The term “data analytics” has been a buzzword for a while now, so there’s a good chance you’ve heard or read about it—but do you know what it is and why it’s important? Throughout this tutorial, we’ll cover the basics, starting with defining common terminology, then moving on to real-world examples and hands-on problems to help you better understand this complex field. Whether you are considering attending a data analytics bootcamp or you just want to learn more, read on to explore the fundamental concepts that drive the world of Big Data.
The Importance of Data Analytics
Today, much of our daily interaction occurs via computers and smartphones. While we use our devices to communicate with friends, update social media and surf the web, they’re also being used to quietly collect data points related to our activity. Companies and individuals generate huge amounts of these data daily, and most businesses today can benefit from studying and analyzing both external and internal information. Because this data is unique to each individual, it can help to paint a picture of their personal interests, habits, connections and more, providing valuable insight that can be leveraged in a variety of ways.
Here are just a handful of the many uses of data collection and analysis:
- Gathering Insights — Data analytics help to uncover patterns and trends within a data set, allowing businesses and individuals to interpret campaign results, consumer patterns, product awareness and more.
- Creating Reports — Once the data is gathered and insights are revealed, a data analyst will create a comprehensive report that translates their findings into digestible terms that the rest of their team can leverage for their own purposes.
- Analyzing the Market — From large organizations to individual stakeholders, the insights provided by analyzing market data hold infinite potential for learning about the strengths or weaknesses of your competitors and your target audience.
What Is Data Analytics?
It all begins when a data analyst is provided raw data representing a variety of information about an individual, product or otherwise that has been collected. The analyst must sort through the data and assign values to it before translating relevant information into visualizations and delivering insights to stakeholders.
For example, your internet cookies are a digital representation of the places you’ve visited on the internet, when you visited them, what you did there and for how long. Those cookies can be scraped—or collected—then sorted and analyzed by a data analyst in order to understand your current browsing habits. Those patterns can then be used to predict your future interactions, including products you might buy, websites you might visit or people you might be interested in.
Since data analysts work with a variety of data, those individuals must also be able to determine which tools or techniques should be used to evaluate each dataset. We’ll explore the most popular data analytics tools in the next section, but before we do, let’s define a few terms you’re going to see throughout this data analytics tutorial:
- Artificial Intelligence (AI) — Refers to the construction of a device or program with independent reasoning power.
- Machine Learning — This is a form of artificial intelligence (AI) in which the program is designed to learn on its own.
- Deep Learning — A mathematical model that can be used to uncover complex patterns in data.
- Data Visualization — A way of presenting data that can point out correlations and patterns among variables.
- Data Cleaning — The process of correcting errors within a dataset before extracting values from it.
Tools Used for Data Analytics
While a data analytics bootcamp will cover a wide range of industry tools, we’re going to focus only on a handful of the most common ones you can expect to work with if you decide to enter the field.
- R Programming — Thanks to its user-friendliness and extensive packages available, R is one of the most popular tools used in data analytics. While it’s technically a programming language, R is geared toward statistical analysis and helps users perform tests, build visualizations and map predictions based on input data.
- Python — This open-source—or, free and editable—program provides users with a variety of visualization libraries, including TensorFlow (for deep learning), Scikit-learn (for making predictions), Pandas (for cleaning data) and Keras (for machine learning).
- Microsoft Excel — Used by data analytics teams for internal data, Excel can do much more than just build out spreadsheets. By applying mathematical algorithms to data input into the cells, analysts can use the program to easily identify specific data points or trends they are looking to uncover.
- KNIME — Konstanz Information Miner is another open-source data analytics tool used to model and analyze data. In other words, analysts use KNIME to represent their findings in a way that others can interpret and leverage without having to understand technical terminology.
- OpenRefine — Previously known as GoogleRefine, analysts use the software to clean and transform messy or disorganized datasets into usable information. The program helps to make sense of complicated groups of data by converting values, de-duplicating lists and providing a cleaner overall structure.
- Apache Spark —When data analysts and machine learning developers are working with vast amounts of information, they often use Apache Spark to process and analyze it quickly. The processing engine tends to get a lot of usage because of its speed and efficiency, especially when it comes to processing real-time data.
What a Data Analytics Bootcamp Can Do for You
If you’re curious, good at finding patterns and a strong communicator, you might make a good data analyst. These professionals are constantly looking for interesting, usable insights that may not be obvious to the untrained eye. In fact, their ability to zero in on the important data—and ignore anything irrelevant—is what makes them so in-demand in today’s job market.
Attending a data analytics bootcamp can help you learn and master the skills necessary to succeed as an analyst. Common job requirements include a solid understanding of:
- Machine Learning — While not every analyst will work with machine learning, it’s an important skill to master if you want to grow as a professional in the field.
- Data Cleaning — A majority of your work as a data professional starts long before you can analyze the information. Cleaning is a crucial part of preparing your data.
- Statistics — If your work requires you to determine probabilities within your data analysis, statistics will be a crucial component of your daily work.
- Data Visualization — If you can’t make your data tell a story, stakeholders won’t understand it, know what to do with it or how to properly leverage it.
- Data Analysis Techniques — It might seem obvious, but understanding the questions or problems faced by an organization will help you to transform and study your data.
Example of Data Analytics
If you attend any type of data analytics bootcamp, you’ll work with hands-on examples to better understand how data is collected, cleaned, visualized and communicated. Let’s take a look at a sample problem you might be faced with:
We will use R programming to analyze data from a sample census. Let’s say that our data contains information such as house numbers, ages of individuals in households, income level and other demographic information.
First, input the dataset using a read.csv command. Then, mention the CSV file’s path to read, allowing you to view the dataset. Some values may appear as NA; replace these with zero using the program’s is.na function. Use the summary function to get a summary of all the data. For example, if you wanted to find the maximum, minimum and average values of “Wife_age,” the is.na function would help you uncover those values.
If you wanted to determine the husband’s income, you could use quantile, median, sd and var. You can use a bar plot and a histogram to create a visualization of the information to help you see who has the highest income.
Working with data requires a lot of knowledge. Whether you are working for a company performing market research, building Facebook campaigns or compiling household statistics for the U.S. Census Bureau, data analytics has an impact on nearly every industry today. If you’re interested in working with data, attending a data analytics bootcamp may be the right next step for you.