In the digital age, data is everything. However, not everyone is cut out for interpreting data. Therefore, for that purpose we need experts – those with data analyst skills.
In this article, we’ll look into who data analysts are, their role in an organization, and the essential data analyst skills that are needed to interpret big data.
Let’s get started.
Table of Contents
What is Data Analytics?
Data analysis refers to analyzing raw data in order to unravel its meaning, look for patterns, and extract valuable insights from it.
Data analysts and scientists employ data to understand its impact on future trends and measure the effectiveness of key metrics that can optimize a system’s efficiency.
What is the Role of a Data Analyst in an Organization?
Data analysts play a fundamental role in every organization by driving well-informed, data-backed business decisions. They identify, analyze, and present data sets that are essential for the progression of a business.
Every organization needs data analysts to improve their product’s quality, and in turn increase their chances of a high return-on-investment.
Be it a healthcare organization, travel agency, or any business targeting a diverse audience – data analyst’s work can help them achieve exponential growth.
Using their analytical skills and expertise in various tools and programming languages, they perform the following actions:
Data Sourcing
Data analysts collect raw data using various tools, software, application programming interfaces (APIs), smart (IoT) devices, social media, data feeds, and more.
They extract tons of information by applying structured query language (SQL) scripts in a system’s relational databases – known as data sourcing/data warehousing. In some cases, they also scrap data from websites and collect relevant information from there.
Filter out viable data
Scrutinizing useful data takes up almost 80% of the total time dedicated to the project.
Data analysts team up with business analysts and IT staff to recognize all the important variables and clean data. At this point, they also tag sensitive and personal information to ensure data privacy.
Data manipulation
The process of data manipulation entails plotting out data, finding correlations, and organizing it. Most data analysts use pivot tables in Microsoft Excel to sort out and filter necessary elements.
Other popular data analysis tools and software that professionals usually use include Tableau Public, SAS, and Microsoft Business Intelligence tool (Power BI).
Build Data Visualizations
The best way to present large dataset values is by building graphs, maps, charts, etc.
Data analysts use visualization to communicate their findings, enrich datasets, and signify gathered insights.
Build Machine Learning (ML) and Artificial Intelligence (AI) Models
Most data analysts take a big leap into predicting future trends by training ML and AI models. They utilize past impactful data trends and use them to build predictive algorithms.
Even if the analyst is not technically qualified to “build” a system, they can influence the data science team into building one.
Reconstruct the Models Regularly
Data analysts keep their algorithms operational by collecting new and continually improved data and feeding it to train their models. If not, models get stale and degrade in quality over time.
Furthermore, data analysts evaluate current and predictive trends to help data scientists develop new features in their ML/AI models.
What is Big Data – And Its Uses in Different Verticals?
The term “big data” refers to complex/large data sets that are too complicated to process using traditional data-processing application tools.
To completely understand big data, you must go through the factors that add to its complexity:
- Volume – organizations gather different types of data from different sources. With traditional methods, the storage could become a problem. However, cheaper platforms like Hadoop and Data Lake have made it convenient to incorporate big data.
- Velocity – the extensive use of digital technologies and growth in the Internet of Things result in high data streams. Data transfers at an unprecedented speed and untimed manner, which could be lost if not handled carefully. This especially applies to non-stop transactions (via RFID tags, credit/debit cards, sensors, etc.) that need real-time handling.
- Variety – data is collected in different formats – from structured, unstructured, numeric, digital data to emails, videos, images, etc.
These three V’s are the reason behind the need to originate newer methods to incorporate big data.
The Use of Big Data in Different Fields
Data analytics provide companies a chance to know their audience better, including their products, buyers, suppliers, and consumer preferences.
The use of big data is common in many fields, especially in the domains listed below:
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- Healthcare – healthcare analytics is used in medical staffing, maintaining electronic health records (EHRs), enhancing patient engagement, predictive analysis, and more. Healthcare professionals can also use analytics to prevent disease outbreaks, reduce costs for treatments, and avoid illnesses among patients.
- Travel Agencies – travel agencies rely heavily on their customers. Data analytics unravel customer behaviors, needs, and expectations – allowing agencies to offer tailored plans to their custom audience.
- Accounting – data analytics allow accountants and finance executives to utilize valuable information to optimize their methods and prevent risks. Understanding the necessary variables within the financial vertical provides accountants the ability to strengthen their relationship with business leaders.
- Retail – retail store owners are also reaping the benefits of using data analytics in their processes. To name a few – it helps them understand consumer behavior, match them to products, predict trends, optimize pricing, and create a custom audience for ad retargeting.
Other than that, data analytics is making a remarkable difference in the way agriculture, sports, energy sectors, education departments, and government and public sectors are operating.
12 Top Skills Required by a Data Analyst
Every data analyst’s job description necessitates a set of key skills – a combination of technical, leadership, and soft skills in order for them to excel at their work.
Here’s a list of 12 top data analyst skills that help them achieve their organizational goals:
Technical Skills
Data analyst’s work requires them to have a firm hold of programming languages and technical concepts. Apart from analyzing raw data, they’re also responsible for removing corrupted data, ensuring data quality, and presenting their findings to their employers.
To achieve that, following are the essential data analyst skills that help data analysts to effectively perform their job functions.
Fluent Control Over Microsoft Excel
Microsoft Excel is an extraordinary tool for managing and manipulating data, even if it is not as accessible for massive data sets. However, analysts still use advanced methods in MS Excel like writing Macros, using VBA lookups, COUNTA, etc., for quick analytics.
While big firms employ advanced tools to manage data, small firms/startups may use MS Excel to manage small data sets.
Therefore, a data analyst must know how every feature in Microsoft Excel works.
Fortunately, there is a wide array of courses and training programs to help you get started.
However, mastering Microsoft Excel is only the first step. You still need to sharpen your programming skills to move up in your career path.
Proficiency in SQL/MySQL
SQL is an industry-standard database language, and most certainly one of the top skills a data analyst needs to acquire. It is termed as the “graduated” version of Excel because querying in SQL produces more refined and quicker results.
SQL is mostly useful in operations like storing and managing data, designing relational database management systems (RDBMS), or changing database structures.
Statistical Programming – Python
Python is an object-oriented programming language that is heavily utilized to streamline complex data sets and write scripts. It’s fast, dynamic, and highly readable.
Due to its flexibility, Python is a popular choice among the data analyst and data scientist community. Whether the task is building data models, creating ML-powered algorithms, or data mining – Python does it all, that too, in the blink of an eye.
Apart from that, it has a vast collection of libraries, graphics and visualization tools, and an extended pack of analytics tools, making it the most important programming language for data analysts.
Linear Algebra and Calculus
Mathematical skills – especially linear algebra and calculus – are highly crucial. So much so, that some data analysts choose to specialize in these fields before starting their careers.
Vectors, matrices, and tensor operations in linear algebra have applications in machine and deep learning. On the other hand, the rules of calculus help in driving ML algorithms to achieve their objectives.
Data Visualization and Presentation Skills
The most important data analyst job function is to comprehend data findings for both, technical and non-technical people. Data analysts use graphs and illustrations to make data-driven insights more readable, and help business decision makers to prioritize their tasks.
According to a study conducted by LinkedIn learning, data visualization is currently the number one skill college graduates are learning on LinkedIn.
Machine Learning
Machine learning is not a programming language or skill, per se. It’s a whole field that you need to step in if you want a competitive edge over other data analysts.
Research published by Statista indicates that 31.7 billion US dollars have been invested in AI-based projects.
The worldwide revenue generated from the AI industry is expected to reach as high as 97.9 billion US dollars by 2023. These statistics signify that AI and ML are, in fact, the future of this world and an important skill to learn.
MATLAB
MATLAB is a multi-paradigm numerical computing environment that supports data plotting, data visualization, algorithm implementation, matrix manipulation, etc.
Data analysts love MATLAB for it allows them to perform real time data pre-processing, data cleaning, and data organization.
Soft Skills
In any field, technical or hard skills alone are not enough to promise you a good job or a successful career. You need to develop some soft, non-industry-specific skills to channelize your efforts and maintain a work discipline.
Data analysts should develop the following soft skills:
- Critical Thinking
- Problem-Solving skills
- Communication skills
- Project Management
- Decision making
While technical skills steer the quality of your methods, techniques, and the work you produce, soft skills allow you to control your productivity levels and manage your time effectively.
Therefore, a balanced skill set will raise your demand as a data analyst in your industry.
How to Become a Great Data Analyst?
If you have a bachelor's degree in Computer Science or have completed a statistical and data analytics certification course, you're a perfect candidate for a data analyst job.
However, some employers prefer candidates having a strong background in mathematics, algebra, and calculus.
In some cases, employers also fund their employees to complete a master’s degree in relevant courses.
Nevertheless, it all comes down to a single point – if you have the required data analyst skills and know-how to use them well, you are already moving up on the ladder of success.
Josh Fechter is the founder of The Product Company and a partner at Product Manager HQ.
