Be A Big Data Analyst – Skills, Salary & Job Roles

In an era dominated by Big Data, one cannot imagine that the skill set and expertise of traditional Data Analysts are enough to handle the complexities of Big Data. So, who then jumps in to save the day?

Essentially, Big Data Analysts are data analysts in the truest sense of the term, but they have a significant point of difference – unlike traditional Data Analysts who mostly deal with structured data, Big Data Analysts deal with Big Data that primarily consists of raw unstructured and semi-structured data.

Naturally, the job responsibilities and skill set of Big Data Analysts differs from traditional Data Analysts.

To help you understand the job profile of a Big Data Analyst, we’ve created this guide containing a detailed description of job responsibilities, skill set, salary, and the career path to becoming a Big Data Analyst.

Who Is a Big Data Analyst?

The job description of a Data Scientist and Big Data Analyst often overlap, since they both deal in Big Data. Just as Data Scientists are engaged in gathering, cleaning, processing, and analyzing Big Data to uncover meaningful insights and patterns, Big Data Analysts are responsible for understanding those insights and identifying ways to transform them into actionable business strategies.

Big Data Analysts have one fundamental aim – to help businesses realize the true potential of Big Data in positively influencing business decisions.

Big Data Analysts gather data from multiple sources and company data warehouses and analyze and interpret the data to extract such information that can be beneficial for businesses. They must visualize and report their findings by preparing comprehensive reports, graphs, charts, etc. Visual representation of the data findings helps all the stakeholders (both technical and non-technical) to understand it better. Once everyone can visualize the idea clearly, the entire IT and business team can brainstorm as to how to use the insights in improving business decisions, boosting revenues, influencing customer decisions, enhancing customer satisfaction, and much more.

Big Data Analysts are also summoned by businesses to perform competitive market analysis tasks to identify key industry trends.

To analyze and interpret data, Big Data Analysts spend a lot of time working with a host of business, Big Data, and analytics tools like Microsoft Excel, MS Office, SAS, Tableau, QlikView, Hadoop, Spark, MongoDB, Cassandra, Hive, Pig, R, Python, SQL, to name a few.

What Are the Job Responsibilities of a Big Data Analyst?

Now that you have a fair understanding of the job profile of a Big Data Analyst, let’s look at their core duties:

• To gather and accumulate data from disparate sources, clean it, organize it, process it, and analyze it to extract valuable insights and information.
• To identify new sources of data and develop methods to improve data mining, analysis, and reporting.
• To write SQL queries to extract data from the data warehouse.
• To create data definitions for new database files or alterations made to the already existing ones for analysis purposes.
• To present the findings in reports (in table, chart, or graph format) to help the management team in the decision-making process.
• To develop relational databases for sourcing and collecting data.
• To monitor the performance of data mining systems and fix issues, if any.
• To apply statistical analysis methods for consumer data research and analysis purposes.
• To keep track of the trends and correlational patterns among complex data sets.
• To perform routine analysis tasks to support day-to-day business functioning and decision making.
• To collaborate with Data Scientists to develop innovative analytical tools.
• To work in close collaboration with both the IT team and the business management team to accomplish company goals.

What Are the Skills Required to Become a Big Data Analyst?

1. Programming

A Big Data Analyst must be a master coder and should be proficient in at least two programming languages (the more, the merrier). Coding is the base for performing numerical and statistical analysis on large data sets. Some of the most commonly used programming languages in Big Data analysis are R, Python, Ruby,  C++, Java, Scala, and Julia. Start small and master one language first. Once you get the hang of it, you can easily pick up other programming languages as well.

2. Quantitative Aptitude 

To perform data analysis, you must possess a firm grasp over Statistics and Mathematics including Linear Algebra, Multivariable Calculus, Probability Distribution, Hypothesis Testing, Bayesian Analysis, Time Series and Longitudinal Analysis, among other things.

Checkout: Data Analyst Salary in India

3. Knowledge of computational frameworks

The job of a Big Data Analyst is a versatile one. Thus, you need to be comfortable in working with multiple technologies and computational frameworks including both basic tools like Excel and SQL as well as advanced frameworks like Hadoop, MapReduce, Spark, Storm, SPSS, Cognos, SAS, and MATLAB.

4. Data warehousing skills

Every Big Data Analyst must have proficiency in working with both relational and non-relational database systems such as MySQL, Oracle, DB2, NoSQL, HDFS, MongoDB, CouchDB, Cassandra, to name a few.

5. Business acumen

What good would be the findings of Big Data Analysts if they couldn’t visualize them from a business angle? To use the extracted insights to transform businesses for the better, a Big Data Analyst must possess an acute understanding of the business world. Only then can they identify potential business opportunities and use the data findings to steer the business in a better direction.

6. Communication Skills

As we mentioned earlier, Big Data Analysts must know how to effectively convey and present their findings for the ease of understanding of others. Hence, they need to possess impeccable written and verbal communication skills so that they can explain their vision to others and breakdown complex ideas into simpler terms.

7. Data Visualization 

Trends, patterns, and outliers in your data are communicated through visualizations. If you’re relatively fresh to data analysis and searching for an initiative, making visualizations is a wonderful place to start. Choose graphs that are appropriate for the narrative you’re attempting to portray. Bar and line charts portray changes over duration concisely, whereas pie charts represent part-to-whole correlations. On the other hand, histograms and bar charts depict data distribution. 

An instance of data visualization is when data analyst Han visualizes the level of expertise needed in 60 distinct activities to determine which one is the most difficult. Following are some excellent examples of data analytics projects for beginners using data visualization:

• Astronomical Visualization
• History Visualization
• Instagram Visualization

Here are a few free data visualizations tools you can use for your data analyst projects:

• Google Charts

This data visualization tool and interactive chart gallery make it simple to insert visualizations inside a portfolio using JavaScript code and HTML. A comprehensive Guides feature walks you through the design process.

• RAW Graphs

This free and open-source web application makes it simple to convert CSV files or spreadsheets into a variety of chart kinds that would be challenging to create otherwise. You can even play with example data sets provided by the program.

• Data-Driven Documents or D3

You can accomplish a lot using this JavaScript package if you have basic technical knowledge.

8. Data Mining

The procedure of converting raw data into meaningful information is known as data mining. One of the data mining assignments you can conduct to boost your data analyst portfolio is Speech Recognition.

Speech recognition software recognizes spoken words and converts them to written content. Install speech recognition programs in Python, such as SpeechRecognition, Watson-developer-cloud, or Apiai. DeepSpeech is a free and open-source engine that is based on TensorFlow by Google. You can use the application to convert speeches into texts.

Another data analytics example using data mining is Anime Recommendation System. While streaming recommendations are great, why not create one targeting a certain genre? You can make use of user preference data and develop multiple recommendation systems by categorizing related shows based on characters, reviews, and synopses. 

9. Natural Language Processing (NLP)

NLP is an area of artificial intelligence that assists computers in interpreting and manipulating natural language in the manner of audio and text. To acquire a higher senior-level role, try to include some of the following NLP projects works in your portfolio:

• Autocorrect and Autocomplete

In Python, you can generate a neural network that autocompletes phrases and identifies grammatical problems.

•  News Translation

Python can be used to create a web-based program that converts news from one particular language into another.

Salary of a Big Data Analyst

According to Glassdoor, the average salary of a Big Data Analyst is Rs. 6,54,438 in India. The salary of Big Data professionals depends on many factors including educational background, level of expertise in Big Data, years of working experience, and so on. The entry-level salaries can be anywhere between 5 – 6 LPA, the salary increases exponentially with experience and upskilling. Experienced Big Data Analyst can earn as high as 25 LPA, depending upon the company they work for.

Steps to launch a career as a Big Data Analyst

Here’s how you can launch your career as a Big Data Analyst in three simple steps:

1. Graduate with a Bachelor’s degree with STEM (science, technology, engineering, or math) background.

While the job profile of a Big Data Analyst doesn’t demand highly advanced degrees,  most companies look for candidates who’ve graduated with a Bachelor’s degree with a specialization in STEM subjects. This is the minimum selection criteria for the job, so you have to make sure you attain it. Learning STEM subjects will introduce you to the fundamentals of Data Science, including programming, statistical, and mathematical skills. As for project management and database management, you can take special classes for these.

2. Get an internship or entry-level job in data analysis.

While it is difficult to bag data analysis jobs with zero experience in the field, you must always be on the lookout for opportunities. Many institutions or companies offer internship programs in data analysis, which could be a great start to your career. Then there are also various in-house training programs in Big Data Management, Statistical Analysis, etc. Enrolling into such programs will help you gain the necessary skills required for data analysis. Another option is to look for entry-level jobs related to this field, such as that of a Statistician, or a Junior Business Analyst/Data Analyst. Needless to say, these positions will not only help further your training, but they will also act as a stepping stone to a Big Data career.

3. Get an advanced degree.

Once you obtain working experience, it is time to amp up your game. How so? By getting an advanced degree like a Master’s degree in Data Science, or Data Analytics, or Big Data Management. Having an advanced degree will enhance your resume and open up new vistas for employment in high-level data analysis positions. Naturally, your prospective salary package will also increase by a great extent if you have a Master’s degree or equivalent certification.

Preparing for a data analyst role? Sharpen your interview skills with our comprehensive list of data analyst interview questions and answers to confidently tackle any challenge thrown your way.

Job Outlook of Big Data Analysts

According to the predictions of the World Economic Forum, Data Analysts will be in high demand in companies all around the world. Furthermore, the US Bureau of Labor Statistics (BLS) maintains that employment opportunities for market research analysts, including Data Analysts, will grow by 19% between 2014 to 2024. This is hardly surprising since the pace at which data is increasing every day, companies will need to hire more and more skilled Data Science professionals to meet their business needs. All said and done, the career outlook of Big Data Analysts looks extremely promising.

Conclusion

Big Data Analysts play a crucial role in transforming raw data into valuable insights that drive business decisions. Unlike traditional Data Analysts, they work with massive volumes of structured, semi-structured, and unstructured data, requiring expertise in programming, statistical analysis, data visualization, and business acumen. Their responsibilities range from data mining and warehousing to interpreting trends and collaborating with cross-functional teams to enhance business strategies.

With salaries ranging from ₹5-6 LPA for beginners to ₹25 LPA for experienced professionals, Big Data Analytics is a lucrative career path. To succeed, aspiring analysts should build a strong foundation in STEM subjects, gain hands-on experience through internships or entry-level jobs, and pursue advanced degrees or certifications. As businesses increasingly rely on data-driven strategies, the demand for skilled Big Data Analysts continues to grow, making it an excellent career choice for those passionate about data, analytics, and technology.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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References:

https://www.morningfuture.com/en/article/2018/02/21/data-analyst-data-scientist-big-data-work/235/