It's essential to first define data analytics before delving into what a data analyst does. The practice of extracting meaning from disorganised information is the essence of data analytics. 

Data analysts aim to discover and share critical insights utilising such data by methodically examining data for patterns and linkages. 

What, however, constitutes data? I guess pretty much anything you can think of. Frequently, data are numerical (quantitative data). 

However, anything that can be understood, including words, images, sounds, and other forms of media, can also be considered data (qualitative data).

'Raw data' is the basis of an analyst's work. Without context, raw data are disorganised and virtually worthless. 

We can get important information from them until we have established order amid the turmoil. Data gathering, cleaning, and organisation are all steps in the data analytics process.

Effective data analytics also uses various methods to speed up the process. These include programming, visualisation, statistics, and other topics. 

Fortunately, many of these processes have been automated to speed up the process. Some are even evolving into distinct fields. A qualified data analyst will, however, be familiar with all of them.

Why is data analysis important?

Data analytics is essential for two clear causes. First of all, it aids in decision-making. Second, it is supported by facts. 

When these two characteristics are combined, data analytics becomes a powerful instrument. Making decisions based on empirical data (instead of intuition or "gut feeling") is a far more scientific method of solving issues. 

Data analytics is the most vital tool for forecasting future patterns and making inferences about the past, even though it is only sometimes 100% correct.

The applications of data analytics are numerous and varied in today's world. Data analytics is frequently promoted online as a tool for business information, such as forecasting future sales or guiding product development and marketing expenditures.

What does a data analyst do?

Let's examine what the job of a data analyst truly entails now that we have a better understanding of what data analytics is.

Your duty as a data analyst is to transform unprocessed data into insightful knowledge. 

You'll use the data and the insights it yields to solve specific problems or respond to particular queries after going through the data analysis process, which we'll explore in the following section.

Following that, you will present these insights to important decision-makers and stakeholders so they may act or create plans. 

In addition to establishing standards for data quality, data analysts may also manage the overall procedures for gathering and storing data.

Examining the duties and tasks generally included in data analyst job descriptions is an excellent approach to determining what a data analyst does daily. 

According to actual job descriptions on Indeed.com, the following is what a data analyst would typically do:

• Make databases and data collection systems and implement them.
• Work closely with management to determine key performance indicators (KPIs) and business needs.
• Gather information from primary and secondary sources.
• Clean up and filter data.
• In complex data sets, recognise, examine, and evaluate trends and patterns.
• Explain findings to important stakeholders through illustrations.
• Create and alter reports.
• Create and keep dashboards.
• As data models, measurements, and infrastructure are built, create and update documentation for these concepts.

Data analyst vs data scientist

As a result, you could have already read articles discussing data science when researching the data analyst job. 

Even though these two names are sometimes used interchangeably, they refer to two distinct professional paths with different functions that call for specific skill sets.

As we've already discussed, data analysts use a company's data and interpret it for individuals making business decisions. 

Their work is centred on providing answers to issues and creating solutions by examining data trends and transforming them into dashboards and visualisations for broader usage.

A data scientist will explore the data to find patterns using machine learning and data mining. 

They will design experiments to support or refute their conclusions and create models and tests. Based on their findings, they will provide recommendations for how a company should proceed.

Types of Data Analysts

The data analysis process serves a vital purpose and has applications in various industries, as you may have deduced from what has been said thus far.

However, data analytics is much more than a way to increase a business's bottom line. To enhance patient care, it is also applied in medical settings. 

It is currently used in agriculture to revolutionise how the world is fed. Governments even employ it to combat situations like human trafficking. 

Therefore, a career in data analytics can be for you if you wish to contribute to improving both the world and business.

The following are some of the specific job titles you might see on job adverts while looking at different types of data analysts:

• Business analyst
• Business systems analyst
• Business intelligence analyst
• Medical and healthcare analyst
• Operations Analyst
• Market research analyst
• Intelligence analyst

Roles and Responsibilities of a Data Analyst

Your responsibility as a data analyst is to complete each phase of the data analytics process to locate and resolve an issue. 

You can specialise in a specific field as your career develops, such as data visualisation or data engineering. But for a newcomer, it's crucial to understand the entire procedure.

So, what primary responsibilities and procedures should a data analyst follow? The significant jobs are as follows, albeit it's not as simple as doing one thing after another:

Defining objective

The first step is to determine your objective. This is the most challenging step in the procedure. This is because an apparent issue could not necessarily represent the root of a problem. Consider the scenario where you work for a business that wishes to increase sales. Senior management is determined to do this by introducing several new items. As a result, you invest a lot of time and money deciding which items to develop and into which markets.

Collecting data

Finding the data that will best enable you to address the question is your next step after identifying it. This could be quantitative information (like marketing statistics) or qualitative information (such as customer reviews). Third-party data, which includes first-party data from another organisation, and second-party data are the three categories into which data types can be further subdivided (which is aggregated from numerous sources by a third party). You'll need to devise a plan to gather these data if you still need access. This could involve conducting surveys, keeping an eye on social media, tracking internet activity, etc. Once you have the data at your disposal, regardless of how you obtained it, you can clean it.

Cleaning of data

Data that has recently been collected is often in a raw format. This indicates that it needs to be organised, reviewed for accuracy, etc. The data must be cleaned for it to be ready for analysis. A range of equipment and approaches is used to make it more acceptable. Data cleaning chores include eliminating mistakes, duplicates, and outliers; removing unnecessary data (i.e., those that don't support your study); arranging the data more helpfully; filling in gaps; and so forth. You'll validate the data once you've finished this. This entails making sure it complies with your standards. You'll frequently discover that it doesn't, so you'll have to backtrack.

Carrying out an analysis

You can begin your analysis once your dataset is organised and neat. There are many different forms of data analysis, so choosing the most appropriate for the task at hand might be difficult.

Communicating your results

After conducting analysis and coming to some conclusions, the next step is to share these with the people who originally commissioned you to do so. Typically, this entails some form of data visualisation, such as creating graphs and charts. Additionally, it might entail producing interactive reports, presentations, documents, and dashboards. Although it's simple to ignore the artistic merit of this process, getting it correctly is crucial. In addition to adequately interpreting your results, you must communicate them to non-technical staff who are pressed for time. This is crucial since it guarantees that decisions are based on high-quality, comprehended insights.

Skills Set Required by a Data Analyst

• You'll have an aptitude for mathematics and statistics. You might hold a bachelor's or master's degree in applied mathematics, statistics, or computing. Although credentials can be helpful, they are only sometimes required if you're a beginner in the industry. For example, that might be enough if you are proficient in algebra and calculus.
• Programming expertise: An aspect of programming expertise is unavoidable when creating or modifying algorithms that automate data analytics operations (such as parsing or re-structuring massive datasets). Data analytics frequently uses statistical computer languages like R and SAS and scripting languages like Python or MATLAB.
• Database knowledge: Besides programming languages, you'll require some familiarity with analytics engines like Spark and database warehousing technologies like Hive. Furthermore, you must be familiar with database query languages like SQL.
• Excel abilities: MS Excel is an essential component of every data analyst's toolkit because it is frequently used to convert raw data into an understandable format or to automate complicated computations. Make sure you are familiar with its main analytical capabilities.
• Imagination abilities: The capacity to visualise data via graphs and charts are a crucial component of data analytics. This aids in the discovery of trends, correlations, and patterns. You should be able to use Excel to produce tables and charts and Python to plot data.
• Basic understanding of machine learning Nobody will expect you to be an expert in machine learning as a novice because it is a separate discipline. Nevertheless, many data analytics jobs are supported by machine learning principles. You should know the theory, such as the difference between supervised and unsupervised learning.
• Communication is essential in every profession, but it's necessary for data analytics. The importance of gathering accurate insights cannot be overstated, yet it is crucial to successfully convey these to larger audiences. You should be able to express complicated ideas in simple terms, have outstanding interpersonal skills, and be fine making presentations to non-technical staff members and responding to their inquiries.
• Critical thinking is challenging what is before you to comprehend it better. It is the most crucial skill in data analytics. You'll approach projects using logical thinking and deduction, have a naturally curious mindset, and won't accept anything at face value.
• Creative problem-solving: When solving a problem, you should use your reflecting perspective on the particular data-related circumstance or issue at hand. You'll do so step-by-step when describing a problem, coming up with a solution, and performing the essential follow-up duties. You'll need to have a creative mentality because these activities will vary.
• Ethics: You'll be conscious of your prejudices, grasp the value of data protection, and feel at ease delivering results, even when they are unwanted or unlikely to garner any acclaim. It is crucial to uphold a strict code of ethics. Without it, data can be readily abused, which may harm the people and groups impacted by your work in the real world.