Discover the ins and outs of a career in data analytics, from the daily tasks of a data analyst to the expanding global market of big data analytics projected to reach $105 billion by 2027. Learn about how data analytics is becoming more complex with the integration of machine learning and AI, and how this affects the industry.
- Data analytics is a process of analyzing raw data to extract trends and insights which are used for problem-solving, decision-making, and predictions guiding a company's next steps.
- Data analysts are playing an increasingly important role as the global big data analytics market is projected to reach $105 billion by 2027.
- Data analysts are involved on a daily basis in a variety of tasks including data gathering, organizing, analyzing, and sharing findings.
- A data analyst's role is not isolated. They often collaborate with various departments within an organization. Therefore, good communication skills are essential.
- Despite the automation and streamlining of tasks provided by machine learning and AI, data analysts are required to keep up with changes in the field of data analytics, sometimes requiring further coursework or attendance at conferences.
- Noble Desktop offers various data analytics classes in formats such as Tableau, Python, Excel, and SQL, with live online courses and bootcamps also offered on topics like FinTech, Excel for Business, and Microsoft Power BI.
What is Data Analytics?
Data analytics is the set of techniques used to analyze raw data (unprocessed data) in order to extract relevant information, trends, and insights. This process 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 are then used for problem-solving, business processing, decision-making, and predictions that can inform what a company’s next steps should be.
Data Analystsare currently 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. In 2018, the Global Big Data Analytics Market was estimated to be worth $37 billion. This number is projected to reach$105 billionby 2027. 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. Data Analysts are playing a more important role than ever with big data, and likely will continue to do so in the coming years.
Daily Tasks of Data Analysts
Data Analysts are relied on in any field that uses data, such as fast-food chains, retail stores, and healthcare providers. No matter the industry, most Data Analysts are tasked with finding insights in data that can be used to learn more about the needs of the end user or customer. Although the daily 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.
It’s important to note that there isn’t a “typical” day for most Data Analysts. Instead, those who work in this profession can expect to complete a variety of tasks. This is why it’s imperative for Data Analysts to be flexible and adaptable to change. Although it’s expected that a day-in-the-life of a Data Analyst will be full of flux, there are certain elements of their workday that are generally the same across industries and jobs: working with data and sharing their findings with others.
Here are a few of the most common, and universal, tasks a Data Analyst will execute on a daily basis:
- Designing and maintaining databases: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.
- Identifying the problem.Prior to collecting data, it’s important for a Data Analyst to identify and articulate the problem or objective at hand. This often requires a detailed understanding of how the company or organization works, as well as its goals and how it hopes to achieve them. In the problem-identification state, it is common for Data Analysts to work with those in other departments to brainstorm various challenges as well as avenues into improvement. The better understanding a Data Analyst has of the problem, the easier it will be for them to gather useful data.
- Collecting data:Data is at the heart of a Data Analyst’s job, so collecting it plays an integral role in their daily work tasks. 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.
- Defining the criteria for data quality:This is an essential component of the data-collection process. Data Analysts use data quality criteria to establish whether the data in a system is reliable and complete.
- Filtering and cleaning data: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 numbers won’t be incorrect or skewed.
- Finding patterns:Before a report can be 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:Variousstatistical toolsmust 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.
- 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 Salespersons, 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 those within an organization as well as clients. 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:Documenting the 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.Visualizationssuch as reports and charts are used to visually present the information in a way that’s understandable and engaging.
- Keeping up with changes in the field of data analytics.While this is not technically a step in the data analytics process, it’s an important component of a Data Analysts’s job, staying abreast of timely and industry-relevant knowledge, tools, and services. This can involve reading blogs pertaining to data analytics, as well as discussion boards or newsletters. In addition, it may require the Data Analyst to take classes or attend conferences.
Across industries, the job of a Data Analyst is growing more complex, as new modeling and prescriptive analytic techniques are becoming mainstream for analysis. The integration ofmachine learningand AI provides Data Analysts with helpful ways to automate and streamline tasks, but also means that those working with big data must wear many hats to provide their company with the most meaningful insights from the data.
Hands-On Data Analytics Classes
The best way to learn about the current best practices, trends, and industry-standard software and tools in data analytics is to consider enrolling in one of Noble Desktop’sdata analytics classes. Courses are offered in New York City, as well as in the live online format in topics like Tableau, Python, Excel, and SQL.
In addition, more than 170live online data analytics coursesare also available from top providers. Topics offered include FinTech, Excel for Business, and Microsoft Power BI, 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 adata 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’sData Analytics Classes Near Me toolprovides an easy way to locate and browse approximately 400 data analytics classes currently offered in in-person and live online formats. Course lengths vary from three hours to nine months and cost $119-$60,229.