Every day, companies collect large quantities of data. Customer email addresses, sales transaction details, log-in credentials, online portfolios–these are all examples of the types of data that are stored away in company servers. But what happens to that data? Does it sit, collecting dust, or does it get utilized? In this article, we’ll dig deeper into data science, the types of data analytics, real careers in the field and how data analytics bootcamps are giving students the tools to succeed in this satisfying, in-demand profession.
What Is Data Analytics?
When diving into the field of data analytics, it’s helpful to first understand what’s meant by data. In short, data is information. Data analytics is the process of sorting that raw information.
This data is critical to helping companies adjust their marketing strategies and business models. By identifying patterns, uncovering correlations and finding insights, data analysts can help companies make informed decisions about best practices. Data analytics answers the question “How can this information make our company stronger?”
In order for companies to reach maximum potential, they should examine their current strategies and determine what’s working and what’s not. Companies should be flexible and willing to adapt their business model. By studying real-time data, companies have the tools to understand what’s previously happened, what’s happening now and what might happen in the future.
Because of the vast amount of data generated on a daily basis, data analytics is absolutely essential for every company. Across business, education, health, military and government industries, data scientists are working hard to keep companies performing at their best. And as new digital storage methods continue to expand, the demand for data scientists is at an all-time high, with a predicted growth in job opportunities of 36% over the next several years.
Types of Data Analytics
Different data analysis techniques give data scientists different answers. There’s much to the data analysis process, but here are the four most practiced methods.
Descriptive: The foundation of data analysis, descriptive analysis is the most common method of interpreting raw data. By answering the question “What happened?” descriptive analysis tells us the facts without exploring correlations or cause-and-effect. The stats of an email newsletter–whether someone opened the email, engaged with it, or deleted it–are a good example of descriptive analysis.
Diagnostic: After analysts understand “What happened?” next they will ask “Why?” This is where diagnostic analysis is key. Diagnostic analysis seeks to break down the statistical results of descriptive analysis in order to discover what caused the outcome. Say 60% of the people who received your newsletter never opened it. Diagnostic analysis will thoroughly examine data sets and start digging deeper to explore the reason behind your stats.
Predictive: As can be guessed by the name, predictive analysis seeks to guess the likely outcomes of future events. Predictive models can guesstimate how well something will perform. Perhaps your newsletter will have more engagement if you send it during everyone’s lunch break, when people are more likely to be checking their phones. Or maybe you can schedule your newsletter around the date of a particular movie launch and include that movie’s title in your email subject. Predictive analysis helps companies plan ahead and make smarter choices.
Prescriptive: Now that the data has been examined and future outcomes have been predicted, it’s time to answer the question “What do we do now?” By applying statistical analysis and utilizing machine learning, prescriptive analysis suggests ways for moving forward that entail the least amount of risk and the highest amount of success. It’s the final phase of analysis, incorporating structured and unstructured data and shows companies what actions to take next based on all previously gathered and studied data.
These four methods of data analysis work in tandem with each other, answering different questions and pushing data scientists to the next step. Data analytics bootcamps delve more into these processes and walk students through the step-by-step procedure for solving data questions.
What are Data Analytics Tools?
Like with any profession, certain tools should be properly understood and utilized. In data analytics, those are mostly software tools that help data scientists crunch numbers, organize data, run predictions and visualize information. These tools continue to change as the fields becomes more advanced, but here are some of the basic tools you can expect to work with in data analytics.
Microsoft Excel: Though this Office program might seem basic, it’s a fundamental tool, providing users with data insights and systematic organization. Its ability for real-time collaboration makes it a popular tool for data scientists who need to share with fellow team members.
Tableau: This business intelligence tool is a visualization software which allows chart mapping, data management and more. It can connect to a variety of data sources, which is helpful because it can pull data from different files or systems, putting them all in one place in order to generate reports.
R & Python: Programming languages are necessary for certain statistical and data analysis software. While Python is higher-level with easy syntax, R is an open-source tool great for statistical analysis.
RapidMiner: This platform uses a client/server model and provides both machine learning and data mining. Companies can utilize RapidMiner to study their internal infrastructure and data footprints.
KNIME: This open-source platform is used for integration and reporting. KNIME gathers, cleans, and analyzes data, integrating machine learning and data mining and giving companies insights that drive better decision-making.
Power BI: Another Microsoft product, Power BI is a data visualization software particularly used for business intelligence (BI). With both cloud and desktop capabilities, it’s a great tool for sharing data in a visually immersive way.
These tools, developed over the decades, have made data collection, mapping, storage and visualization more manageable. Any data scientist would be familiar with tools like these and have intimate knowledge of their capabilities. Power BI and Tableau, two of the most popular, receive special focus in data analytics bootcamps, which shows just how integral these tools are to the field of data analytics.
What Does a Data Analyst Do?
Given that data analytics is broadly used across many spheres of industry, the specific responsibilities of data analysts vary, whether they’re analyzing shopping trends for an online store or planning user recommendations for a video streaming service. But, across the board, data analysts will follow the same principles. Here are some of the tasks data analysts perform on a daily basis.
Consult with companies and business owners to determine what the problem is and in what areas the company hopes to perform better.
Collect data using the appropriate tools.
Utilize data visualization software for reports.
Extract, load and transform data using ETL pipelines.
Employ quality assurance to end up with clean, workable data.
Maintain constant communication with company stakeholders, clients and tech teams to see that all needs are met.
Present and report data findings to relevant team members.
Prepare plans for future projects using predictive analysis.
Design and test backend code.
Establish and maintain data processes.
These day-to-day tasks require familiarity with data analytics tools and processes. Programming languages and knowledge of the appropriate computer software are necessary for the daily responsibilities of data analysts. Bootcamps are a great way to study and practice these foundational tools, all on your own time.
What Procedures Do Data Analysts Use?
Now that we understand the daily responsibilities of data analysts, we can explore the methodical procedure analysts use to accomplish their goals.
Define the question
First comes understanding the company's needs. Are clients unsubscribing from newsletters? Has engagement dropped, seemingly randomly? Data analysts seek to get to the crux of the issue. Once they understand the problem at hand, they know what step to take next.
Using the tools we’ve discussed, data analysts will then start digging for quantitative data that can then be studied. Without examining the raw data, analysts won’t be able to see what could be causing the issue. This structured data usually comes from primary or internal sources.
Now that the data has been gathered, it needs to be organized in a way that’s easy to digest. Statistics, crucial though they may be, will mean nothing to the company if that raw data is jumbled or irrelevant. Data analysts have to pick through the data, removing anomalies and eliminating duplicates.
Here is where those methods of data analysis come into play. Descriptive, diagnostic, predictive and prescriptive analysis will happen at this stage as the analyst examines the problem, searches for the appropriate information, and comes up with informed solutions.
Share data findings
In the final step in the process, data analysts will present their reports in a way that’s easy to digest. Charts and graphs can show stakeholders both the problem and the solution. Once all relevant stakeholders understand the proposed solution, the company can move forward in problem-solving
Average Salary For Data Analyst
With millions of terabytes of data generated daily, it’s no wonder data analysts are in such high demand. According to Glassdoor, the average pay for a data analyst is around $70,000 per year. Of course, experience and an advanced degree will help to increase pay. Within the field of data analytics, there are many career paths, each offering their own lucrative pay and incentives.
Careers in Data Analytics
Now that you have a firm grasp of the importance and necessity of data analytics in our current society, it’s time to ask yourself: Is this the right career path for me? The good news is, because companies today are so dependent on data, there’s a need for data analytics professionals across nearly every industry. You have the opportunity to choose the industry you’re most passionate about–whether that’s commercial, medical, entertainment, or government–and search for a job in data analytics
But first, build your skills and resume. Stony Brook University’s Data Analytics Bootcamp offers a 6-month program that lets you work around your own schedule. Learn visualization tools like Microsoft Power BI and Excel, mentor under experts in the field and strengthen your portfolio with two capstone projects that will show hiring managers just how ready you are to enter the challenging, cutting-edge world of data analytics.
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.