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Data analytics is the process of analyzing raw datasets to locate patterns and trends. This information is then used to inform an organization’s decision-making process. If you’ve always wanted to learn data analytics but aren’t sure how to get started, this guide is for you. Here, you’ll learn more about the various ways to learn data analytics, free resources to explore, and the types of careers that commonly use 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.
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.
Because more data is being created than ever before, and because so many organizations realize its importance, the field of data analytics is one of the most popular professions currently available. Those with skills in analyzing data are valued in various industries because they can help organizations translate these numbers into actionable insights.
Data Scientists rely on elements of the analytics process, such as creating algorithms, performing statistical analysis, and running data tests to understand and use data to help an organization’s decision-making process. Machine Learning Engineers also rely on data analytics tools and skills as they design models, algorithms, frameworks, and systems to help machines independently carry out functions.
Those working as Business Intelligence Analysts also use data analytics to help improve how their organization operates. Their job often entails collecting, sorting, and analyzing internal and external data to spot patterns, trends, or issues that require attention. These data findings provide policymakers with insights that can help them devise strategies to improve the business's overall operations.
Data Architects work with data analytics when they create data management frameworks designed to help their organization translate business objectives into a system that can be used across an organization. Those who work as Marketing Analysts draw from their data analytics skills as they work to help their company perform market research, spot purchasing trends, and process customer surveys. Their strategies empower their company to connect with customers more effectively and better connect with existing customers.
The driving principle behind data analytics is solving problems. These problems can affect entire organizations and their employees and customers. A background in data analytics empowers you to think analytically when approaching these problems, a skill that extends beyond the professional sector. In addition, this field requires individuals to have excellent communication skills, leadership training, and attention to detail, all transferable skills that can help in all facets of life.
The big data boom has created more of a need than ever before for those with skills in data analytics to help organizations understand their data to capitalize on the insights these numbers reveal. For this reason, Data Analysts are in demand in nearly every industry that works with data. As more businesses and sectors begin to see the value in collecting data and using it to fuel their decision-making process, the more value they see in the data analytics process. This is why pursuing a career in data analytics can lead to high-paying professional options in many industries.
Read more about why you should learn data analytics.
One of the most popular and effective ways to learn data analytics is through in-person and live online data analytics classes. These learning options provide an environment where you connect with an expert instructor in real-time, who is there to field questions, offer guidance, and provide feedback. In-person study also provides a classroom with computers with the latest data analytics software and tools installed. Live online study has the added benefit of allowing learners to complete coursework from the comfort of their home or office space. If you want to search for learning options close to home, Noble’s Classes Near Me tool is a great resource to help you choose from different in-person and live online data analytics courses provided by reputable educators.
Suppose you aren’t fully ready to commit to a data analytics class that meets at regularly scheduled times. In that case, on-demand data analytics classes are available from Noble Desktop and other reputable educational providers. You can complete these classes at your own pace and from any location. Some of these resources are free, whereas others require a fee or a platform subscription for study.
Noble also has various free seminars to help anyone interested learn about big data and other data-related concepts. They offer content on topics related to data analytics, such as a free Data Science seminar and an Intro to SQL seminar. These resources provide beginner-friendly instruction that can help you get a feel for whether working with data is right for you.
Read the full guide on how to learn data analytics.
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.
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.
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.
Learning data analytics has never been easier, thanks to the variety of in-person and online learning options. When deciding which is best for you, the important thing to consider is what you intend to use data analytics for once you learn this skill.
If you are new to data analytics and are interested in an overview of this topic, a great starting point is free introductory-level classes. Many top educational providers, such as Udemy, Coursera, and Noble Desktop, have free online content that presents a basic overview of data analytics and data science. These videos and tutorials can be watched from any location and accessed multiple times if necessary since they are pre-recorded. Because they are free, they don’t require any monetary commitment.
However, if you are interested in developing a working knowledge of data analytics, then a bootcamp in data analytics is a better learning option. These classes are taught by skilled instructors who have industry experience working with data. They provide real-world, hands-on training that can be used in your current career and to plug a skill gap. Many bootcamps offer the option of completing coursework in the live online format, which means you can study from the comfort of your home or office without having to commute to and from campus for scheduled meetings. Although bootcamps can cost several thousand dollars, they are an excellent learning option for those who regularly work with data analytics and want to advance professionally.
If you want to master data analytics to pivot to a new industry or start a new career, a certificate in data analytics is for you. Certificate study provides rigorous training in the field of data analytics in topics like Excel, Python, SQL, and Tableau. Certificate study with Noble Desktop also includes the added perk of one-on-one mentoring for all students and the option of a free course retake to brush up on the material covered.
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.
Answering the “where” has become more and more important in the interpretation of your data. For that purpose, Tableau presents many possibilities when it comes to displaying your data on a map, and no other tool offers as much flexibility and integration with your data.
A dual-axis chart is a chart representing two sets of data overlaid on top of one another, in which the resulting charts will have a shared axis.
We have seen in a previous article that it is possible to create maps in Tableau using various methods. Your starting point will always be that Tableau will need to know the geographical coordinates of what is actually drawn on your map, be it a city, the location of a building, or any geographic place of importance for your data mapping.
In the previous article, we looked at the use of GIS spatial files to create map visualizations. This article aims at taking you further on this path by detailing some useful approaches to choropleth maps.
Similarly to applications such as Microsoft Excel, Tableau offers several functionalities to add calculations to data visualization and to transform your table values quickly. These calculations, and how they are computed, differ from other applications. We will discuss these differences, as well as how to utilize them best.
Parameters are a powerful feature within Tableau. They expand the possibilities of how you interact with the data in your visualization. You can think of parameters as tools to create different scenarios around your data.
Learn how to install Tableau on your computer with this step-by-step guide with pictures.
Besides using the Color Marks Card in Tableau, Colors play an essential role in understanding your data visualizations.
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