How Does Learning Data Analytics Compare with Other Skills?

Take your data analytics skills to the next level by exploring various related skills such as MATLAB, machine learning, SQL, and calculus. These languages and tools can help you analyze vast amounts of data, extract valuable insights, and advance your career in the fast-growing field of big data.

Key Insights

  • Data analytics involves organizing, analyzing, and visualizing large volumes of raw data to provide valuable insights for informed business decisions.
  • Related skills such as MATLAB, machine learning, SQL, and calculus can complement your data analytics training and boost your ability to handle big data.
  • MATLAB is a popular tool among engineers and scientists for tasks like data preprocessing, trend detection, and machine learning model execution.
  • Machine learning, a branch of AI, can be a competitive edge in the data analytics job market. It mimics human learning and improves accuracy over time.
  • NoSQL databases, such as MongoDB, are more flexible than SQL and are better suited for handling big data due to their flexible schema requirements.
  • A strong background in calculus and linear algebra can provide an understanding of the theory behind the analytics practice, aiding in the creation of cost, objective, and loss functions by which algorithms learn how to achieve their objectives.

Data analytics is the act of organizing, exploring, and analyzing large volumes of raw data, such as that from web servers, log files, and transactional information, to locate trends, correlations, and patterns. The information gleaned from the data analytics process can then be presented in the form of reports and visualizations so companies can use it to make sound business decisions. An important factor that goes into deciding to learn a new skill is researching complementary and related skills. Doing so will allow you to make more informed decisions about what you want to learn, and it will help you decide whether learning a specific skill is the right decision for you. You will also discover what kind of skills you may want to learn to complement your training later on. Read on to learn more about related data analytics training options, such as machine learning, MATLAB, SQL, and calculus, which can help you work with big data.

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 Skills?

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.

Skills Related to Data Analytics

Those who work with data draw from a vast range of soft and hard skills. In addition to familiarity with core programming languages like Python and expertise using software like Excel and Tableau, many of these professionals also apply other related skills when handling data. The following sections will explore some skills you may consider learning once you’ve mastered data analytics, as well as how they can be used to work with data:


The multi-paradigm programming language MATLAB is used for various functions, such as manipulating matrices, plotting data, and implementing algorithms. Organizations that are starting to see the value of big data often work with MATLAB because it can help them significantly reduce the time they need to spend pre-processing data. This can facilitate faster data cleansing, analysis, and visualization. In addition, MATLAB has the capability of executing any machine learning model that is built into its environment, and to do so across various platforms. 

MATLAB is a popular tool among Scientists and Engineers for tasks like signal processing, deep learning, video processing, and control systems. It can be used to find trends in signal data, locate meaningful information within images, make accurate predictions using time-series data, contextualize geographic data, and compare date and time information. Although not all Data Analysts use MATLAB, this language is becoming more popular because of how many useful applications it provides for those who work with data.

Machine Learning

Machine learning is a branch of computer science and artificial intelligence (AI) in which algorithms and data are used to mimic the ways humans learn. The longer it’s used, the more accurate machine learning can grow. Machine learning is commonly used across the internet. Websites collect data based on your online actions, such as shopping carts, social media posts, and purchasing patterns. More organizations are beginning to see the value in applying machine learning measures to datasets. Machine learning algorithms can be applied to analyze social media engagement, provide product recommendations on Amazon or movie recommendations on Netflix, and analyze text.

Machine learning is commonly used by Data Scientists, Business Intelligence Analysts, and Financial Analysts, as well as Software Developers and Software Engineers. Although machine learning isn’t as common a skill in data analytics as data cleaning or programming, those with a general understanding of basic machine learning principles have a competitive edge in the data analytics job market.


Most Data Analysts are familiar with programming languages like Python and R. Some also work with SQL, or structured query language, to communicate with databases. However, it’s not as common for these professionals to learn NoSQL right off the bat. 

SQL remains the standard language for querying and managing the data held in relational databases. It remains an effective and high-demand language across companies that work with data. Those who are interested in learning how to write SQL queries can learn more in Noble Desktop’s SQL Bootcamp. You can also study how SQL can be used for data analytics in Noble’s in-person and live online Data Analytics Certificate program.

Unlike SQL, NoSQL’s databases keep information in documents instead of relational tables. They are then classified as “not only SQL” and are subdivided into different data models. Many NoSQL databases exist, such as graph databases, wide-column databases, and key-value stores. These databases were created to process huge amounts of data at scale. They’re flexible and can quickly respond to the data demands of large organizations. NoSQL databases, such as MongoDB, are more useful than SQLl for handling big data because of the flexibility of their schema requirements.

Calculus & Linear Algebra

While it may seem obvious, a strong background in mathematics is essential for data professionals. Although not all Data Analysts studied advanced mathematics in college, most have a solid background in statistics or other related fields. While a range of mathematical skills is helpful for performing data analytics, calculus and linear algebra can be especially useful because they provide an understanding of the theory behind the analytics practice. 

Linear algebra is a popular branch of mathematics for machine learning and deep learning because it supports tensor, matrix, and vector operations. Calculus is another useful form of math in that it’s useful for creating the cost, objective, and loss functions by which algorithms learn how to achieve their objectives. While it’s not essential to have a robust theoretical understanding of calculus or linear algebra, a top-down approach to learning these fields of math can help Data Analysts see the larger picture when completing data projects and how the algorithms they’re using work.

Learn Data Analytics Skills with 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 Takeaway

  • The data analytics process comprises organizing, cleaning, analyzing, and visualizing vast volumes of raw data. The information this process provides is valuable to employers, who can use it to help them make more informed business decisions. 
  • If you’ve already mastered many of the common tools for data analytics, such as Tableau, SQL, and Excel, you may want to expand your professional repertoire and explore other skills and languages that can help you work with big data. 
  • Data professionals can also benefit from studying linear algebra and calculus to gain a theoretical understanding of the algorithms they apply to data. In addition, learning NoSQL, MATLAB, and machine learning can also help when working with data.
  • For those who would like to learn more about data analytics and the tools commonly used in this profession, Noble Desktop offers in-person and live online courses devoted to data analytics and data visualization. 

How to Learn Data Analytics

Master data analytics with hands-on training. Data analytics involves the process of drawing insights from data analysis and presenting them to leaders and stakeholders.

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