Last Updated on December 11, 2019
Today one of the most cutting-edge, emerging, and trending technologies is big data. This is one of the biggest reasons why young aspirants are diving into this field more. The plethora of interesting tools and technologies by which big data is surrounded is huge, which is generating buzz among both the candidates and the industries booming for it.
With the increase in data, organizations have started adopting these technologies to gain better visions from the data. Big Data analysts assist organizations to fulfill their requirements by curating valuable understandings from raw data.
The role of a Big Data Analyst is not just limited to analysis of raw data, but it varies from a data engineer’s involvement in ETL – extract, transform and load operations, working with visualization and reporting tools to machine learning (ML) algorithms. The job responsibilities of that of big data analysts and data scientists quite often intersect unless a data scientist is a statistical or mathematical conformist.
Who is a Big Data Analyst?
A person who procures information about specific topics and then gathers, examines, and presents findings in comprehensive reports. A big data analyst takes the data and consumes it to help companies make better business decisions. Big Data analysts are being hired by different types of businesses to help them gather and analyze data.
As experts, big data analysts are frequently called on to use their knowledge, skill-sets, and tools to provide competitive analysis and identify trends in the industry.
A big data analyst is like a data scientist, but are generally not responsible for making the algorithms used for acquisition and data discovery. Rather than preparing their own data projects, big data analysts frequently find themselves attempting specific business tasks using existing systems, tools, and data sets.
Both careers (big data analysts and data scientists) share a common goal: to determine how to use the information to answer questions and solve glitches for the benefit of their industry/business.
Responsibilities of a Big Data Analyst:
As per the level of expertise a big data analyst might:
- Work with management and/or data experts, IT teams to define organizational goals
- Data Mining from primary and secondary sources
- Clean and crop data to remove unwanted information
- Examine and infer results using standard statistical techniques and tools
- Pinpoint correlations, trends, and patterns in complex data-sets
- Identify new prospects for process enhancement
- Provide clear data visualizations and concise data reports to the management
- Design, generate and preserve relational data systems and databases
- Triage data-related matters and code problems
Now let’s look at the following must-have skills to become a skilled Big Data Analyst. You can either gain these skills yourself or can opt for a Big Data Hadoop online certification training course, which can ensure that you can gain all the skills required for a Big Data analyst. So, here are the skills that you should know:
Learning algorithms and data structures is the most crucial part of the Big Data analyst’s arsenal. You need to code to conduct statistical and numerical analysis with massive data sets. You must be able to write, comprehend, and rectify errors in the code.
To be an expert Big Data analyst, you must know and understand the statistical languages like Python, Java, R and C++ among other programming languages such as Scala, SQL, HIVE, etc. which will be benefiting you in your career journey. The more you know, the better it as. Always remember that you are not supposed to learn every single language out there.
Quantitative & Analytical Skills
For Big Data analytics, quantitative and analytical play a major role. Knowledge of statistics and mathematics guides and supports you while curating a major chunk of unstructured data. For beginners, you need to know multivariable calculus, probability, and linear and matrix algebra.
Quantitative data analysis methods such as inferential statistics and descriptive statistics help the data analysts to summarize the data, find patterns, generalize outcomes, make predictions, etc.
Visualization is the easiest way to understand any concept. Organizations can view the analytics of the data when presented visually by big data analysts who work on a large number of unstructured and structured data.
In the present era, there are numerous data visualization tools such as Data Wrapper, Tableau, Plotly, etc. This is the easiest way to present the results of a large dataset in a simple yet understandable manner.
Data Handling & Interpreting
In any organization, the data is increasing day-by-day and handling them to generate great insights requires a skilled big data analyst. Handling and interpreting data is not an easy job.
The analysts, with the help of data interpretation, can review the data to arrive at the conclusion. Data handling and making sense out of the same takes a lot of time and is a crucial part of any project.
Know-how of Multiple Technologies and Frameworks
Programming is an essential big data analysis skills. But, technologies are not restricted to programming alone. Learning multi-programming languages will help you to grow as a Big Data analyst.
Frameworks like Apache Spark, Apache Hadoop, etc. helps in streaming Big Data to a larger extent. Components of Apache Hadoop such as MapReduce, HDFS, HIVE, Pig are in great demand these days. For better decision making, being familiar with these technologies and frameworks plays a crucial role in big data analysts.
Understanding Business & Problem Solving Skills
Along with technical and statistical knowledge, a good big data analyst should possess knowledge of the business process. He must be able to learn the business goals and business aspects set by an organization. Domain expertise of big data analysts helps them to connect with company stakeholders efficiently.
Becoming a big data analyst needs mastery of your skill-sets. There is a well-known saying in English “Practice makes a man perfect”, the more you practice and work hard on the above-mentioned skills, the more you will gain understanding in this domain.
IT professionals have a benefit in learning new technologies and programming languages. Others will require putting in more struggles to learn computing technologies and skills. Yet softer skills such as domain expertise and business experience would work as the basic force for the playing ground.