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Data Analyst Job Description ( A Complete Guide with Best Practices )

Last updated on 27th Oct 2022, Artciles, Blog

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Jamie (Senior Data Analyst )

Jamie provides in-depth presentations on various big data technologies and the role of the data analyst. She specializes in Docker, Hadoop, Microservices, Commvault, BI tools, SQL/SQL server/SSIS, DB2, Control-M, Oracle Workflow, Autosys, DB2, MSSQL Server, Oracle, and Microsoft Office, including Excel, Visio, and PowerPoint.

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    • In this article you will get
    • 1.Introduction
    • 2.What Is Data Analysis?
    • 3.What exactly does an analyst of data do?
    • 4.Various Categories of Data Analysts
    • 5.What is meant by the term “Data Processing Cycle”?
    • 6. What is data, What are the different types of data, and How should data be analyzed?
    • 7.Skills needed for data analyst required
    • 8.Competencies essential for a data analyst
    • 9.What kind of qualifications are needed to be a data analyst?
    • 10.How much does a data analyst make?
    • 11.Top Companies Hiring Data Analysts


Over the past decade, data has fundamentally altered the appearance of our world. The innumerable emails and text messages we send and receive, as well as the YouTube videos we watch, are all examples of the almost 2.5 quintillion bytes worth of data that are produced every day throughout the globe. There are enormous amounts of data that need to be processed by businesses of all sizes, and a lot rides on their capacity to extract useful information from this data. That is exactly what a data analyst is responsible for. They provide an interpretation of the statistical data and transform it into knowledge that can be utilized by companies and organizations for the purpose of making crucial decisions.

Organizations in every industry are becoming more reliant on data in order to make important business decisions such as which products to manufacture, which markets to enter, what investments to make, or which customers to target. They are also making use of the data to identify weak areas of the business that require improvement in order to move forward.

Because of this, data analysis has become one of the vocations that is in the highest demand across the globe, and data analysts are highly sought after by the most successful companies in the world. The income and benefits offered to data analysts are merely a reflection of the demand for this particular job type, which is expected to continue expanding by leaps and bounds in the foreseeable future.

You would be acting irresponsibly if you did not take advantage of this situation if you already had the skills necessary to become a data analyst. In this article on the “data analyst job description,” let’s take a look at all of the features that are relevant to this job function, and then explore a data analyst job description in detail at the end of the article.

What Is Data Analysis?

Data analysis is the process of cleaning, analysing, interpreting, and displaying data by utilising a variety of methodologies and technologies for business intelligence. This process can take place in a number of different formats. The use of data analysis tools can help you uncover critical insights, which can then lead to more informed decision-making that is more likely to be effective. The process of transforming raw data into relevant statistics, information, and explanations is the focus of this area of study.

What exactly does an analyst of data do?

A data analyst examines different kinds of data to see how various issues concerning a company’s clients might be resolved. An additional responsibility of a data analyst is to convey this information to management and any other relevant stakeholders. These individuals find work in a wide variety of fields, including government, research, medicine, business, and the criminal justice system, to name a few.

An individual who possesses the knowledge and abilities necessary to transform raw data into information and insight that can be utilized in the process of making business decisions is someone who fills the function of a data analyst.

Various Categories of Data Analysts

Analyst of Medical and Health Care Systems:

Medical and healthcare data analysts, as the name suggests, utilize data obtained from a variety of sources in order to assist in the improvement of healthcare outcomes. Typically, they focus on the commercial aspects of medicine, such as improving patient care or streamlining operations.

Role of Analyst in Medical field

Analyst of Market Research Position:

Market research analysts are responsible for gathering and analyzing data pertaining to customers and competitors. In order to forecast how well a product or service will sell in the future, market research experts study the current state of the market. They provide assistance to firms in establishing what kinds of products people want, who will buy those products, and how much they will pay for those products.

Analyst for Businesses:

Business analysts make use of the data they collect to generate business insights and advocate for improvements inside corporations and other types of organizations. Business analysts are trained to identify issues in virtually every facet of a company, including information technology (IT) procedures, organizational structures, and employee development. Business analytics is progressively becoming a vital component of firms’ operations as a result of the ongoing efforts that businesses make to boost their overall efficiency and reduce their operating costs.

Analyst in the Business Intelligence Department:

A business intelligence analyst, often known as a BI analyst, examines data and other types of information with the goal of assisting companies in making decisions that are beneficial to their operations. They might collect, clean, and analyze data such as a company’s revenue, sales, market intelligence, or consumer engagement indicators, among other types of information. There is also the possibility that BI analysts will be required to develop tools and data models in order to assist with the visualization or monitoring of data.

An Analyst of Operations Research:

Operations research analysts are high-level problem solvers who apply complex problem-solving methodologies such as optimization, data mining, statistical analysis, and mathematical modelling to create solutions that help corporations and organizations perform more efficiently and cost-effectively.

Analyst of Intelligence (Intelligence Analyst):

Intelligence analysts evaluate information and data in order to detect potential security risks and develop solutions to address those risks. There are many different types of information sources, some examples of which are databases, field reports, internal and external statistics, and statistic compilations. In order to successfully produce action plans and synthesize information, analysts need to have strong research, comprehension, and analytical ability.

The roles and responsibilities of a data analyst are described in their job description

A data analyst’s job include categorizing many kinds of data, such as those pertaining to sales figures, market research, logistics, linguistics, or other behavioural patterns. They make use of their extensive technological knowledge to guarantee that the data is of the highest possible standard and accuracy. Following this step, the data is evaluated, formatted, and presented in a format that provides individuals, businesses, and organizations with the assistance necessary to make more informed decisions.

  • The collection of information from primary and secondary sources through the utilisation of automated tools.
  • Deleting damaged data and correcting any coding errors and other problems that may have arisen as a result.
  • Creating and managing databases as well as data systems, as well as restructuring data into a format that can be read.
  • Carrying out analysis in order to determine the significance of the data and the quality of the data.
  • Filter the data by going through the reports and the performance indicators in order to locate and fix any code issues.
  • Employing statistical methods to locate, investigate, and understand patterns and trends in extensive data sets, which could be of assistance in making a diagnosis or making a prognosis of some aspect of a medical condition.
  • The process of providing critical business functions with a numerical value in order to evaluate and evaluate and compare the performance of the firm throughout different time periods.
  • Conducting research into regional, national, and international tendencies that have an effect not only on the company but also on the market as a whole.
  • Putting together reports for management that state trends, patterns, and predictions utilising data that is pertinent to the situation.
  • Working together with system programmers, engineers, and management to pinpoint areas where processes could be improved, suggest changes to existing systems, and develop data governance plans.
  • Preparing final analysis reports for the stakeholders so that they can understand the phases of the data analysis, which will enable the stakeholders to make crucial decisions based on a variety of facts and trends.

The Exploratory Data Analysis Project (EDA for short) is yet another essential part of the job description for the position of data analyst. When working on projects of this nature, a data analyst will need to carefully examine the data in order to find and recognise trends. The following step for data analysts is to utilise data modelling approaches in order to describe the overarching characteristics of data analysis.

Skills needed for data analyst required

If you want to be successful as a data analyst, you need to have the following eight skills:

  • Knowledge and experience with statistics.
  • Analyzing and visualising data.
  • Reports and dashboards are available.
  • Communication and writing expertise Domain-specific expertise Solution-oriented communication and writing.

Let’s have a look at the skills you need to become a data analyst now that you’re familiar with the job description for the position. It is essential for a good data analyst to possess both strong technical knowledge and strong leadership abilities. If you want to develop a successful career as a data analyst, having a background in a discipline such as mathematics, statistics, computer science, information management, or economics might serve as a solid foundation.

Competencies essential for a data analyst

  • Strong mathematical abilities can assist in data collection, measurement, organisation, and analysis.
  • A familiarity with many programming languages such as SQL, Oracle, R, and MATLAB, among others.
  • Competence in the technical aspects of developing database designs, working with data models, employing methods for data mining, and segmenting data.
  • Experience with the management of reporting packages such as Business Objects, programming (Javascript, XML, or ETL frameworks), and database management is required.
  • A solid understanding of statistics as well as statistical software such as Excel, SPSS, and SAS, which can be utilised for data set analysis.
  • Experienced in the use of data processing technologies such as Hadoop and Apache Spark.
  • A familiarity with data visualisation software such as Tableau or Qlik is required.
  • Having the knowledge to be able to construct the most accurate algorithms and apply them to datasets in order to identify solutions.
  • Problem-solving skills.
  • Accuracy and careful attention to the specifics.
  • Competent at answering questions, composing reports, and giving presentations.
  • Team-working talents.
  • Abilities in both verbal and written communication.
  • Proven experience in the workplace performing analyses of data.

What Kinds of Tools Are Utilized in Data Analysis?


SQL is one of the most crucial tools for analysts, as it is used extensively for data analysis in major organisations and is recognised as one of the most vital tools. Software developers also make use of SQL in the process of developing software. The Structured Query Language, or SQL, is a type of computer language that was developed specifically to manage data from relational databases. Learning how to use this tool is not difficult, and yet it is capable of doing complex and challenging data analytics. Because the code is easy to read and understand, and because it can be used to change and update data, it is a popular choice among those who work in the data analysis industry. In addition, it enables you to compile data in a manner that is comparable to Excel, but you may do so over vast datasets and simultaneously across several tables.

Microsoft Excel:

Excel is the programme that a data analyst has to become proficient in the most because it is the industry standard for data analysis. Excel is a straightforward application that can be learned quickly, and data analysts should be proficient in all aspects of the programme, from formulas to pivot tables. Any application that can create spreadsheets will do, although Microsoft Excel is by far the most common and widely used.

SPSS and Visual basic for applications :

In addition to the equipment described previously, analysts typically require a statistical analysis programme such as SPSS. For recently certified analysts, SPSS is a fantastic option to consider (more on SPSS below). It’s possible that more experienced data analysts will need to be familiar with VBA, which stands for Visual Basic for Applications. It is a programming language that was developed specifically for Excel and is utilised rather frequently in the process of financial analysis. Additionally, it is compatible with Word and PowerPoint. Matlab is yet another powerful programme that can be utilised for the development of algorithms, the construction of models, and the examination of data.

Jupyter Notebooks:

Project Jupyter is an innovative platform that focuses on the creation of open-source software, open standards, and interactive computing applications. There is a large variety of programming languages that are compatible with it. You have the ability to create and share documents using the Jupyter Notebook platform, which is an open-source online application. These documents can include live code, equations, pictures, and narrative prose. The notebook has several applications, some of which include data purification and transformation, machine learning, and even more.


Another essential and popular instrument in data analysis is the open-source programming language R, which works on all operating systems, including Windows, Mac OS, and Linux. R is also compatible with all modern web browsers. Because it offers a diverse selection of statistical and graphical tools, as well as the capability to perform data wrangling, it is regularly used by statisticians for statistical modelling. This is one of the reasons why it is so popular. It is accessible in a variety of libraries such as Plotly and enables data analysts to generate data visualizations such as plots and graphs. It is utilized in the banking and sales industries, in addition to various scientific fields including the medical and technological fields. In order to make use of this data analysis tool, you will need to have a fundamental understanding of programming and statistics in general.


Tableau is yet another application that serves a useful purpose in the hands of data scientists. Due to the ease with which it can evaluate data, it has found widespread application. In addition to dashboards and spreadsheets, visualizations may be built using a variety of tools. Tableau makes it possible to create dashboards that provide information that can be put to use and advance a company. Tableau products always run in virtualized settings if they are properly configured with the appropriate underlying operating system and hardware.

The Statistical Analysis System:

Better known by its acronym SAS, is a popular and widely used commercial suite of tools for business intelligence and data analytics. It was initially developed in the 1960s by the SAS Institute, and since then it has through various stages of development. Currently, customer profiling, reporting, data mining, and predictive modelling are the key applications for this technology. The software is typically more powerful, versatile, and user-friendly for large organizations because it was developed specifically for the commercial sector. This is because they have varying degrees of in-house programming competence, which is the reason for this difference.

Power BI from Microsoft:

Since it has only been on the market for a little under ten years, Microsoft Power BI can be considered a novice to the market for data analytics tools. It was initially developed as an Excel add-on, but in the early 2010s it was upgraded to become a whole suite of tools for business data analysis. Users are able to build interactive visual reports and dashboards more quickly and easily with the assistance of Power BI. It works well with Excel (as one would expect from a tool developed by Microsoft), but it also works well with text files, SQL servers, and cloud sources such as Google and Facebook analytics. This is the product’s primary selling feature because of its strong data connectivity.

Tool used by analyst

What kind of qualifications are needed to be a data analyst?

Let’s get some more information on the credentials of a data analyst after we go over the job description and competencies for a data analyst. If you want to be successful in a profession in data analytics, you need more than just technical know-how. The ideal candidate will have a bachelor’s degree in a discipline that places a premium on analytical and statistical prowess. Students with a background in mathematics, statistics, computer science, or economics typically have an advantage when applying for data analyst positions in the workforce. A postgraduate program in data analytics, such as Data Analytics Bootcamp, on the other hand, can prepare you to be a successful professional in the field.

In addition to that, you would require soft data analyst abilities such as:

  • Great communication and presentation abilities.
  • Capacity for reflective thought and creative endeavours.
  • Problem-solving abilities that are characterized by being methodical and rational, as well as teamwork abilities.

How much does a data analyst make?

Are you sufficiently energized by the job description for data analyst? If that is not the case, how about we take a look at some compensation estimates for this in-demand career role? However, it is important to keep in mind that the income of a data analyst is determined by a variety of criteria including educational qualification, location, relevant experience, and skill set.

It is possible for an experienced data analyst to make anywhere from around $60,000 to $140,000 per year on average in income. Companies in the financial sector and the technology industry typically provide greater pay packages than other types of businesses.The average compensation for a data analyst in all markets is around $73,528 per year.

Data analysts often advance their careers to take on more responsible roles such as senior data analysts, data scientists, data analytics managers, business analysts, and so on. When you take on additional responsibility, you should expect a significant compensation increase as well. According to some estimates, the beginning compensation for a data scientist is somewhere about $95,000 per year, but the beginning salary for an analytical manager is approximately $106,000 per year.

Top Companies Hiring Data Analysts

If you are interested in working as a data analyst, there are now more than 86,000 vacant positions available all around the world. It’s incredible, isn’t it? This is mostly due to the fact that data analysis is beneficial to virtually every industry. The role of a data analyst has evolved over the years to encompass a wide variety of subfields, including healthcare, finance, business, marketing, and e-commerce, to name a few.

At the moment, businesses that specialize in business intelligence have the most available positions for data analysts in both the United States and Europe. This is followed by businesses that specialize in banking, sharing economy services, healthcare, and entertainment.

Amazon, Netflix, Google, Intuit, Facebook, Apple, and CISCO Systems are just few of the leading worldwide organizations that are currently looking to hire data analysts. Focus KPI, Affinity Solutions, and Norgate Technology are some examples of the types of smaller organizations that are hiring data analysts. Financial behemoths like Paypal and Barclays are also hiring data analysts across a variety of departments.

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