What is Data Analytics?
Each day, huge amounts of data are created. This onslaught has led to a corresponding need for those with data analytics training to make sense of it all. This is why many exciting and high-paying career opportunities are currently available for Data Analysts.
If you’re considering a career in data analytics, it’s important to have the proper qualifications and skill-set to get the attention of employers. This article will explore what kind of training and skills a Data Analyst needs for a successful career.
What Skills Are Needed for a Career in Data Analytics?
Those working with data must wear many hats; they have to have an understanding of how to collect and clean data, patience for analyzing large sets of data, a knack for storytelling, and a passion for finding ways to visualize their findings so that they can be shared with others and ultimately acted upon. The following are some of the main skills, tools, and types of training employers value when deciding whether to hire Data Analysts.
Modern data storage has been around for nearly one hundred years. In the time since, it has evolved from storing information magnetically on tape to the vast database management systems and cloud storage options currently used to retain digital data.
Here are a few of the most common ways data is being stored in 2022 that Data Analysts use regularly:
- MySQL is a relational database management system. Whereas SQL Server is a licensed Microsoft product users have to pay to use, MySQL is free and open-source. MySQL also differs from SQL Server in terms of the languages it supports, as well as storage space, query cancellation capabilities, and capacity for data file manipulation.
- Apache Hadoop is a free software framework based on Java that clusters large amounts of data in order to store it.
- NoSQL databases are non-tabular and are designed to store data in a different manner than it is stored in relational tables. They are able to easily scale with significant amounts of data and large user loads, and offer users flexible schemas.
Data mining involves uncovering relationships and patterns in large datasets. This advanced form of data analysis draws from AI, statistics, and machine learning to help users locate important information. Those who are skilled at data mining can offer their organization insights about customer needs, as well as suggestions for how to cut down on costs and increase revenue.
Here are a few data mining tools that Data Analyst are working with in 2022:
- Oracle: Considered to be the international leader in database software, Oracle provides users with multiple data mining algorithms that can be used for classification, regressing, prediction, and anomaly detection.
- KNIME: This platform is open-source and free. Its pre-built components allow users to quickly model without the need to enter any code. KNIME has an intuitive user interface that’s perfect for modeling and production endeavors.
- Rapid Miner: This software platform is commonly used by Data Scientists and Data Analysts for several stages of data modeling, such as for data preparation and cleaning, exploratory analysis, and data visualizations. Rapid Miner is particularly useful for deep learning, text mining, machine learning, and predictive analysis.
- SAS Enterprise Miner: This data management and analytics platform was designed to simplify data mining and aid with the process of transforming huge datasets into actionable insights. SAS’s extensive set of algorithms can be used to explore or prepare data, as well as to create complex descriptive or predictive models.
Statistics plays an integral role in the data analytics process. It provides users with a means for collecting, reviewing, and analyzing data, as well as a way to draw conclusions from this data and ultimately make better business decisions. Applying statistical methods to data analytics is a vital component, one that fuels new discoveries, informs the decision-making process, and helps predict what is to come.
The following list contains some of the currently most-used statistical tools for data analytics:
- R is becoming an increasingly popular programming language for those working with big data. This powerful scripting language is often used for statistical computing and graphics. It’s also able to handle complex datasets, which makes it a go-to language for anyone working in data analytics or data science. R is generally seen as more approachable than Python for non-developers because users can create a statistical model and compelling visualization with only a few lines of code. R is open-source, offers simple data wrangling options, powerful graphics, and reproducible research.
- MATLAB is a programming language and analytics platform commonly used by Scientists and Engineers. It has applications in image processing, computational biology, deep learning, and machine learning. In order to use MATLAB successfully, it’s important to have a background in coding.
- Microsoft Excel is a spreadsheet software application used for organizing data, making calculations, working with graphic tools, and designing PivotTables. Its ease of use and simple interface make Excel the industry standard for spreadsheets.
One of the most useful skills available for presenting complicated material in an accessible way is data visualization. Presenting data in a visual manner makes it easier to understand and faster to process, even for those who aren’t mathematically inclined or trained in analytics. These visual representations of data are not just visually appealing, they also tell a story about the information, allowing audience members to spot outliers, notice trends, and see patterns emerge from data. Visually conveying points is a powerful way to leverage data in order to achieve the desired outcome.
The following are some of the most-used data visualization platforms currently used by Data Analysts:
- Tableau is one of the leading tools for data visualization. Its ease of use and power make this software one of the best options for those working with data. Tableau enables even non-technical users to design customized dashboards and worksheets that present data in a clear and understandable manner. It is able to integrate with hundreds of sources when inputting data and can output dozens of visualization types. Tableau is most often used for data blending, data collaborating, and real-time analysis.
- Infogram is a suite of data visualization tools that provides users with a means of presenting important data and messages in a visually appealing and understandable format. It optimizes a drag-and-drop visualization tool that enables users who don’t have any prior experience with document design to make creative and compelling visualizations. After visualizations are finalized, they can be easily exported into a variety of formats, like PDFs or JPGs, and then shared with other team members or stakeholders. A variety of chart types can be made using Infogram, like word clouds, dual-axis charts, and pyramid charts.
- Zoho Analytics: A self-service data analytics and BI tool that allows users to work with data from many sources. Zoho can blend data and also design cross-functional dashboards and reports.
Business Analytics & Data Reporting
Business analytics is a subset of business intelligence. It is involved with using methods such as predictive analytics, statistical analysis, and data mining in order to find useful information in large datasets. This information is helpful for identifying and predicting trends and various outcomes, which can be reported and shared with stakeholders in order to facilitate better decision-making.
The following list includes some of the most powerful tools for business analytics and data reporting Data Analysts are using in 2022:
- QlikViewis a classic analytics solution with an associative engine that has transformed the way businesses manage data. It incorporates intuitive visual discovery so that more people can have access to business intelligence insights.
- Microsoft Power BI is a collection of business analytics services that enables users to create data visualizations that can be shared within organizations or on websites. This suite of architectures, technologies, processes, reporting, and data visualization products can be used to extract meaningful information from raw data. Power BI has a clear and easy-to-use interface with which users can design their own dashboards and reports.
- Jupyter Notebook is an open-sourced, free web application that enables users to create and share documents containing visualizations, equations, text, and live code. It is commonly used to clean and transform data. Jupyter Notebook users can document and run code, review the outcome, and create a data visualization based on the insights gathered without having to leave the environment.
In addition to technical, creative, business, and mathematical skills, most employers looking to hire Data Analysts also seek out employees who also possess the following soft skills:
- Communication. Not only are Data Analysts tasked with extracting important information from large datasets, but they must also be skilled at communicating their findings with stakeholders, else these insights will be lost.
- Problem-solving/critical thinking. It’s important to keep the big picture in mind when working with data. Thinking critically about larger data trends and patterns, as well as contextualizing key insights, are just a few of the ways problem solving plays an integral role in the daily tasks of a Data Analyst.
- Teamwork. Data Analysts often work alongside team members or others within an organization who share a common goal of using data to drive business decisions. Not only is it important for Data Analysts to communicate clearly with one another, but to be team players working toward a common goal.
- Work ethic. Data analytics can be a tedious, time-consuming process. This is why aspiring Data Analysts must have patience, perseverance, and a drive to learn all they can about big data.
In addition to the above-mentioned skill-sets that are important for aspiring Data Analysts, it’s also crucial to have real-world experience as well, such as:
- Knowledge of real-world case studies. Data Analysts should be familiar with real-world examples of how data is being handled in various industries, such as government, business, and finance. This kind of knowledge helps paint a larger picture of the role data analytics plays within existing structures.
- Experience with live projects. The know-how to handle real-world challenges is a must for any Data Analyst. This kind of knowledge applies beyond the hypothetical into real stakeholders in real-time.
Although it’s not essential to have knowledge of all the applications, platforms, and skills mentioned above to be a successful Data Analyst, the more training you have, and the better you are at communicating your findings, the more likely you will be to succeed in data analytics.
Start Learning Data Analytics with Hands-On Classes
For those who want to learn more about how to process, analyze, and ultimately visualize big data, Noble Desktop’s data analytics classes are a great starting point. Courses are offered in New York City, as well as in the live online format in topics like Excel, Python, data science, and data analytics technologies, among others.
In addition, more than 170 live online data analytics courses are also available in data analytics and visualization from top providers. Topics offered include FinTech, Excel for Business, and Tableau, among others. Courses range from three hours to nine months and cost from $229 to $60,229.
Additionally, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse more than 420 data analytics classes currently offered in in-person and live online formats. This helpful tool allows users to search for learning options near home in a variety of data analytics-specific areas. Course lengths vary from three hours to nine months and cost $119-$60,229.