Every piece of information that we give out today is converted to data in some or the other way. From the chat you do on your mobile to the feedback you give in a restaurant, everything is data. Today, the technological revolution has transformed the way businesses work, everything has become digitized which in turn generates a massive amount of data every second of the day. Data can be in any form and to handle such data, companies hire professionals called data analysts. If you are also dreaming of becoming a data analyst and do not know where to start, then this article is for you. Let us understand each and every step in detail.
What is Data Analytics?
In general Data Analytics mean making sense of a bunch of data or changing data to information. Thus, Data Analytics by definition means drawing meaningful insights from raw data by scrutinizing it. These insights can be anything from your survey data to your innovation data, research data, market trends, etc. Data analytics is the analysis of data from several sources and then transforming it into information that can easily be interpreted by humans.
Also Read: Benefits Of MBA With A Data Analytics Specialization
Who is a Data Analyst?
Data Analysts are responsible for gathering, processing, and then analyzing the data. The data can be structured, semi-structured, or unstructured depending on how it was collected in the raw form. This data when analyzed can be presented in the form of graphs or charts in order to show precise results of the analytics performed. This data can be from different backgrounds like customer feedback, market trend, account, logistics, etc. After analyzing the data, data analysts come up with strategies or different measures to improve the quality of the service provided by the respective organization which in turn will improve the quality of the data. They are expected to effectively communicate their findings to the decision-maker.
Skills required to Become a Data Analyst
Data analysts have a strong base in Mathematics and Computer Science. The additional knowledge of the respective field in which the data is to be handled is always a plus. For example, for handling biological genomic data, a slight knowledge of biology and genomics is a good idea. In order to fulfill the responsibilities of a data analyst, data analysts need to possess and fulfill a criterion of a vast and rich skillset. These are as follows: –
Programming and Mathematics
Having a good knowledge of programming and a grip on coding is the basic skill set required to become a data analyst. The most common programming languages used today are Java Script, R, and Python. Data analysis also requires maths and especially statistics. R is the most powerful scripting language for huge datasets.
Knowledge of Data Processing Tools and Platforms
Data Analysts should be experienced in using huge databases, data processing, and analysis tools. One should be proficient in writing SQL queries and procedures. One of the most used data processing platforms is Hadoop and Apache Spark. After processing the data, it is analyzed by different tools such as Datapine, Looker, Rapidminer, etc.
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Communication
Having knowledge of the subject and its skills is important but are incomplete without soft skills. These are a part of the personality and which play a major role in the working environment, especially as a team. Communication is essential for data analysts as they have to communicate the analyzed data to the decision-making people. In order to give advice for the improvement of the services, communication is a must.
Creativity and Critical Thinking
In order to come up with accurate evaluations, data analysts have to think out of the box. They are expected to take a systematic and logical approach to problem-solving. These skills are like the cherry on the cake of the data analyst skillset.
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Qualifications to become a Data Analyst
Undergraduate Courses
There are multiple undergraduate courses to start with such as:
- Computer Science
- Data Analysis
- Information Systems
- Statistics
Post Graduate Courses
The postgraduate courses can provide you with one year or six months of hands-on exposure to the corporate world in order to get familiar with the actual working environment. Some of these common courses are:
- Master in Computer Science
- Masters in Data Analytics
- Master in Data Science
- Masters in Business Analytics
There is a common misconception that to become a Data Analyst, one must have a degree in Computer Science or Mathematics. But this is clearly not the case. If your communication skills are strong and you can convey your finding and ideas to the decision-makers efficiently then this field is for you. Many non-technical people become excellent Data Analysts just by communicating well. Though the degree is not a compulsion to enter this stream, a certification in the respective subjects from an accredited institute is imperative.
Also Read: What Is Data Science & Analytics, Job And Future Scope
Job Profiles and Salaries of Data Analysts
Data Analysts can be of different types depending on the type of data being handled by them. Some of them are Financial Analyst, Healthcare Analyst, Social Media Data Analyst, and Insurance Underwriting Analyst.
The average salary of a Data Analyst in India can be anywhere between 2-12 LPA.
Data is the new currency!! We have heard of this before and it is true in all senses. Today data is everywhere but in raw form. Companies hire Data Analysts who can help them in converting raw data to meaningful information which helps in the growth of the company. Chitkara University offers a postgraduate Master of Business Administration course in Business Analytics. They have mindfully curated the curriculum to give its students a mix of theoretical and practical hands-on training experience. It gives its students a strong understanding of the fundamentals of analytics, data science, technical skills, analysis methods, and problem-solving approaches. The common recruiters at Chitkara University are Deloitte, ITC, Shopclues, WNS, SG Analytics, Evalueserve, EY, AGSI, etc.
