Data analytics is a method of analyzing data to make data-driven decisions that help companies to get a conclusion about the information. Data analytics techniques can reveal metrics and trends that are available in tons of information. This information is used to optimize processes to enhance the efficiency and performance of organizations. 

A data analyst acquires information about a particular topic and then analyzes, interprets, and provides findings in reports. Many organizations make use of a data analyst who helps to collect and analyze the data. In simple words, a data analyst is a person who converts data into information and information into insights. These insights are often used in many ways to make advanced marketing strategies and enhance the production process. So, a data analyst plays a crucial role in an organization.  

In this article, we will learn the following topics:

Types of Data analytics

Steps to become a data analyst

What is the job description for a data analyst?

What does a data analyst do?

What are the skills required for a data analyst?

What are the tools used by data analysts?

Salary package for a data analyst

Conclusion

Types of Data analytics

Data analytics are classified into four basic types. They are as follows:

  • Descriptive analytics

  • Diagnostic analytics

  • Predictive analytics

  • Prescriptive analytics

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The Gartner Analytics Ascending Model represents the relation between these types of analytics in terms of difficulty and value.  

Steps to Become a Data Analyst

Data analysts are often hired by companies to make decisions or improve business practices. The steps to becoming a data analysts are as follows:

Graduation in Information technology, Computer Science, or Statistics

Complete graduation in the fields of Computer Science, Information technology, or Statistics that emphasize analytical and statistical skills. Find an advisor or career counsellor who is familiar with a data analyst career path.

Data analyst certification

Take a complete Data analyst course to grab the data analytics skills that help to work on the data analysis, integration, and provide data-driven decisions. The productive course offers the best data analytics skills along with real-time projects that give the experience to work as a data analyst. Along with the projects, internships will help to gain skills to be a good data analyst.

Grab an opportunity for an entry-level job

After adopting all the basic skills required for a data analyst from the course, try to grab an opportunity as an entry-level job. Yes, it is difficult to gain an opportunity with no experience. But try as many as in-house classes and practice interview questions that build confidence to face the interview. These entry-level jobs will add value to training and experience. Knowledge, experience, and perseverance will help to reach the level of desire.

Master degree in Data analyst

By holding a master degree in Data analyst, it offers more opportunities and opens a way to advance in the career. The choice of a master degree in analyst helps you to explore new software skills with the help of experts in fields. Many universities collaborate with organizations to build team assignments, projects and internships that help to gain real-world experience. Of course, these days, organizations are willing to take a person who is updated with new technology and skills to work on data analysis. 

What is the job description for data analysts?

A data analyst interprets data into information that offers data-driven decisions to improve business. A data analyst job description includes the following, but not limited to this list:

  • Collecting and interpreting the data

  • Analysing the data

  • Reporting the results

  • Identifying trends and patterns in data sets

  • Working in teams to establish business requirements

  • Defining a new data collection and analysis processes.

  • Report data in a comprehensive and repeatable way

What does a data analyst do?

A data analyst does collects, processes, and performs statistical analyses on large data sets. They help to discover how data is used to solve the problems and used to answer the questions. The data analysis has evolved with the advancement of technology and ever-increasing data storage and data analysis techniques. Data management is the key aspects of a data analyst’s job, and it includes designing and implementing databases that allow easy access to the results of data mining 

Statistical analysis is the heart of data analytics. With the use of statistical analysis, the insights are created from data. Both statistical and machine learning techniques are used to analyze data. The enhancement of the relational database enables a new way of data analysis that analysts made to use SQL to retrieve data from databases.

What are the skills required for a data analyst?

Data analysts evaluate the information using several data analysis tools. The output given by a data analyst is used to make data-driven decisions. A data analyst has to possess the following skills:

Programming skills

The programming skills are the necessary tool for a data analyst. The programming languages that are used to manipulate data are R, Python, Java, MatLab, C++, PHP, and many more. Most commonly R and Python languages are used by the data analyst. R is designed for data analytics. Python is used to perform statistical analysis. 

Mathematical ability

Good knowledge of statistics and formulae is required for analyzing data to provide real-world reports. A data analyst should be able to solve business problems such as depreciation, compound interest, statistical measures, and many more. Should have good knowledge of linear algebra and calculus is highly helpful for a data analyst.

Microsoft excel

Microsoft Excel is highly useful in organizing data and calculating numbers among tasks of data analysis. A data analyst should possess advanced Microsoft excel and be able to understand the advanced modeling and analytics techniques. 

Communication skills

A data analyst should have strong and effective communication skills that help to grab the attention of the audience to make a clear understanding of the data output. Simply, strong communication skill is the key to success.

SQL databases

For a data analyst, SQL is a primary tool for data management that includes Data Extraction, Transformation, and Loading (ETL). A data analyst is required to run the queries to find relevant trends in the current data and for data processing. SQL databases are relational databases with structured data. It stores data in tables and makes it easy to pull the data to perform analysis. 

Creative and Analytical thinking

For a data analyst, it is required to have a strong grounding in statistical methods, but even more critical to think to solve problems with creative and analytical thinking. It enables us to generate more research questions that enhance the company’s efficiency to tackle business problems easily.

What are the tools used by data analysts?

There are many tools for data analysts to perform data mining, analysis, and reporting. Some of the tools are as follows:

  • R Programming

  • Tableau public

  • Google analytics

  • GitHub

  • AWS S3

  • Jupyter Notebook system

  • Python

  • SAS

  • Apache spark

  • Microsoft Excel

  • RapidMiner

  • KNIME

  • QlikView

  • Splunk and many more

Salary package for a data analyst

Salary package for a data analyst depends on the job roles and responsibilities. Although, there is a huge demand for a data analyst so the salary package may vary depending on the work experience and performance. 

The average salary for entry-level data analyst: 5245.79$

The average salary for data analyst: 8414.11$

The average salary for senior data analyst: 11289.58$

Conclusion

According to recent data from the Bureau of Labor Statistics, the market research analyst positions are expected to grow by 20%, and management analyst positions are expected to increase by 14%. The demand for Data Analysts has been rising these days. As we know, data analysts play a key role to make data-driven decisions for organizations.