In this increasingly digital era, more data than ever is generated daily. As a result, organizations and businesses need professionals in the field of data analytics to decipher that data into practical information they can use to turn a profit, meet their goals, and more.
Data analytics is an immensely profitable realm, even for a beginner. Entry-level data analysts are known to make lucrative salaries and are often elected for promotions due to their skills. So, if you're interested in the compilation, conversion, and grouping of data to draw conclusions, make prognoses, and inform decision-making, this could be the career for you.
Read on to learn more about what a data analyst is, their day-to-day tasks, and how you can begin a career as a data analyst yourself.
What Is Data Analytics?
Data analytics is a step above data analysis; though they are closely related, they're not the same. Data analytics includes procedures beyond analysis, such as data science (theorizing and forecasting according to data) and data engineering (creating data systems).
Data analytics is a method of analyzing data to determine developing trends and assess data points to discover new approaches for improving an enterprise's performance or worth. Many businesses accrue information from their clients or collect their own in-house data, and scrutinizing these sources wholly and carefully can help them to make intelligent business decisions. Studying trends in market data also assists executives in concluding which investments are most likely to induce more significant returns.
What is a Data Analyst?
Data analysts serve as gatekeepers for an organization's data. They collect and break it down clearly so stakeholders can comprehend even the most detailed info and use it to make astute business decisions. The role is a technical one that usually demands a degree.
Companies in every industry, from restaurants to healthcare systems to government, can profit from strategic data analytics. The understanding data analysts grant to an organization is invaluable to employers eager to solve problems, meet demands, and satiate their clients. But no matter the industry, data analysts spend their time creating systems for data collection, seeking meaningful patterns, and compiling their findings into reports that can help improve the health and prosperity of their organization.
Analysts can participate in any aspect of the analysis process in a data analyst role. They can be a part of everything from establishing an analytics system to supplying insights based on the data they assemble, occasionally even training others in the data-collection system. In addition, at small organizations like startups, it's common for a data analyst to assume some of the predictive modeling or managerial responsibilities that may otherwise be appointed to a data scientist.
What Does a Data Analyst Do Day to Day?
A data analyst's day-to-day role involves managing and interpreting complex datasets to provide valuable insights for informed decision-making. They employ specialized software, tools, and efficient methodologies while collaborating with professionals across various departments. These tasks most often include:
Gathering Data and Establishing Infrastructure
Data collection is arguably the most technical aspect of an analyst's job. Initially, data comes as "big data" - large and diverse sets that grow ever-increasingly. Then, analysts need to break this information into smaller chunks, organize it and turn it into something informative. Hence, simplifying the collection process is essential for optimizing their daily tasks.
The amount of raw data seen daily would be overwhelming without the routines analysts develop to help streamline their data collection. These routines can be automated and even modified for recycling in other job areas to make carrying out their duties more efficiently. Data analysts typically use specialized software and tools to help them achieve this, sometimes working alongside web developers to enhance their methods further.
Raw data sometimes incorporates duplicates, errors, or outliers. Cleaning the data means preserving the quality of data in a spreadsheet or through a programming language so that the analyst's analysis will not be flawed.
Cleaning data is crucial because analysts must recognize which data is usable and which is not. Having incorrect data during the subsequent processing step could yield false results, leading to the wrong solutions.
This task refers to converting data from its raw form to a more functional and readable format. At this stage, data analysts move to designing and building database structures. They must choose what kinds of data should be stored and collected, set up how each data category relates to one another, and then work through how the data is represented. This process can be automated thanks to modern machine-learning algorithms, mathematical modeling, and statistical knowledge.
Data analysts spend considerable time producing and maintaining internal and client-facing reports; to make those reports meaningful and straightforward to others (who are usually not analysts), a data analyst must spot relevant patterns in the data that could answer the question at hand.
Reporting in regular increments, such as weekly, monthly, or quarterly, is a standard practice because it helps analysts recognize significant patterns. As the patterns are logged in an overarching time frame, trends are discernible, and analysts can make valid client recommendations.
Reports present valuable insights to management concerning rising trends and areas their organization can improve. But unfortunately, this is one of a data analyst's most time-consuming daily tasks.
Creating a report involves more than jotting numbers down on a blank page or hitting print on a copy-and-pasted spreadsheet. The most successful data analysts can use the patterns in that data to tell a story, fashioning it with informational narratives. To be profitable, the reports, answers and insights that data analysis provides must be understandable to the subsequent decision-maker, who frequently is not an analyst.
Analysts can also use their reports to demonstrate the magnitude of their work in the context of local, national and global trends that affect both their organization and industry.
Collaborating with Others
Most data analyst responsibilities involve the collaboration of other departments in the organization, including marketers, executives, operations managers and salespeople. There's likely also to be close cooperative work with other employees in data science, such as data architects and database developers.
Because of this regular collaboration, an analyst's success depends on their ability to have a professional working relationship with those they are gathering research questions from, peers they collaborate with to execute the work and the people they deliver the final presentation to.
How to Become a Data Analyst
If you are looking seriously into a career as a data analyst, it's helpful to know the skills and traits needed to succeed.
Many employers prefer at least three years of experience in data mining or a complementary field, and having a Bachelor's degree in analytics, mathematics, statistics, computer science, or economics is a bonus. At the very least, candidates must have provable analytics skills, including mining, evaluation, and visualization, and experience in technical writing in relevant areas such as reports, queries, and presentations.
Additional qualifications in job descriptions:
Experience with database and model design and segmentation techniques.
Knowledge of coding languages like SQL, Oracle, R, MATLAB and Python.
Practical experience in the use of statistical packages, including Excel, SPSS and SAS.
Skillful with data processing platforms like Hadoop and Apache Spark.
Adept with data visualization software like Tableau and Qlik.
Competent at creating and applying the most accurate algorithms to datasets to find solutions.
Besides technical skills, there are requisite soft skills. Successful data analysts possess communication and leadership skills, among others. Here are some of the essential personality traits and interpersonal skills:
A systematic and logical approach to problem-solving.
Attentive to detail.
Capable in a collaborative, team-based environment.
Strong oral and written communication skills.
But here's the top tip for attaining a job in data analytics: get certified.
To stand out in today's job market, it's worth investing in a structured program. The Stony Brook University Data Analytics Bootcamp, is 100% online and flexible and also offers mentorship, project-based learning and a focus on building your portfolio.
Move toward a new career in data analytics today by exploring the Stony Brook University Data Analytics Bootcamp.
FAQs About Data Analysts
Do Data Analysts Code?
Coding is not necessarily a required skill for a data analyst. While a surface level understanding is very helpful when processing programming languages, coding is not a typical task for a data analyst. However, there are certain organizations that include coding in data analysts’ responsibilities.
Can You Learn Data Analytics on Your Own?
Yes, you can learn data analytics on your own. There are several resources available online, both paid and free, that can help you become proficient at data analysis. It is helpful to start with the mathematical foundations of data analysis, which includes concepts in statistics and probability. You can then move on to programming languages like Python and tools such as Tableau.
Who Can Opt for a Career in Data Analytics?
You don’t need to have a particular background or even a specific degree to work in data science or have a data analytics career. A lot of companies have begun to hire candidates on the basis of the courses and projects they’ve completed. So anyone can break into the data analytics industry as long as they’re willing to work on their critical thinking, mathematical, and programming skills.
How Will the Data Analytics Market Change in the Next 5 Years?
The prospects for the data analytics profession are overwhelmingly positive over the next five years. According to the US Bureau of Labor Statistics, jobs in data analysis will grow by 23% by 2031.