A data scientist is undoubtedly the hottest job of the 21st century. An immeasurable amount of data is generating each and every second through our mobile phones, laptops, and many other devices all over the world.
You must be wondering how to become a Data scientist? What skills are required to become a Data Scientist? I know so many questions will be there in your mind. With this article, you will find all the answers.
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What is a data scientist?
Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, scepticism of existing assumptions to uncover solutions to business challenges.
A data scientist’s work typically involves making decision and prediction from messy, unstructured data, from sources such as smart devices, social media feeds, and emails that don’t neatly fit into a database.
Skills Required to Become a Data Scientist
The most obvious task of a Data Scientist is to collect, analyze, and evaluate the data for obtaining meaningful insights from it. For this purpose, you need to have your hands-on Statistics. You should at least have a knowledge of the basic concepts such as linear regression, probability, etc. Also, you will have to select the right statistical approach based on your data to extract some meaningful inferences from it. You can also use different tools for this such as SPSS, SAS, etc.
You need to have the knowledge of programming languages like Python, R, SQL, Java, Julia, Scala and MATLAB with Python being the most common coding language required in data science roles. Programming languages help you clean, massage, and organize an unstructured set of data.
3. Machine Learning
If you’re at a large company with huge amounts of data or working at a company where the product itself is especially data-driven (e.g. Netflix, Google Maps, Uber), it may be the case that you’ll want to be familiar with machine learning methods. This can mean things like k-nearest neighbours, random forests, ensemble methods, and more. It’s true that a lot of these techniques can be implemented using R or Python libraries because of this, it’s not necessary to become an expert on how the algorithms work. More important is to understand the broad strokes and really understand when it is appropriate to use different techniques.
4. Data Visualization
It is always better to portray things visually; the real value is well-established and understood. When I create a visualization, I am sure to get meaningful information, which can be surprising out it holds power to influence the system. Histograms, Bar charts, Pie charts, Scatter plots, Line plots, Time series, Relationship maps, Heat maps, Geo Maps, 3-D Plots, and a long list of visualizations you can use for your data.
5. Database Management
You know 80% of the work goes into preparing the data for processing in an industry setting. With heaps and large chunks of data to work on, it is quintessential that a data scientist knows how to manage that data. Database Management quintessentially consists of a group of programs that can edit, index, and manipulate the database. The DBMS accepts a request made for data from an application and instructs the OS to provide specific required data. In large systems, a DBMS helps users to store and retrieve data at any given point of time.
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How to Become A Data Scientist: A Career Guide
There are five basic steps that everyone should follow for becoming a Data Scientist.
1. Earn a Bachelor’s Degree
For building a career in any domain, the mandatory requirement is a College Education. You should at least have a four-year bachelor’s degree. Many universities are offering several Data Science courses. You can get some other degree programs such as computer science, Mathematics, Statistics etc which are related to the skills required for doing Data Science. The college education will help you to learn many new things that will help you further in becoming a Data Scientist.
2. Choose a programming language and Earn Certificates
Python and R Programming are the most popular programming languages for data science. On one hand, R has been the leading language for statistics and data analysis for the past two decades. On the other hand, Python has rapidly become one of the most popular and fastest-growing programming languages in the last five years. You can improve your skills and boost your confidence by earning various certificates. These certificates will enrich your skills and increase your value in the market.
3. Make a few projects
This is the most important part. Nothing showcases your skill and knowledge, like a project made by you. Don’t wait till you know enough for making a project. Just get started with whatever you know till now. This will show your knowledge till now, improve your understanding of the concepts and also give you the confidence and motivation to keep going. Keep making projects that suit your current level. This way you can show your skill with basic concepts as well as the advanced ones. It will also show your progress.
4. Take part in open-source projects
There are many open-source projects out there that are constantly looking for good contributors. You can even find projects that are suitable for beginners and move up as you gain confidence. This not only improves and showcases your skills. It also helps in making contacts and connections. Other contributors and maybe even the project leaders and owners might help you in getting your first job.
5. Never Stop Learning
In any profession, you can never stop learning because you need to keep yourself updated with the recent advances and changing dynamics of your job. Continuous learning and hard work will make you stand in this highly demanded job. Also, Data Science is an evolving field and the best solution to a problem can become just a good one anytime. You need to continuously discover innovative ideas to prove your worth. This will also help you to boost your confidence and become a better Data Scientist.
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