Like many aspiring Data Analysts, you may want to learn data analytics but worry that it will take too much time. Learning to analyze and visualize data is a process that requires training with a variety of tools, languages, and applications, such as Microsoft Excel, Python, Tableau, and statistics. It is estimated that most people can acquire basic proficiency in data analytics in as little as three months. Of course, this depends on several factors. Keep reading to see how you can learn data analytics and find some resources to help speed the process along.
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.
Average Time it Takes to Learn Data Analytics
Learning data analytics is a process that requires knowledge of several related fields, such as statistics, data visualization, computer programming, and spreadsheets. If you want to become proficient in basic data analytics concepts and skills, most people can learn core concepts in each discipline in approximately three months. However, this number is just an estimate that depends on various factors. The method of learning you select, as well as your background in math and working with data, will also contribute to the speed at which you learn to work with big data. Acquiring more advanced data analytics skills can take much longer than three months. Some estimate it can take between three and four years to achieve a mastery of data analytics.
There is no “right” way to study data analytics, but rather many approaches to learning this set of skills. The prior knowledge you bring to learning data analytics can play a huge role in how quickly you learn the skills needed to be successful in this field. In addition, your professional goals for studying data analytics also factor into your learning process. The following are several variables that will inform your data analytics learning journey:
Experience with Computer Programming
If you already have experience working with computer programming, learning data analytics will be much easier and faster than those with no prior programming skills. A background in
Python is standard for Data Analysts. This multi-functional programming language includes many free libraries with applications for Data Analysts and Data Scientists, making it a go-to language for those working with big data. In addition, Python has data visualization capabilities and can transform numbers into plots, graphs, and charts, which is useful when conveying complex information to stakeholders from non-technical backgrounds.
Another tool commonly used by Data Analysts is SQL or structured query language. SQL is a popular language that allows users to communicate with relational database systems. This plays an integral role in the data analytics process since it is a common way to retrieve data. SQL is based on English syntax, which makes it relatively easy to learn.
Familiarity with Spreadsheets
Another factor that will play into how quickly you learn data analytics is whether you have a background working with spreadsheets. Prior training with Microsoft Excel, a spreadsheet application that’s available as part of the Microsoft 365 suite, can make it much easier to organize and analyze data. This tool has an array of applications for data analytics; it can quickly execute repetitive tasks, visualize data, and perform complex analyses in mere seconds that would otherwise take hours to complete. For those new to working with data, becoming familiar with Excel is a good starting point.
Prior Knowledge of Data Visualization
Data visualization is the process of using visuals to represent information or data. This efficient, engaging way of communicating data findings helps Data Analysts present their findings to others who may not have technical backgrounds. Because data visualization plays such an integral role in a Data Analyst’s daily job, a background working with data visualization software like Tableau can be extremely helpful in expediting the process of learning data analytics. Tableau makes it possible to present complex information through storytelling that illustrates data trends, outliers, and other findings in a way that’s memorable and accessible.
One of the most important factors that dictate one’s data analytics learning path is what you want to do with this skill. Studying this field through self-paced online content may be sufficient for those who have a general interest in big data. This study approach would provide a general overview of data analytics, its professional applications, and the basics of the process. However, suppose you are interested in becoming a Data Analyst or looking to acquire more pointed training with Python, Excel, or Tableau to pivot careers. In that case, a more rigorous form of study may be needed, such as a live online bootcamp or in-person certificate course.
Level of Difficulty, Prerequisites, & Cost
Data Analysts rely on many tools and languages to help them organize, analyze, and visualize data. Computer programming languages like Python and SQL play an integral role in the analytics process.
Python is a free, multi-functional programming language with many uses for data analysis. Because of its easy-to-learn, relatively simple syntax, Python is used to write effective solutions to complicated problems in just a few lines of code. In addition, Python has various free libraries available to the public. Libraries such as SciPy and Pandas are among the most popular for data science professionals. Python also includes many data visualization options, such as interactive plots, charts, and graphs. Python’s built-in analytical tools can quickly locate patterns and correlations in massive datasets, making it a vital tool for Data Analysts.
SQL, or structured query language, is another helpful tool for Data Analysts. This querying language was created to communicate with the data located in relational database systems. SQL is based on English syntax, meaning it’s easy to learn, even for those without prior programming experience. SQL is free for testers and developers who intend to use it in non-production environments, like designing and developing software solutions. Students can download SQL Server 2019 Developer for free from Microsoft.
Microsoft Excel is available as part of the Microsoft 365 family. This spreadsheet program has a range of applications for data analytics. This industry-standard spreadsheet application is easy to use and accessible to those who do not have technical expertise. An Excel spreadsheet is made up of rows and columns that contain cells. Each cell contains one data point. When using Excel for data analytics, complex, repetitive tasks are easy to perform and can be executed in a fraction of the time it would take manually. In addition, Excel’s clear organizational structure allows users to easily retrieve data from spreadsheets to locate actionable insights.
Because most Data Analysts create data visualizations as part of their job, another essential tool they rely on is Tableau. Tableau is a popular visual analytics and business intelligence platform that allows users to create stunning, interactive visualizations based on their data findings. This program is considered easy to use and accessible to users from all backgrounds. Its dynamic software allows users to create visual depictions of data trends, patterns, and findings, which can then be shared with others within a company to make better business decisions.
Read about how difficult it is to learn data analytics.
Watch a Free Data Analytics Online Course
Not ready to dive into an entire course? Are you instead looking for an overview of data analytics? If so, then you can start learning data analytics for free online. In Noble Desktop’s free introductory data science webinar, you’ll learn how Python can be used when working with data. Those enrolled will have an overview of the field of data science and will become familiar with the tools Data Scientists use in the real world to analyze datasets.
In addition, other top providers also offer classes in data analytics. Udemy currently has free tutorials and classes in data analytics available on topics like Intro to Data Analysis for Government, Learn Data Analysis Using Pandas and Python, and Introduction to Data Analysis: From Zero to Hero. Additionally, Coursera provides several free classes in the field of data science. They offer classes like Introduction to Embedded Machine Learning, Practical Time Series Analysis, and Foundations in Data Science: K Means Clustering in Data Science.
Read more about free data analytics videos and online tutorials.
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.
- Learning data analytics is a versatile skill-set that helps improve how efficiently an organization operates and its bottom line.
- Depending on your prior training and experience, most people can learn the basics of data analytics in as little as three months. However, mastering more complex analytics and visualization tools can take three to four years.
- A variety of factors contribute to the speed at which you will learn data analytics, such as your prior experience working with Excel, Tableau, and computer programming languages, as well as the reasons you are interested in learning to work with data.
- For those who are interested in receiving instruction from industry experts, Noble Desktop’s in-person and live online data analytics courses are a great way to learn more about the data analytics process.