Data analytics is currently one of the most in-demand professions across industries. Those with a background in analyzing and visualizing big data provide their organizations with knowledge about customer behaviors, purchasing patterns, employee satisfaction, or other metrics that fuel the decision-making process. You may worry that data analytics will be too hard to learn. This guide will help you find the best methods for studying data analytics and what you should learn first to make the process easier. This way, you’ll be successful however you choose to apply your new skills.
What is Data Analytics?
Data analytics is a broad term that describes the various techniques used to analyze raw, unprocessed data so that relevant insights and trends can be extracted. Organizations then use this information to make more informed, data-driven decisions. The data analytics process includes gathering data, organizing them, and performing statistical analysis on these numbers. Once the analysis is complete, the Data Analyst offers predictions that can inform the company’s next steps.
Each day, an estimated 2.5 quintillion bytes of data is created. This number is expected to grow as more organizations embrace technological advances and expand their online presence. However, until these data are analyzed, they are just numbers. A Data Analyst’s job involves combining computer programming, math, and statistics to interpret these numbers and transform them into actionable insights that they can share with various stakeholders in their organization.
Read more about why you should learn data analytics.
What Can You Do with Data Analytics?
The field of data analytics has many real-world applications across industries and professions. At the core of the data analytics process is the goal of using data to make better decisions. Companies rely on the data's insights to make more informed business decisions, which can help their organization grow, increase revenue, or better serve employees and customers.
Data analytics can help with marketing campaigns by eliminating the bulk of the guesswork in planning, creating content, and developing products. It also provides an overview of customers, which can help organizations better meet their needs. By better understanding your audience and what they want, targeting them with more pointed marketing initiatives and campaigns is easier.
Another widespread use of data analytics is to improve customer service. When data is analyzed, it reveals important insights about customers, which makes it possible to offer them more personalized service. Data can provide information on how customers prefer to communicate, what interests they have, what concerns they hold, and what products they look at most often. In addition, having data stored in a central location allows an organization’s customer service team to be on the same page as their marketing and sales teams.
Another use of data analytics is to improve the efficiency of operations within an organization. Data can reveal weaknesses in operations, as well as opportunities to streamline various processes. This not only helps the business perform more efficiently but can save money over time. For example, if you have a clearer idea of what your target audience is looking for, you won’t need to waste time and resources designing ads or writing content that doesn’t directly target their interests. This leads to less wasted money, as well as better campaign results.
Is Data Analytics Easy to Learn?
Data analytics is not simply a hard or soft skill but a combination of the two. Those who want to be successful Data Analysts must learn various technical, mathematical, creative, and interpersonal skills. This can require a background in computer programming, data visualization, spreadsheet applications, statistics, communication, storytelling, and time management.
Although the speed at which you learn data analytics will vary depending on several factors, such as whether you enroll in live online or in-person data analytics classes, as well as your prior background working with big data, most consider this field to be relatively easy to learn for those willing to devote the time to their studies.
If you are interested in learning the fundamentals of data analytics, you likely will need to start with computer programming languages like Python and SQL. Some estimate that it takes approximately two months for those with a background in computer programming to learn core Python coding skills and as little as two to three weeks to gain a basic understanding of SQL.
Because the field of data analytics relies on a combination of skills, studying computer programming isn’t enough to be a successful Data Analyst. Often, those beginning their data analytics path also work to acquire other relevant job-related skills while learning to program. It takes approximately 12-14 weeks to learn the core components of the data analytics process, which involves studying data visualization programs like Tableau, studying essential Microsoft Excel spreadsheet functions, and developing a combination of critical soft skills, such as attention to detail, time management, and clear communication.
However, it’s important to remember that the learning process in data analytics is never “complete.” Instead, as the field of data analytics continues its evolution and new technologies are introduced, aspiring Data Analysts should remain committed to ongoing learning. This requires staying abreast of new tools, trends, and updates to computer programming languages.
What to Know Before Learning Data Analytics
Learning data analytics is an ongoing process, one that demands a variety of skills. Some of these skills can be learned during the analytics process, whereas you can learn others before breaking into this field.
Python is a free, multi-functional programming language with various uses for data analytics. Because it has a relatively easy-to-learn syntax, Python is a valuable tool for writing effective solutions to complex problems. Additionally, because Python has an assortment of free public libraries, such as Pandas and SciPy, it is often used in data science and data analytics. Another benefit of learning Python is its data visualization capabilities, such as interactive plots, graphs, and charts. This language’s built-in analytic tools have a range of uses for spotting correlations and patterns in big data, which makes it an important skill to have as a Data Analyst.
Another skill you may want to learn before beginning with data analytics is Microsoft Excel. This spreadsheet program has many uses for Data Analysts. When performing analysis on data, Excel users can perform repetitive tasks easily and quickly, drastically reducing the time needed to complete a project. Because of its clear organizational structure of rows and columns full of cells, users can quickly locate data from a spreadsheet and transform these numbers into actionable insights. In addition, Excel’s visualization capabilities allow users to place visual depictions right next to the data they illustrate, another powerful feature for Data Analysts.
One of the main tasks of a Data Analyst is to transform data findings into visual representations depicting patterns, trends, and outliers. Tableau is a commonly used tool for Data Analysts because it allows them to make interactive, eye-catching visualizations that can communicate the stories behind the numbers to audience members who do not come from technical backgrounds.
Learn Data Analytics with Hands-on Training at Noble Desktop
There’s never been a better time to study data analytics. If you’re interested in learning more about this popular field, Noble Desktop has you covered. Noble offers several data analytics classes for beginners and those with a background in data who want to master specific or advanced tools and skills.
Noble’s Data Analytics Certificate is a project-based course designed to teach students how to collect, wrangle, analyze, and visualize data. Participants complete several real-world projects using predictive and prescriptive analytics to prepare them for careers as Business Analysts or Data Analysts. This course provides all students with one-on-one mentoring, as well as the option of a free course retake to revisit the material.
In addition, Noble’s SQL Bootcamp is a great learning option for learners interested in studying SQL to help with their data journey. This 18-hour class teaches participants how to use PostgreSQL to find information in databases. Those who enroll will learn how to write SQL queries, aggregate data, join tables, and filter results.
For students who want to work with Tableau for data analytics and visualization, Noble’s Tableau Bootcamp is a solid choice. This class is taught by an expert instructor and is available both in-person in Manhattan and in the live online format for those who wish to study remotely. This course takes students on a learning journey, starting with the basics of data visualization. Those enrolled also work with Tableau Public and its various tools for analysis and visualization. By course completion, participants will be able to analyze, filter, structure, and visualize data and publish the results. All students have the option of a free course retake.
- Working as a Data Analyst requires both hard and soft skills. This is why most Data Analysts start by learning computer programming languages such as Python and applications like Microsoft Excel and Tableau.
- If you are interested in studying data analytics, learning basic programming, data visualization, and spreadsheet skills can take approximately three months.
- The speed at which you learn data analytics depends on a variety of factors, such as prior technical training, experience working with data, and mode of learning.
- A great way to learn more about data analytics and the skill-set you will need to work with data successfully is to enroll in one of Noble Desktops classes in data analytics, Tableau, Python, SQL, or Excel.