Is Data Analytics Hard to Learn?

Are you curious about learning data analytics but worried it might be too hard? Of course, the difficulty that comes with learning a new skill is somewhat subjective. The challenges of learning data analytics depend on factors such as whether you have experience working with spreadsheets, computer programming languages like Python and SQL, as well as if you have a background in creating data visualizations.

No matter your current schedule or comfort level with data analytics, a variety of tools are available to help make learning more manageable than you might think.

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.

What Are the Most Challenging Parts of Learning Data Analytics?

Like any acquired skill, learning data analytics poses unique challenges and requires time and commitment to master. Learning to work with big data can be difficult, especially for those without a technical background or who don’t have prior experience with programming languages or data visualization software. The following breaks down some of the main challenges associated with learning data analytics and offers some suggestions for how to make this process easier:

• The initial step of the data analytics process requires finding the data you will use. Not just any data will suffice; the selected data must be relevant for it to be valuable for an organization’s decision-making process. Data comes from multiple sources, making it even harder for Data Analysts to decide which is best for their organization’s needs. Data warehouses are so popular because they can store data from different sources in one central location. Keeping an organized inventory of the data assets in data repositories can significantly help with this process.
• Understanding the data that’s been accessed is another common challenge facing Data Analysts. This process often requires documenting data assets by keeping track of information, such as every definition of columns in a data warehouse's tables. Modern solutions to this hurdle, such as automated analytic features, can make this process much faster without sacrificing accuracy.
• Once the data has been gathered, it is often messy and must be cleaned. Pre-processing data can be time-consuming for Data Analysts since it involves tedious tasks such as encoding variables and deleting outliers. Although this portion of the analytics process is often considered the worst part of working as a Data Analyst, it’s essential because it ensures that the models used are constructed based on high-quality, clean data. Using techniques like augmented analytics, which involves AI and machine learning, can significantly speed up pre-processing.

How Does Learning Data Analytics Compare to Other Fields?

The field of data science is closely related to data analytics. Data analytics typically involves posing data-related questions, then seeking out the answers by statistically analyzing data. Once answers to these questions are found, the Data Analyst can offer actionable insights that will improve operations, increase revenue, and help the business run more smoothly. This process often involves using data visualization tools, working with computer programming languages, and running various types of analyses.

On the other hand, data science is more interested in questions about the data instead of the answers to said questions. Data Scientists often perform data modeling and open-ended research. In a sense, data science provides the foundation for the different types of analysis the organization will want to perform on the data. This process often requires data wrangling, statistical modeling, and computer programming to analyze large datasets. Data Scientists are often involved with the marketing and sales teams within their organizations, as well as product development and finance. Unlike Data Analysts, who generally respond to decision-makers' requests, Data Scientists are typically the driving force behind the decision-making process.

Learning data science is comparable to data analytics in that both rely on training in computer programming languages such as Python, R, and SQL, as well as a solid foundation in Microsoft Excel. However, while data science focuses more on machine learning, data analytics focuses more heavily on data visualization using programs such as Tableau.

A great way to learn more about data science is to watch Noble Desktop’s free Data Science Webinar.

Why Should You Learn Data Analytics?

Data can provide an organization with insights of immense value that can be used to drive its decision-making process. However, without the analytics process, data is just numbers. More organizations than ever are recognizing the power hidden in these numbers and seeking individuals with data analytics training to help them extract valuable information from datasets. Here are just a few additional reasons why studying data analytics is a good professional path:

• It helps organizations make more informed decisions and predictions. The data analytics process draws from techniques like machine learning, AI, and data mining, all of which can be used to help a business make more accurate predictions. This can help the organization anticipate potential business changes and prepare to react to these changes.

• Learning data analytics can help with marketing initiatives. Data analytics allows businesses to focus on what their customers specifically want and how best to provide it to them. Performing in-depth analyses on data pertaining to client trends is one way they can create more focused and ultimately successful marketing that targets specific customers who want specific products or services.

• Data analytics teaches valuable problem-solving skills. One of the core skills of successful Data Analysts is their ability to solve problems that occur on a larger scale than everyday challenges. These problems often affect the entire organization, including its staff and customers. Applying analytic thinking to the problem-solving process is an in-demand skill across industries, professions, and everyday life.

• It’s a popular skill that is expected to get more popular. Because more data is being created than ever before, and because more businesses are beginning to see the value in these numbers, skilled Data Analysts are sought out by many organizations. As more employers begin to depend on the insights found in big data, the importance of Data Analysts is expected to continue to increase.

Why You Should Develop a Data Analytics Process

If you are interested in learning data analytics, establishing a straightforward process is essential to ensure the success of a project. Once you have developed this process, you can apply it to different projects, saving time and eliminating guesswork. The following is one example of a data analytics process that can be adopted and tailored to meet individual needs:

• Start by articulating a clear question. The more thoroughly you can define the question you are attempting to answer and the goals of the entire project, the better.
• Gather data. This process can involve independently collecting data or working with others, such as Data Engineers, who are trained in gathering data.

• Clean the data. It’s essential to ensure that the data you are working with does not have irrelevant, repetitive, or incorrect entries and is standardized.

• Analyze the data. The next step of the data analytics process involves applying tools and techniques to provide insights into the data and find answers to your initial question. Depending on the question you’ve set out to answer, this step can take various forms.

• Communicate the results. One of the best ways to share the results of the data analytics process is to create a data visualization using a program such as Tableau. The visualization can convey the key findings of your analytics process using charts, graphs, or other interactive visual aids so that those from a non-technical background can understand your insights.

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.