To find the solution to a problem or provide an answer to a question, a data analyst gathers, purifies, and analyses data sets. They work in a variety of fields, including as government, business, finance, law enforcement, and science.
What types of clients ought a company to focus on in its upcoming advertising campaign? Which age range is most susceptible to a specific disease? What behavioural trends are associated with financial fraud?
As a data analyst, you might have to respond to inquiries like these. Learn more about what a data analyst does, what skills you’ll need, and how to get started on your path to becoming one by reading on.
What is data analysis?
According to data analyst course Malaysia, the practice of extracting information from data to guide better business decisions is known as data analysis. Five iterative phases typically comprise the data analysis process:
- Choose the data you want to examine.
- Gather the data
- Clean up the data before analysis.
- Study the information
- Interpret the analysis’ findings
Depending on the question you’re attempting to answer, data analysis might take on various shapes. More information on different data analysis types is available here. In a nutshell, descriptive analysis explains what happened, diagnostic analysis explains why it occurred, predictive analytics offers future projections, and prescriptive analysis generates actionable recommendations for what to do.
Duties and obligations of data analysts
A data analyst is a person whose role it is to collect and analyse data in order to address a certain issue. In addition to spending a lot of time with data, the profession also requires communicating results.
Many data analysts work like follows on a daily basis:
- Assemble data: Many times, analysts gather their own data. This can entail completing surveys, monitoring website visitor demographics, or purchasing datasets from data collection experts.
- Clean data: Raw data may include outliers, duplicates, or errors. In order to prevent inaccurate or distorted interpretations, cleaning the data refers to maintaining the quality of data in a spreadsheet or through a programming language.
- Model data: This requires developing and planning a database’s structural elements.
- Interpret data: Finding patterns or trends in the data will enable you to interpret it and use it to support your interpretation of the question at hand.
- Present: An important aspect of your employment will be to convey the conclusions of your research. You accomplish this by assembling visual aids like graphs and charts, producing written reports, and delivering information to interested parties.
Types of data analysts
Knowing how to gather, classify, and analyse data has turned into a key component of practically any sector due to the rapid expansion of the types and amount of information we can collect as a result of increasing technology. The criminal justice, fashion, food, technology, business, environment, and public sectors, among many others, all employ data analysts.
Data analysts may also be known by other names, such as:
- Medical and health care analyst
- Market research analyst
- Business analyst
- Business intelligence analyst
- Operations research analyst
- Intelligence analyst
A typical day for a data analyst
In general, a data analyst will take data, arrange it, and use it to draw inferences that are useful. According to Stephanie Pham, an analyst for Porter Novelli, “data analysts’ work differs based on the sort of data they’re dealing with (sales, social media, inventory, etc.) as well as the unique client project.”
The work of data analysts can be advantageous to businesses in almost every sector, including healthcare providers, retailers, and fast food franchises. Employers who are interested in learning more about the demands of their customers or end users may find the insights that data analysts bring to an organisation to be beneficial.
Data analysts may anticipate spending their time creating processes for gathering data regardless of the industry they operate in and combining their results into reports that can aid their business.
Any step of the analysis process can involve analysts. As a data analyst, you can be requested to train people on how to use your data-collection system in addition to helping to build up an analytics system and provide insights based on the data you collect.
You’re prepared to delve into the nuances of working as a data analyst now that you have a general understanding of what data analysts perform.
Data Scientist vs. Data Analyst
In light of all of this, you might be curious about another important data role: the data scientist. While it’s reasonable to presume that there is some overlap in the work they undertake, data analysts and data scientists differ greatly from one another.
People in the area have sought to define and set the position of a data scientist apart from that of a data analyst because the position is still relatively young and somewhat vague. Let’s organise everything based on job responsibilities and skill sets.
Data scientists:
- have a basic understanding of statistics and maths
- possess a keen business sense
- possess mediocre coding or computer science skills.
- Create important performance metrics
- Use business intelligence and analytics technologies to visualise the data
Data analysts
- possess good statistics and maths abilities
- possess a keen business sense
- possess excellent coding and computer science skills.
- Make use of machine learning to spot trends
- based on data trends, make forecasts
- Create software to help with data analysis.
Are strong maths skills required for data analysts?
If you have a background in maths, you’ll have an advantage over some of your rivals and will eventually stand out. Even a related sector, like finance, can be helpful.
A career as a data analyst probably isn’t the ideal choice for you if maths isn’t your strong suit. However, becoming a great data analyst doesn’t require you to be a maths prodigy. Being a mathematician will be far less useful than having a strong understanding of business and the business world because most of what data analysts do requires following a series of logical processes.
This article is posted on SeanDock.
