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Build A Dream Career in Data Analytics in 5 Easy Steps

Data Science Career

Data is the new oil, and everyone wants to leverage the benefits of data. It aids companies in better consumer awareness, effective advertising and facilitates enhanced customer experience. And to do this, a Data Analyst is the go-to person. As a result, a career in Big Data Analytics is in great demand today. Data science is booming, and so are career opportunities in Data Analytics!

But how to start a career in Data Analytics? 

How do I become a Data Analyst with no experience?

Can a beginner learn Data Analytics?

We all know data science is a high-paying career. But when we aspire to become a Data Analyst or a Data Scientist, these questions hit our minds. So, just put all your worries to rest, and read this blog to discover tips and tricks to start a career in big data.

How to Start a Career in Big Data Analytics?

1. Complete your basic education

You need an undergraduate or postgraduate degree in a related field, such as computer science, marketing, business management, economics, life sciences, statistics, etc., to work as a data analyst. However, the optimal place to start is with education in STEM (Science, Technology, Engineering, and Mathematics) disciplines to develop the fundamental competencies required for this position. But certainly, anyone from any domain may learn data science because it is not limited to any particular field. As a result, you should finish your primary education to have a solid understanding of that industry before making plans to enrol in a Master’s in Data Science programme.

2. Learn programming

As a Data Analyst, you must have a fine understanding of programming languages. Furthermore, to become a Data Scientist, you must be highly proficient in coding. So, start with the basics of programming language. If you aren’t a computer science or STEM student, initially, coding might appear a daunting task. But once you get the hang of it, it will become easier. Here we bring you a list of programming languages you need to learn-

  • Python
  • SQL, No SQL, My SQL
  • R
  • Java, JavaScript
  • Julia
  • C/C++

3. Enrol in a Data Analytics Course

This is one of the essential steps in your data science journey. People often think upskilling or data science courses are a waste of money. No. It isn’t. Invest in some good courses, and learn the fundamentals of the domain thoroughly. A good Data Analytics course will not only give you a certificate, but also teach you the data concepts from scratch. You can start with some good free data science courses, and once you get a fair picture of the domain, what are the things you need to learn and what your interests are; choose a course accordingly. 

What are the important skillsets you must consider while learning Data Analytics?

  • Data Visualisation
  • Data Modelling
  • Data processing
  • Data Mining
  • Machine Learning
  • Predictive Analysis

Top Data Analytics Course:

4. Take up projects or internships

Projects and internships can help you learn the subject matter practically. Look for courses that provide you with internship opportunities with some good reputable companies. Some courses have projects or assignments where you get to work on certain topics related to the course. Here, you will put all of your academic knowledge into practice. Moreover, you can enhance your resume with these internship certifications. In addition, projects provide you the opportunity to speak more confidently about the technical aspects of the field, so your lack of professional experience won’t be an issue while applying for jobs.

5. Start freelancing and build a portfolio in Data Science

Freelancing opens doors for you in multiple ways. Here you can actually start a career in data analytics. But how? Well, you get to work on different data analytics projects for numerous clients across diverse domains. This, in turn, builds a strong portfolio for you. So, you aren’t a fresher anymore! Work with some reputed clients, and take up different projects that will highlight diverse skillsets within you. In fact, with the rising demand for the gig economy, freelancing is one of the most flexible and lucrative option one can consider today. 

Freelancing platforms for data analysts—

Quick tips to Land Your First Data Analyst Job!

  • Job hunting takes time, so patience is the key. Regularly apply for jobs, and start with entry-level data analyst jobs. You can consider top job portals such as Naukri.com, Indeed, PlacementIndia and Hirist.com.
  • Don’t just rely on job portals. Study the market and research top companies hiring data analysts. Look for start-ups as well because in the initial years, the more you work with start-ups, the more exposure you get to versatility in skillsets.
  • Don’t ignore networking. Leverage the best of LinkedIn and network with industry leaders. Follow top pages or leading companies of the domain.
  • Be an active member of data science groups and forums. Some popular communities for data science include Kaggle, Dataquest, and IBM Data Community.
  • Tailor your resume nicely. Mention all the skillsets, incorporate keywords, add certification, highlight your projects and internships, and speak about your portfolio.

Learning data science might seem intimidating for a beginner, especially if you don’t belong to the STEM field. But this fascinating field is something anyone from any domain can excel, provided you have the determination to learn. So, with the right data science course, show some dedication and start your career journey!

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Romina Gopalan is a top-notch upskilling advisor & content writer. Her areas of expertise include Digital Marketing, Data Sciences, IoT, RPA, and UX/UI writing. Her sharp research and writing skills allow her to identify futuristic opportunities. Thus, helping you understand how you can leverage expertise in any domain.

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