necessary skills that will help you in your journey.
Check out Hero Vired’s Integrated Program in Data Science, Machine Learning and Artificial Intelligence program to find out more about how you can get involved with data science and build a career in it.
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