Data science is the leading career option for the tech enthusiasts in 21st century. With data that companies have to deal with, rising at an alarming rate, demand for certified professionals in big data is experiencing an aggressive hit. From businesses to non-profit organizations to government institutions, the demand for data science professionals is sure to rise as there’ll be 11.5 million jobs in data science and analytics by 2026, according to the US bureau of Labor Statistics report.

Data scientists’ role demands computer expertise with their eyes set on the recurring trends and forecasts with a mathematical bend of mind. Data science industry is a technical domain that promises to grow in rapid adoption of cloud-based solutions and humungous application of data science platform in various industries.

Here are the 5 easy steps to becoming a data scientist:

  1. Begin with the basics of data science

It’s always a great idea to begin with the basics as learning data science fundamentals is the key to prepare you for the learning challenges. Before diving into the field, it’s advised to gauge what you really want out of this industry. Getting started by learning the basics of statistics & data science by watching beginner-friendly video tutorials can prove to be advantageous. These will help you understand various components of data science, data ingestion, data cleaning, data analysing, modelling & applications.

  1. Knowledge of programming languages

Mastery in Python, R, SQL, Julia is a must as you go deeper into the process. These and many other programming languages help you research, do data visualization, designing state-of-the-art applications & clearing technical interviews as well.

  1. Applied data science track

Data science track includes all critical courses to master like data management, programming, exploratory analysis, statistical experimentation, model development & reporting. It provides you a structure and interactive coding exercises that help you in understanding core concepts of statistics, ML & NLP.

  1. Gain hands-on experience on data science projects

By participating in data science competitions, volunteering in NGO projects or applying for internships as these will help you build your data science portfolio. Join Kaggle competitions to scale and gain knowledge. Do not restrict yourself to jobs as you can gain experience by contributing to open-source projects. Finding apprenticeships, summer camp invites or internships on LinkedIn or local job boards will help you gain necessary experience in the field.

  1. Earn a credible data science certification

Equip yourself with the best data science certifications in the industry from – USDSI, Google, IBM amid others, that can train you for the meatier data science roles ahead. Possessing a beginner’s level experience in data science projects is always a great idea before taking the certification tests. These tests assess your knowledge on programming, data management, modelling and reporting. Some popular data science certifications are USDSI Certified data science professional program, Data camp certification, IBM data science professional certificate, etc. to gain knowledge from.

With adequate skills-training and a strong portfolio, you can begin working on establishing your public profile as a data scientist. A well-executed project that you pull-off on your own is a great way to build your profile. Publishing your work on GitHub to present your process and findings, in order to highlight your technical skills and creativity in a compelling narrative is also a preferred channel to grow.

As your journey begins in the field of data sciences, the next step is to find yourself a full-time role in any sub-field of data science and while you wait to get hired, it’s better to keep learning new tools and work on projects & gain experience. You may also join data science communities or write blogs about things you have learned. A huge data science career awaits you- Go grab it!

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