“A good beginning is half the battle won.” This saying is a popular one and well known. Applicable over here as well. A solid and good beginning makes your further journey smooth, and increases the chances of becoming successful.
This blog is for all the freshers who are planning to pursue a career in the field of Data Science. Over here, we are going to share some top tips on how to start career in Data Science. These tips would help in laying a solid foundation for a successful career ahead. As a fresher, one needs to make the right moves towards the start, particularly, if one is eyeing a career in a challenging field like Data Science. Doing so would prove to be beneficial in a number of ways. So, get to know what all things are required to be done at the start, to set your career on the right track.
- Tips on how to become a Data Scientist with the right start
- Find out what all to learn and which skills to acquire
The most fundamental and essential thing to begin a career in any field for that matter.
What is Data Science?
Well, in simple and the most basic terms, Data Science involves asking interesting questions, and then finding answers to those questions with the help of data. Broadly speaking, the Data Science workflow appears somewhat like below:
- Pose a question
- Collect data which could help answer that question
- Carry out data cleaning
- Explore, perform analysis and do data visualization
- Create and evaluate a machine learning model
- Put forth the results
Such kind of workflow does not necessarily require skills like expertise in deep learning, advanced mathematics etc. However, it demands knowledge of a programming language and the capability to handle data in that language. There is no doubt that mathematical fluency is required to become proficient at Data Science; but, in order to get started, one simply requires to have a basic understanding of mathematics.
Get one thing clear, although good to have, mastering of all the essential skills is not necessary to start your career in Data Science.
- Know which role would be right
As far as the Data Science industry is concerned, there are a whole range of varied roles available. E.g. Data Scientist, Data Visualization Expert, Data Engineer, Machine Learning Expert etc. These are some of the numerous roles which one can get into. Based on the background and the work experience, one can choose a suitable role amongst those available. This would prove to be fruitful. E.g. Data Engineering would prove to be a suitable option for software developers or to those having knowledge of programming.
Therefore, till the time one isn’t clear about what one want’s to become, there might be a confusion as to which skills to acquire and what role to go for.
But, how to find out which role will be suitable for me? Well, below are a few ways that would help,
- Analyse as to what you want, what are your interests, what areas are you good in etc. and then choose from the available roles.
- Have a word with the people from the industry to know and understand the details about each role.
- Seek mentorship from related people. Request for their time and ask relevant questions to them for answers. Most would not refuse.
Do not hastily go for any role. Get to know things like your liking, what the role demands etc. and then come to a decision.
Before knowing about the roles available, the most primary thing is to gain knowledge of Data Science. Data Science classes in Pune from Digital Trainee is where one can get the same. All that is required is enrolling your name.
- Gain knowledge of Python
Python and R are the programming languages which are known to be the preferred choices for Data Science. While Python is the popular one in the industry, R happens to be more widely used in academics. Despite this, both these languages possess a complete range of packages which support the Data Science workflow.
It’s fine to learn any one language amongst these, along with its ecosystem of the Data Science packages while getting started.
E.g. if one goes for Python, then there is no need to become a Python expert in order to move to the next level. Rather, one should target mastering things like data types, functions, loops, conditional statements, data structures, imports, comparisons and comprehensions. Other things can wait for later stage.
For Python, it would be a good idea to install Anaconda distribution since it simplifies the package installation process and management on Windows, Linux and OSX.
- Put in efforts and network harder
Introducing yourself to rest of the Data Scientists is one of the best approaches to learn about the various career opportunities in this field. Who knows, this could be an interaction with your future team members!
By doing this, important things like which company to work for (in terms of size, domain culture etc.), types of projects that would appeal, how to ready yourself for the job application process, etc. would become more clear and visible.
Another alternative is to enter into Data Science from another position in your organization. If your image is good, there is no harm in starting to network internally and look for an opportunity to have a discussion with the Data Science team. Of course the technical requirements need to be met.
While beginning without experience, it could be easier breaking into small sized companies. However, there is every chance that bigger companies from the tech industry having entry level programs would have better infrastructure built-in for the sake of training and mentorship.
All in all, once a network is established, it would open up the doors to request people within your network for personalized referrals. As per a study, as much as 82% of the employers have agreed upon referrals to have the highest ROI! In fact, many companies encourage their employees to be on the hunt for emerging talent by offering cash incentives.
Join Digital Trainee’s Data Science course in Pune, and not just learn Data Science in a practical manner, but get an insight into important things like how to build a network etc. That too from the industry experienced trainers.
- Do go for internships or side projects
It is pretty obvious that recruiters would like to see practical professional experience. After starting to build your knowledge base, it would be a wise thing to apply your learnt skills in the real-time settings and receive a feedback on the same.
For this, one can go about searching for internships or part-time work through social media and job portals. Freelancing platforms such as Upwork can also be used.
It is difficult to acquire experience sans experience. However, by beginning small through leveraging online communities, one can prove that he/she has the required skills and potential.
With this, we feel that we have answered the question “how to start career in Data Science?”
Begin your journey by joining the best course for Data Science, and keep these tips in mind and go by them at the same time.
Wishing you a bright career and future!