Is your dream to become a data scientist? Do you want to know step by step method to become a Data Scientist? If yes you have come to the right place. In this article, we have discussed What a data scientist is and how to become a data scientist.
In this digital world, While we pave a path we generally have a tendency to work for theoretical knowledge more than imparting ourselves to a practical world. When the whole world is busy with fields to explore there is only one area which has an infinite perimeter to research. Can you guess what it is?
Yes, it’s the data science—The science of the world. Knowing it follows a series of questions cropping up in our grey matter those being- Whether I can be a data scientist? What is data science? What is its field of it? Is a data scientist really helpful or not in our career lives? What should be my measures in order to be a data scientist?
If you want to just get sank in the ocean of this beautiful course of technology then dive into this article and enjoy minutely each process to become a data scientist in your future days to come.
There are various online and offline modes in which knowledge about data science is shared. For online One can go for LinkedIn and Quora and also Github. For off-line one can go for books and various research papers about data science. The following article is an answer to all those questions that cropped up in our heads as a query regarding data science.
What is Data Science?
Data Science is the field of science that surveys the data using some distinguished measures, out of which a single decision and conclusion are based on the statistics and the pattern of the graph formed by verified scrutiny of the data.
Gathering the segmented and unorganised data and giving it a form of organised data which could be useful to many fields and get phenomenal with the impact of change and information technology is what data science provides us.
It is also termed as that is an interdisciplinary science which uses various algorithms, technological ways and innovative methods to research the present data and earn concrete experience and knowledge from it. According to one of the professors at Harvard University, it is one of the sexiest courses of the 21st century.
Who is a data scientist?
The person who deals with data science is called a data scientist. He baskets in various innovative methods and concepts of technical fields ranging from mathematics, information technology and business management to conclude a single technical base code or an algorithm to formulate the unorganised data in a very organised form.
The concluded results at now taken into consideration and then sent to various organisations or software companies in order to make a perfect analysis of the day-to-day data and keep it in a structured, secured and in a smart way. Taking the clay of data and modelling it into a perfect form using fields of statistics, computer science and mathematics and processing of those in the right places is what generally a data scientist does.
Data scientists are those specialists who use both technologies as well as humanities in their blending to uncover masses of data and helps in better understanding of the working of organisations and Pick up the data from the scratch and turn it into a machine learning processed code.
Is Data scientist a perfect choice for you or not?
With not much experience in coding and expertise in graphical user interface systems, one can easily build up an Algorithm and flowchart which is the ultimate creation of a data scientist.
Not much knowledge of programming is needed for you to become a data scientist. It is just that if you are very clear with the concepts of the flowchart and build algorithms at an easy scale then the data scientist is the perfect choice for you. It is generally not barred with the myth that if you have a specialisation in data science then you can become a data scientist.
Anyone can become a data scientist without always not having a specialised degree for it but having a passion and a small graduate degree with the minimum course which has given the basic idea of data science. If your work in data science with a minimum degree is an exceptional one and catches the eye of all the organisations and perimeters of business then it proves that you are in a perfect track to fetch success in this career.
In order to become a data scientist or if you have a passion for data science and want a degree then you can refer to the various online courses, and websites and get certified. Opting for regular classes online can synchronise you if you are a full-time worker.
Also if you want an off-line course you can go to various institutes and for guidance, you can refer to various books and get certified in data science.
If you are in a hurry, you can also do the short-term course, read our full article here; Short Term Courses After 12th For Job.
Step by Step guide to become a Data Scientist
If you have a passion and a will for becoming a data scientist then you will also crave a way to Data Science. Having the potential of working with data gathering from various sources and working with them with perfect analysis and strategy helps you to become a data scientist. One should not get stressed in working with data and not make the work monotonous or boring.
The mettle of learning and updating with regular data and building up innovative methods to sort the data in a new manner sits in the capability of a data scientist. in order to become a data scientist in this 21st century you need to follow a specific algorithm or a proper strategy which would lead to having success at a very short span with minimum effort in India:
Skills you need to incorporate in yourself so that data science becomes easier for you:
Being a normal person, directly getting into the higher level of data science would make the path very complex. Whenever we decide to have a career in data science and aspire to become a data scientist we need to cultivate some skills in us so that data science becomes easier as well as lucid subject for us.
Let’s dive in how to become a data scientist;
- The coding — Gearing up with programming language
Although there is no boundation in learning programming language while initially starting with data science a basic backup of programming language adds to your profile. The programming language has a diverse branches ranging from Matlab, C++, Java, python, tenser flow, Scala, SQL and many more. Learning all of them would neither add on to your profit nor help you out so much.
But learning some of them like python and Matlab would help you rearrange the data in a specific manner and in a shortcut way and also helps in various fields other than data science.
Having a good knowledge of programming language helps you to work out the algorithms with the codes which gives out exactly the desired outputs. Having experience in programming creates a difference in the data science field and helps you to bag a better position in a very short span of time. - The reference backbone and systems to work on:
After making a way through the programming language, now comes into play the systems, a data scientist should work on. The instruments used by a data scientist makes the work straightforward and lets the data scientist work with lower knowledge in coding.
Nowadays, such advanced tools have been generated that data scientist doesn’t even require the experience and learning of any language of programming in order to perform a task related to data science.
The devices designed for data science purpose helps for the gathering of data, data statistics, Strategy and structuring of data, and graphical representation through various charts and other mechanisms. Each system also has software for machine learning and giving a perfect verified conclusion in the analysis of data. - Looking in between the lines:
While working on various projects, one should master the concept as to what method one should look for specific data and what one should avoid. You should have the expertise and update yourself with the daily census of the data and work accordingly with the advanced analysis of it.
Taking all the facts and statistics of a certain data one should be able to work it out in a concrete data-based conclusion and solve it with the appropriate methods possible so that he could deliver the desired results in a very short span and in the most efficient manner. - Basic knowledge about machine learning:
If you have a basic idea about the programming languages like Python or Matlab then, you can easily be a favourite of all the organisations you are working in. So the programming languages give you the benefit of coding with data-driven methods and help you to generate each product with an update of the market.
It is in the hands of data scientist that he can launch his statistics with the means of machine learning and show the most advanced feature, benefits and advantages to the company’s client. - Skills of mathematics and computer science:
If you want to become a data scientist you should have a clear knowledge of linear algebra and calculus as they make up the algorithm. These topics actually Synchronise with computer science totally building up the programming languages on which machine learning is based. - Goggle through the garbage:
One of the biggest magic the data scientist possesses in itself is to make use of the data which is imperfect, unstructured and disorganised. Any article which does not possess any logically proven data which can show benefits to the client in future use does not actually bring any profit to the company.
So when a new company is formed and a new data scientist is hired, then he is expected to Data centralise its product. When the company runs in loss then the only method to bring in profit is to organise and structure the data and make a perfect strategy to unfold the data to the desired client in the most efficient manner. - Data imaging:
We all of course love to see cartoons. Why? Because it is something that lets us create images in our mind and take the whole plot in our brain in a very simplified manner. In the same way, comes the product of the company.
How is it for the client if he gets the chance to imagine by himself the product he is buying from the company? Of course, everyone would plump on this concept of visualising data.
The potential of the data scientist unravels in front of the client and the organisation only when he is able to make visualise the product in the minds of both parties. It is the means of efficient transmission of information and representation logically and scientifically through digital means. - Advanced communication skills:
One of the potentials that a data scientist should possess is the means of communication. You should have advanced communication skills in order to transmit the whole idea of a certain specific project in front of both the technical teams and the non-technical too.
The client, being a layman, should be able to easily absorb the data representation that the data scientist is trying to transfer to him. It is in his hand that each one is it marketing, sales, content, finance, managing, designing, the client, executives be able to be clear with the visualisation and imaging of the whole plot that he is willing to communicate through the means of data science and his communication.
For all these, he should have excellent communication skills with innovative methods and be very prompt while taking punctual decisions and strategic management in order to see success in the future business prospects. - Be a part of different projects and work with real data statistics:
Nurture regularly yourself with creative means and code programs in Some specific languages which are efficient to help you out with data science. Also, try working out with different algorithms and layer up by regular practice.
If you make your roots strong in making flowcharts and algorithms as well as have a strong base in programming then working with real data sets in off-line projects would help you fetch the conclusion in a twist. You would easily be able to recognise and draw a summary from the data in different situations with efficient methods along with the shortcut method of implementation. This would literally make you lead in the data science field.
Try growing slowly starting from an initial project that would be a bit easier and then slowly run in the track and work on the projects given to you by various companies who have hired you. - Networking:
Your initial steppingstone should be your CV or the resume that would help you to make a portfolio. It would have an account of all the mini-projects as well as the life projects that you are working on. A summary of how much experience you have gathered with the data statistics and structuring of the real-time data from scratch is what your resume a portfolio will contain.
This would let you fetch a job in an organisation who are looking for an efficient data scientist to be hired. After working in an organisation do not forget to network with your friends who are also working in the same field.
Be active on LinkedIn GitHub and other useful sites. As you socialise you are able to know about the various systems and advancements in the data science field and keep updated with the technology and trends. - Gathering a degree and getting certified:
The data science course may range from three weeks to 1 year as per the specialisation Van has applied for. Nowadays an undergraduate course regarding data science is available in both offline and online modes and there could also be a certification that would be given by a specific university that you have chosen.
If you are to go deep dive into the sea of this field then a post-graduation is necessary and as per its perimeters of it, if data science is chosen as a core it would be the most popular one in the market. After this, you can easily climb up the ladder and reach the mount of becoming a data scientist.
University of Michigan, Howard, MIT, Cambridge, University of California, and Stanford are various institutions that provide an opportunity to opt for data science in the online mode through Coursera or various other certification platforms. After getting certified you can easily get into a TCS or an MNC or a guaranteed placement at various organisations.
Advantages of becoming a data scientist
- Data scientists have a great footing towards the core process of an organisation. He deals with the most important and sensitive aspect and unfolds with the utmost capability all the tiring efforts to the client in order to make him convinced about the product launched by a company.
- Data science has a high-paying job and its greatest benefit is to sort out candidates from a bunch of resumes and bring out the aptest one in front of the job recruiters.
- Also, data science lets each organisation bring out the best and image the positive side of an article and prove it factually in front of various clients which leads to a great profit.
- It is the ultimate method to synchronise the three subjects together that is maths, computer science, and information technology and to blend them into a single logistic data to conclude a structured concrete result it. It also gives a shape to the course of action that gives the backup to face crucial situations.
Young minds generally take the wrong route, read here Career mistakes that a job seeker should avoid.
Conclusion
Having an annual salary of about eight lakhs for an experienced data scientist and four lakh as the initial payment for the freshers is a dream job for the masses. With this high average pay package, one has to compete with the daily packs of data and compile it into a formula for better use.
There are some other carriers which are multidisciplinary, for example, artificial intelligence and machine learning, which are too analogous to this course. If One does not want to go for data science then they can also opt for these courses as they are also similar to data science. This article finally a route map for all those aspiring candidates who has a love for data science and want to explore the perimeters of it.
If you still have any doubts or queries in regards to how to become a data scientist, then comment below.
