Data Analytics

All About the Data Analyst Skills Professionals Need

Written byJohn Terra
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Updated onDecember 13, 2023

Data is the lifeblood of 21st-century society. It’s used in workplaces, learning institutions, financial activities, commerce, and entertainment. Consequently, we produce colossal quantities of data every day, which presents a formidable challenge for anyone trying to make sense of it and pick the specific information that’s most useful.

This glut of structured and unstructured data has created a greater demand for data analytics experts who can work with this data, making expert predictions and creating actionable insights that help companies make the right decisions.

This article details the ten data analyst skills in the most significant demand for 2023. We will explore these skills in-depth and discuss the differences between data analysis, data analytics, and other subjects relevant to data professionals, including how you can upskill to become a data analyst.

So, before we dive deeply into the best skills required for data analysts, let’s establish a definition.

What is a Data Analyst?

Data analysts are professionals responsible for collecting, analyzing, and interpreting vast sets of data to identify patterns, insights, and trends, patterns so that people can make informed business decisions. These IT professionals typically use statistical methods, data analysis tools, and programming languages to clean and manipulate data, create dashboards and visualizations, and create predictive models.

Data analysts are found in many industries and domains, including finance, healthcare, marketing, and government. They play a vital role in aiding businesses and organizations in making data-driven decisions that can improve customer experience, operational efficiency, and business outcomes while mitigating risks. Successful data analysts have strong analytical and problem-solving skills, plus proficiency in data visualization, data mining, data science, and statistical analysis.

As you can see, the data analyst’s role is more than just analyzing data. However, there is frequent confusion between data analysis and data analytics. So, let’s clear the air.

Also Read: Tutorial: Data Analysis in Excel

Explaining The Difference Between Data Analysis and Data Analytics

Although the terms “data analysis” and “data analytics” are often used interchangeably, they are separate and distinct terms.

Data analytics is an all-encompassing term describing the use of data and appropriate tools to make business decisions. Data analytics consists of processes like:

Collecting and ingesting data
Categorizing data into structured and unstructured forms
Managing data, which typically resides in databases, data lakes, and data warehouses
Storing the data in warm, hot, or cold storage
Performing extract, transform, and load functions (ETL)
Analyzing the data to gather trends, patterns, and insights
Sharing this data to business users or consumers, usually presented in a dashboard or with specific storage

Meanwhile, data analysis is a subset of data analytics focusing on cleaning, changing, modeling, and questioning data in an existing data set to find helpful information. The process usually begins with software to perform the first round of data analysis, followed by the human data analyst who questions and investigates the data within appropriate contexts.

There are several data analysis techniques available, including:

Text analysis. Also referred to as data mining, text analysis employs databases and data mining tools to discover patterns in large-form data sets.
Statistical analysis. This form of data analysis answers, “What happened?” and involves data collection, interpretation, analysis, presentation, and modeling.
Diagnostic analysis. This analysis answers “Why did it happen?” by finding the underlying cause from the insights found during the statistical analysis.
Predictive analysis. This analysis uses previous data to answer the question, “What is likely to happen?”. In addition, this analysis uses data to make predictions about future outcomes.
Prescriptive analysis. This analysis combines the insights from the other four data techniques to determine the actions influencing a decision or solving a current problem.

Also Read: Overview: What is Exploratory Data Analysis?

What Technical Skills Are Required for Data Analysts?

Essential data analyst skills consist of both hard and soft skills. So, let’s begin with the data analyst technical skills first.

Data Visualization

This skill involves creating visually compelling, clean charts, maps, and graphs to present your findings to others. Visualization entails identifying the data’s key metrics and relaying them to the organization and stakeholders through storytelling and visual representations. This process typically involves tools such as Tableau.

Data Cleaning and Preparation

Data analysts spend most of their time retrieving data from one or a group of sources and preparing it for categorical and numerical analysis. This skill also covers reconciling or resolving missing and inconsistent data that could negatively affect the analysis. Some sources add “data collection” to this skill definition.

Statistical Knowledge and Probability

Statistics is defined as the field of math and science that involves data collection, analysis, interpretation, and presentation. When you compare this skill and the data analyst’s job description, they’re strikingly similar. The more you understand probability and statistics, the more accurate your analysis will be, and the more likely you will be to avoid logical errors and common fallacies.

SQL

Structured Query Language, or SQL for short, is the standard language used for database communication. Data analysts who know SQL can easily update, organize, and query any data stored in relational databases and modify data structures (schema). Since virtually every data analyst needs SQL to access data from the company’s database, it’s possibly the most essential skill for data analysts to learn. As a result, data analyst interviews typically include a SQL-based technical screening. Fortunately, SQL is one of the more accessible languages to learn.

Machine Learning

Machine learning, or ML, is a branch of Artificial Intelligence (AI) that focuses on building algorithms that find patterns in large datasets. Over time, the algorithm’s accuracy improves, leading to more intelligent predictions. Data analysts don’t have to become ML experts. However, having a working knowledge of this skill can give the candidate a competitive advantage and possibly expedite a move into the data science field.

Software Tools

Data analysts should be familiar with a host of software, applications, and environments. The following is a brief rundown of the most popular resources:

Excel. This classic Microsoft spreadsheet may be laughably venerable, but it’s used by more than 750 million people worldwide.
MATLAB. MATLAB is a tool that functions as both a programming language and a multi-paradigm numerical computing environment ideal for handling big data. MATLAB facilitates data plotting, algorithm implementation, and matrix manipulations, among others.
SAS. SAS stands for Statistical Analysis System. It is statistical software that enables data analysts to perform advanced analytics and queries that relate to predictive analysis and data analytics. SAS can retrieve information from many disparate sources and perform statistical analysis on the data.
BI. Business intelligence (BI) software ingests business data and offers it in user-friendly formats such as charts, reports, dashboards, and graphs.

Python

Python is a well-known, well-established, high-level, general-purpose programming language for software, web, and AI development. It’s an all-purpose, object-oriented coding language that, despite its age, will become even more valuable as AI grows in popularity. Since data analysis increasingly turns to ML and AI to perform many functions, you can see where the connection comes in.

What Other Skills Does a Data Analyst Need?

Data analysts need more in the skill toolbox than just hard skills. Here are some less tangible skills that every good data analyst needs.

Data Analysis and Exploration

So, we’re saying that a data analyst needs to master a skill called data analysis. Who saw this coming? But seriously, data analysis is the name of a skill and a job title. Data analysis is the skill of taking a business need or question and analyzing the appropriate data to form an answer to that question. Meanwhile, data exploration involves looking for interesting data relationships or trends that ultimately benefit the organization.

Critical Thinking

Data analysts must understand the data they see and expand its implications beyond just the numbers. A critical thinking data analyst identifies patterns and extracts actionable insights and information from the data they have on hand. Critical thinking is also a significant part of problem-solving, a soft skill that some organizations demand from their staff.

Communication

All the insights and conclusions in the world are only helpful if you can relay them clearly to your audience. Data analysts need to be proficient at writing, speaking, explaining, and listening, but it’s for more than just the audience of managers, executives, and stakeholders. Data analysts are members of a larger team, so they must know how to communicate in two different “worlds.” You need to discuss highly technical points with your peers and provide clear, high-level explanations that support, not confuse the business-centered decision-makers.

Also Read: Data Analyst Job Description: What Aspiring Professionals Need to Know

What Are the Most Effective Ways of Learning Data Analyst Skills?

Some people learn better in a classroom, while others prefer learning by doing. Whatever your preferred method, many resources are available to help you learn and hone your data analyst skills. So, how do you accomplish this?

First, try to ascertain where your knowledge gaps lie.
Next, focus on which skills you want to learn; don’t try to do everything at once. Take things easy.
Practice on self-started data projects.
Become part of an online community. These communities help build networks and contacts and provide resources and opportunities for skill training.
Don’t get rattled if you make mistakes; instead, use these incidents as opportunities to learn.
Check out online data analytics classes and certificate bootcamps.

Keywords That Pop

It’s common practice for recruiters to do word searches on resumes they receive and focus on the ones that contain the desired terms. Here is a partial list of currently popular keywords you should incorporate in your resume or CV if you want recruiters and interviewers to notice you.

Access Database
Analyze Data
Analyze Data
BI
Business Processes
Business Rules
Customer Service
Data Analysis
Data Collection
Data Elements
Data Entry
Data Extraction
Data Integrity
Data Issues
Data Lake
Data Management
Data Models
Data Quality
Data Validation
Data Warehouse
Extraction
Financial Data
Management System
Pivot Tables
PL/SQL
Power BI
PowerPoint
Profiling
Python
R
Regression
Salesforce
SAS
SharePoint
SPSS
SQL Server
Statistical Analysis
Strong Analytical
Technical Support
Visualization

Do You Want to Learn About Data Analytics?

If you want a career in the data analytics field, you need a well-rounded set of data analyst skills. This six-month data analytics bootcamp teaches you valuable skills that any data analyst will find useful. The bootcamp covers topics such as statistical analysis using Excel, data analysis in AWS, data analytics and manipulation, linear and logical regression, and much more.

According to the Indeed.com job site, data analysts in the United States earn an average of $74,866 annually, with some salaries as high as over $114,000. So, if you’re interested in a career set in a rapidly growing field that provides an essential element for successful businesses while offering you job security, an exciting challenge, and solid compensation, consider a career in data analysis, and start by sharpening those essential skills.