What is Data Analytics?

Data analytics is the process of analyzing raw data in order to extract relevant information, trends, and insights. This includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, conclusions can be drawn from it, which can be used for problem-solving, business processing, decision-making, and predictions that will be used to inform what a company’s next steps should be.

Data Analysts are in high demand as companies expand their analytic capabilities to keep up with the ever-growing stream of data that must be processed and analyzed. Cloud computing, along with mobile data traffic and AI technologies, is part of a rapid expansion not only of the volume of data that has to be stored and processed, but also its complexity. In 2018, the Global Big Data Analytics Market was estimated to be worth $37 billion. This number is projected to reach $105 billion by 2027. As these numbers indicate, Data Analysts are playing a more important role than ever with big data, and likely will continue to do so in the coming years.

What Do Data Analysts Actually Do?

Data Analysts wear many hats and perform a variety of tasks within a company or organization. Regardless of industry focus, Data Analysts search for insights in data that can be used to learn more about the needs of the end user or customer. Although the specific tasks of a Data Analyst depend on the kind of data they are handling, as well as the project at hand, nearly all Data Analysts are involved with data gathering, organizing, and analyzing. 

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The following is a breakdown of some of the core responsibilities of Data Analysts:

  • Mining data: Data is at the heart of a Data Analyst’s job, so collecting reliable data plays an integral role in their daily work tasks. This process involves sorting through huge stores of pure data with the hope of locating useful nuggets. Data that is mined from primary and secondary sources must then be reorganized into a format that can be understood by a human or machine. Often, Data Analysts work with Web Developers to streamline the data-collection process. The more automated and reusable a routine for data collection is, the more streamlined the process becomes.
  • Filtering and cleaning data: Data doesn’t always present reliable information. Raw data often has outliers, duplicates, or errors present that must be filtered out before the data can be processed. Cleaning data enables a Data Analyst to maintain data quality to ensure any interpretations of the data won’t be incorrect or skewed. This process can involve correcting errors in syntax or spelling in data, incorporating information from a prior model, and addressing any noticeable problems that could affect the quality of the insights. The data cleansing process ensures that the resultant data contains uniform, standard datasets with relevant information that will be used to propel sound strategic decision-making. If this step is omitted or not executed completely, it can result in customer dissatisfaction, as well as incorrect earnings forecasts.
    • Identifying patterns: Before a report is generated and data can be used to tell a story, a Data Analyst must locate valuable patterns within the data itself. Most Data Analysts report in regular time frames, like weekly or quarterly, to ensure that trends over time are noted. 
  • Using statistics to interpret data: Various statistical tools must be applied to datasets in order to interpret data, as well as isolate patterns and trends. Statistics plays a vital role in the Data Analyst’s job in that it helps to contextualize the work based on local, national, and international trends that have implications for the industry or company.
  • Modeling data: Data Analysts must ensure that databases are running smoothly, which involves fixing data-related problems and coding errors. The process of creating a database’s structure allows Data Analysts to model data based on such parameters as what data types should be collected or stored, as well as how the categories of data relate to one another.
    • Collaborating: The notion that a Data Analyst works in isolation, apart from the others at an organization, is largely a myth. Most Data Analysts regularly interact with those in various departments, such as Salespeople, Engineers, Programmers, and Marketers. They also typically collaborate with Database Developers and Data Architects. Because so much interdepartmental interaction occurs in a Data Analyst’s job, good communication skills are essential.
    • Creating reports: A good portion of a Data Analyst’s time is devoted to creating reports for both those within an organization as well as clients or external stakeholders. These reports provide key insights about areas for improvement, as well as emerging trends. A successful report involves much more than collecting numbers to display; it must weave together a clear, compelling narrative that can be accessed by decision-makers who aren’t trained as analysts.
  • Documenting the analytic process: Keeping detailed records of the data analytics process is an important act in that it helps stakeholders appreciate the steps that went into analyzing the data so that the findings can be duplicated.
  • Presenting the findings: The final stage of the data analytics process involves sharing the findings with others within an organization or external stakeholders. Visualizations such as reports and charts are used to visually present the information in a way that’s accessible and engaging. 

What Tools do Data Analysts Use?

In order to organize, analyze, and visualize data, most Data Analysts employ a combination of tools and techniques, such as:

In most corporations, the job of a Data Analyst is growing more complex, as new modeling and prescriptive analytic techniques are becoming more mainstream for analysis. The integration of machine learning and AI provides Data Analysts with helpful ways to automate and streamline tasks, but also means that those working with big data must have a wide range of skills and capabilities to provide their company with the most meaningful insights from the data.

Hands-On Data Analytics Classes

A great way to learn about the current best practices, trends, and industry-standard software and tools in data analytics is to enroll in one of Noble Desktop’s data analytics classes. Courses are offered in New York City, as well as in the live online format in topics like Python, Excel, and SQL. 

In addition, more than 180 live online data analytics courses are also available 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.

Those who wish to study in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 110 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others. 

Are you searching for a data analytics class near you? If so, Noble Desktop’s Data Analytics Classes Near Me tool provides an easy way to locate and browse over 400 data analytics classes currently offered in-person and live online formats. Course lengths vary from three hours to nine months and cost $119-$60,229.