A Beginner’s Guide to Data Analytics

Data analytics is a valuable field that allows businesses and individuals to make informed decisions based on key insights. We'll cover everything you need to know about data analytics — from what it is and why it matters, to the tools and techniques used by professionals. By the end of this guide, you'll have a solid understanding of how to get started with data analysis and how it can benefit your career or business.

What is Data Analytics?

Data analytics helps companies store, organize and analyze raw data so they can find trends and patterns that allow them to make better business decisions. As businesses increasingly rely on data to make data driven decisions and gain a competitive edge, understanding data analytics has become a valuable skill for professionals across numerous industries.

Terms to Know

• Data aggregation: The process of collecting and organizing raw data from various sources into a high-level summary.
• Data mining: The process of observing trends, patterns and relationships between large data sets.
• Data visualization: The process of compiling and presenting data through charts, graphs and other visuals.
• Probability theory: A branch of mathematics focused on measuring the likelihood that a random event will occur.
• Regression analysis: A statistical method that tests the relationship between an independent and dependent variable.

Different Types of Data Analysis

1. Descriptive analysis includes analyzing past success of key performance indicators and putting this information into reports. Data aggregation and data mining are the two main types of techniques used.
2. Diagnostic analysis explores correlations between historical data observed in the descriptive analysis and any patterns in the outcomes. Some of the techniques used include probability theory, regression analysis and filtering.
3. Predictive analysis uses the data found in the diagnostic analysis to predict future outcomes. Logistic regression is a popular algorithm using independent variables to predict a binary outcome.
4. Prescriptive analysis takes findings from descriptive, diagnostic and predictive analyses to determine what actions need to be taken to resolve any company issues.

Popular Analytics Tools

There are a number of data analytics tools available, ranging from data mining to data visualization. Below are some of the most popular analytics tools used to collect and analyze data.

• Structured Query Language (SQL): Programming language used to extract and analyze large amounts of data, combine tables and manipulate datasets.
• SAS: Analytics platform used to access data and create reports in a number of different formats, manage existing data and use statistical techniques to analyze data.
• Power BI: Business intelligence platform used to create and enhance data models, share charts and graphs within an organization, view patterns in data through machine learning capabilities.
• Python: Open-sourced language used to create and manage data sets, merge, pivot and slice data sets, write your own datasets and perform data reprocessing.
• Tableau: Data visualization and analysis tool with a drag and drop interface for quick data visualization, data discovery and exploration capabilities, and offers users the ability to join and blend datasets.

Data Analytics for Business

Analyzing the target audience enables businesses to pivot their strategy and boost sales. Services or products can be adjusted based on these findings which should help retain current customers or clients and gain new ones. By understanding what is working within the company, businesses can avoid wasteful spending on marketing strategies that don’t deliver results. Additionally, data analytics helps businesses gain more insight into what areas of the company are underperforming so they can choose to make improvements. Similarly, they can view competitors to explore any gaps in their own business model.

The Role of a Data Analyst

Data analysts can spend time analyzing a business’ sales patterns and trends, user and customer behavior, customer satisfaction and more. By finding patterns in both qualitative and quantitative data, data analysts can create and distribute reports to leadership and team members, helping to address company problems and explore strategies that have been successful. One of the most important qualities that data analysts possess is their keen ability to tell a story through data. When providing reports, other members may not understand the research so data analysts can explain why this data is important and how it is impacting the company.

Data analysts pull data through different methods related to their business. For instance, in the healthcare sector data analysts might analyze patient information, patient surveys or clinical trial data. Data analysts that work in a marketing department might use Google Analytics to understand how users are navigating the website. Some other industries that utilize data analytics to make informed business decisions include finance, tech, insurance, retail, education and manufacturing.

Why Learn Data Analytics at JWU?

JWU’s Bachelor’s Degree in Data Analytics is offered at our Providence Campus, located in the heart of the city. In this program you’ll be introduced to applied statistics, computer science, database design, programming concepts and more. While you’ll gain the skills and knowledge needed to succeed in your career in data analytics from courses taught by our expert faculty, our internships opportunities will give you the experience to gain real-world, hands-on experience.

If you’re interested in expanding your data analytics knowledge after completing your undergraduate degree, learn about our Accelerated Master’s Degree in Data Analytics.