Introduction

Data analysis is the act of dissecting, scrubbing, altering, and modeling data with the aim of locating useful information, guiding deductions, and supporting decision-making. The idea that data drives modern civilization is not hyperbole.

Data analysts require a specific collection of abilities, most of which are technical in nature, to be successful in their field. They also need a specific set of soft skills. There is no one method that works for everyone to learn these skills.

Who is a Data Analyst?

A data analyst is a person who uses technological expertise to analyze data and provide findings. These data reports help the recipient comprehend a situation better and are frequently used to guide important decisions and actions. Since this action typically requires additional analysis in order to make specific recommendations, data reporting is commonly misunderstood with data analysis. On a typical day, a data analyst would use SQL expertise to get information from a company database, then use programming expertise to analyze that information before informing a larger audience of their findings. The roles call for obligations like:

  • employing automated techniques to extract data from primary and secondary sources
  • repairing bugs in the code and other problems, as well as eradicating tainted data
  • establishing, maintaining, and reorganizing databases and data systems as well as putting data into a usable format
  • evaluating data to establish its value and quality
  • Filter data by assessing reports and performance indicators to find and fix code issues.
  • Finding, analyzing, and interpreting patterns and trends in huge data sets that can help with diagnosis and prediction by using statistical tools
  • putting a number on important business processes so that business performance can be assessed and compared over time.
  • examining regional, societal, and global trends that affect the business and its sector

Data Analyst Technical Skills

 

It's important to first understand how a data analyst operates. At the risk of stating the obvious, data is what all data analysts are interested in. To sort through enormous amounts of unstructured data and find useful information, they use technology techniques. Cleaning up damaged data, establishing data quality, and creating reports for their supervisor are usually the duties of data analysts. As you can anticipate, each of these responsibilities calls for data analysts to have a strong set of technical skills at their disposal. Here are a few to keep in mind.

Visualization of Data

The ability to portray data conclusions using graphics or other visualisations is known as data visualisation. The objective is clear: It enables people with no background in data analysis to better understand data-driven insights. Data analysts can utilise data visualisation to help company decision-makers, who might not have extensive analytical understanding, recognise trends and quickly understand complex ideas. With the use of this functionality, you as a data analyst can better understand a company's position, share pertinent information with team leaders, and even have an impact on corporate decision-making.

You might be able to do more with data visualisation than you might with standard data analysis. One SAS Insights blogger claims that "data visualisation will alter the way our analysts interact with data." They'll be required to address issues more swiftly. They'll also need to have the ability to dig deeper for novel insights, to view data in novel ways, and to think creatively. Data visualisation will promote more creative data research.

Cleaning of Data

Anyone who follows Marie Kondo will tell you that cleaning is crucial to success, and data cleansing is no different! It takes up a significant portion of any data analyst's day and is one of the most crucial steps in creating a functional machine learning model. The truth is a little different from how we typically envision data scientists to spend the most of their time, which is experimenting with machine learning algorithms and models, according to an Analytical Trainer. Most data scientists devote about 80% of their effort to data cleanup. Why? Due to the machine learning axiom that "better data trumps fancier methods,"

Technical Coding Languages

MATLAB

You can use MATLAB, a programming language and multi-paradigm numerical computing environment, to implement algorithms, work with matrices, and plot data, among other things. Businesses who are interested in big data have started utilising MATLAB because it enables analysts to quickly clean, organise, and visualise data while also allowing them to dramatically minimise the amount of time they spend pre-processing data. Any machine learning model developed in MATLAB's environment, for instance, can be used on a variety of platforms. Although having a basic knowledge of the MATLAB environment can make you stand out to employers, understanding MATLAB is not a necessary ability for data analysts.

R Programming

Among the most popular programming languages for data analytics is R. The professional journal Spectrum from the Institute of Electrical and Electronics Engineers (IEEE) surveyed the top 10 programming languages used in 2019 and ranked R sixth. The syntax and organisation of R were created to support analytical work, and by default, it has a variety of built-in, straightforward data structuring commands. The programming language is popular among businesses because it can handle complicated or vast amounts of data. Given its popularity and functionality, learning R should be a top priority for anyone who wants to become a data analyst.

Python

On the other hand, ambitious analysts should start with Python. Because of the startling number of specialized libraries it has, many of which are relevant to artificial intelligence, this high-level, general-purpose programming language won the IEEE's Spectrum 2019 survey (AI). The usefulness of Python for AI development is particularly significant. By the end of 2020, the AI software industry is anticipated to grow 154 percent year over year and reach an estimated value of $22.6 billion, according to Statista. Data analysts need to be familiar with Python to compete in a professional market that is becoming more and more AI-focused.

Data Analyst Soft Skills

All of the aforementioned technical skills are necessary for data analysts, but they cannot guarantee a successful career. Even if you have great credentials as a data analyst, you won't find work. The explanation is simple: Technical proficiency isn't the end-all be-all for aspiring data analysts. Data analysts need a number of softer, non-industry-specific skills in order to succeed. There are simply too many to list in this essay, so we'll focus on two crucial abilities: communication and critical thinking.

 

Problem Solving

It isn't enough to simply look at data; you also need to understand it and take into account its implications outside of just the statistics. As a critical thinker, you may approach data analytically, looking for patterns and drawing forth knowledge and ideas that can be put to use. Going above and beyond is required, as is applying yourself to thinking rather than merely processing.

Though it can be challenging, pushing oneself will help you build critical thinking skills. The next time you are given analytical work or exercise, try to consider the significance behind the pattern you observe.