CareersData Analytics with R, Excel & Tableau

A 5-Step Approach On How To Become A Data Analyst!

As more and more professionals today keep searching for means and ways to become a data analyst, it’s only natural to think “How To Become A Data Analyst?”. It’s hard to ignore the recommendations by Harvard Business Review, LinkedIn, Gartner claiming that a career in data analysis is the best move indeed to stay relevant in future.

How To Become A Data Analyst? Here’s A 5-Step Approach!

1. Don’t Settle With That Bachelor’s, Pursue A Higher Education

A minority of hiring managers prefer candidates with a bachelor’s degree in engineering and technical fields. However, having just a basic graduation degree will open up usually entry-level jobs unless or until you have several years of experience to supplement.

To scale up the career ladder, a master’s degree in computer science, IT, statistics, economics, and data analytics can greatly aid in career enhancement.

2. Add More Skills To Your CV

Despite the availability of plenty of job opportunities for data analysts, there is a huge dearth of skilled professionals in the field. Whenever the resume of a potential candidate flaunts plenty of upskilling journeys, the chances that he/she will get hired are quite high. Simply because it’s a clear indication of the candidate’s motivation and passion. Which organization wouldn’t want to have a self-motivated and passionate professional in their workforce? Still not convinced, here are 5 reasons why you should definitely upskill yourself!

You could enroll for Acadgild’s Data Analytics Certification Course or Analytics for Non-Programmers as it trains you in necessary skills. To be eligible for the best roles in data analysis domain, you need to be trained in statistics, R programming language, data warehousing, SQL databases, XML, Javascript, data mining and visualization. Here’s a blog post on some of the free online courses you can enroll to add more skills to your CV.

3. Foster Your Soft Skills

Your hard skills can be quantified but that’s not the case with your soft skills. These include the skills that come handy while interacting with people (colleagues, clients, and higher-ups) in your professional life. Having excellent soft skills will set you apart from an average data scientist. Moreover, they come in handy during unforeseen circumstances at work. The must-have soft skills that are essential for any successful data analyst or professional include body language, communication, social skills, negotiation skills, and conflict, stress and time management.

4. Do Internships Before Your First Data Analyst Job

Internships are an excellent platform to learn all about data analytics through real-time projects and from experts. Moreover, you even get the opportunity to network as well as train under experienced data analysts. Your CV gets an added weight when you have at least a couple of internships to prove your love for data!

5. Stay Updated About The Latest Trends In Data Analysis

In a dynamic field like data analysis, there’s some innovation or the other happening every minute. Although it’s hard to keep track of all the latest developments in the field, you should be aware of the most important ones at least. This will enhance both your knowledge as well as help you stay abreast of what’s happening in the industry.

I hope you got a clear idea on the important query, ‘how to become a data analyst’. The 5 steps above will help any data analytics aspirant to look forward to fulfilling their dream career of becoming a data analyst. Before you leave, check out the career prospects for data analysts in India. 

Happy Learning!



A Lifelong Learner.

One Comment

  1. Data science is a blend of various tools, algorithms, and machine learning with the goal to discover hidden pattern from the raw data and primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics, and machine learning. It deals with structured and unstructured data focusing mainly on the present and future scenario by using tools such as R, Rapidminer, BigML, Weka.
    You may be confusing it with the business intelligence (BI), but the main difference lies in the type of data as it only deals with the structured data and focuses on past and present time period. Also, there is a difference in data scientists and data analytics. Data analysts analysis the past recorded data and then tell the ongoing trends whereas the data scientists tell the ongoing trends as well as predict what could be the future trends and tell all the known and unknown events behind the trends.
    Data science is an ongoing process of the vicious cycle which includes the following stages or stages:
    1-discovery: understand and identify various sources, specifications, and other details and run a basic hypothesis test
    2-Data preparation: explore the data, and preprocess using R and doing the ETLT
    3-model planning: determining and identifying the relationship between the explanatory and unexplained variables.
    4-Model building: testing the data and seeing whether it is good to go on or it needs some changes or modification
    5-Operationalize: giving final results or reports giving a clear picture of the trends
    6-communicate results: analyzing whether the desired and achieved results are same or not.
    Now as a career option, if you look into the rising data studies you will find that how data science is and will be the ON DEMAND job and going on the statistics the average salary for the data scientist is approximately Rs 6lakhs and according to the O’Reilly 2018, data science survey average $2000 &$2500.

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