What to Learn After Data Analytics

Data analytics is a field devoted to gathering, cleaning, and analyzing data to find relevant and valuable insights. This information can then be shared with others using data visualizations. If you already know how to use data analytics, you may consider learning Microsoft Power BI or progressing into more complex data science concepts, such as machine learning algorithms or relational database skills. We’ll cover how to transition from data analytics into business intelligence or data science below, so you clearly understand what skills you want to tackle next.

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.

Microsoft Power BI

After you’ve developed a foundation working with data analytics, you may consider learning more about business intelligence. Business intelligence is a process that relies on various forms of technology for analyzing data to supply stakeholders with crucial information that they can use to fuel their decision-making process. One of the most widely used tools for business intelligence is Microsoft Power BI, a business intelligence and data visualization tool commonly used by Data and Business Analysts.

Power BI can access large volumes of data from different sources. It also provides a way to analyze and visualize data that can’t be opened in Excel. In addition, Power BI users can perform real-time stream analytics. It allows users to retrieve data from various social media sources and sensors, providing real-time analytics access. This helpful feature enables Power BI users to always be prepared to make business decisions.

Enrolling in Noble Desktop's Power BI Bootcamp is a great way to learn more about Microsoft Power BI.

Relational Databases

Databases are collections of millions (even trillions) of pieces of information that are related to one another. The data contained in databases comes from different sources, like digital footprints and customer activity on web pages. Once this data is analyzed, it offers useful insights, such as how customers are using products or how operations are running. Relational databases sort data into tables that can be linked to one another based on shared data. The information is sorted based on defined relationships, which makes it easier to access. A user can retrieve a new data table by performing a single query.

Data Analysts rely on the tables in relational databases because they provide insights that extend beyond an individual dataset and communicate their relationship to one another. There are also many additional benefits of learning how to work with relational databases. Relational databases, as well as the systems that manage them, are typically stable. Because they use primary and foreign keys so that their tables can interrelate, the data they contain doesn’t duplicate and is non-repetitive. This helps to ensure the data’s accuracy. Because relational databases are relatively simple compared to different network models, they typically operate at a fast speed.

Machine Learning Algorithms

Machine learning is a science that creates algorithms that can learn independently by using data and adapt without human interference. Once data is fed to algorithms, they create their own logic, which can result in solutions and provide insights into topics like price prediction or fraud detection. Deep learning, a subset of machine learning, involves programs discovering detailed complex concepts by crafting them from simpler ones. Machine learning has many applications for those who work with data, such as model-building automation and statistical inferencing.

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.

Key Insights

• Data analytics involves gathering, cleaning, analyzing, and visualizing raw data so that analysts can visually present the insights it offers to inform an organization’s decision-making process.
• Individuals with training in data analytics are an asset to many kinds of organizations, such as those that work with marketing initiatives, customer service, finance, or healthcare.
• If you want to take your training in data analytics to the next step, you may consider studying related skills, such as Microsoft Power BI, relational databases, and machine learning algorithms.
• Noble Desktop has a variety of courses available in the in-person and live online format for those who want to learn more about data analytics or related topics in business intelligence and data science.