What is Data Analytics? cover image

What is Data Analytics?

Jonathan Barrios โ€ข December 3, 2021

data-analytics data-science machine-learning

Perhaps you've interested in becoming a data analyst, or maybe you want to learn more about data and what the hype is all about. Either way, this post is for you!

In this post, you'll find an introduction to data analyticsโ€”starting with a simple, easy-to-understand definition and working up to some of the essential tools and techniques.

By the end of this post, you'll have a clear idea of what data analytics is, how it differs from data science, what machine learning is, and what it takes to break into the field. Here are some of the questions you will find answers to in this post:

What is data analytics?

For most companies, data is super valuable, and they go to great lengths to collect all kinds of dataโ€”but raw and unrefined data is not very valuable. The value is created once the raw data is converted into information. Data analytics is the process of extracting, manipulating, and analyzing data to create meaningful and actionable information. For example, a company could have hundreds of thousands of numbers showing the amount of time users spend browsing their website. Data in this form is not very valuable, but data analysts unlock its value using their data skills. To realize the value of their findings, a data analyst needs to clean, analyze, and present the data insights to key team members.

Individuals interested in the data analysis are called stakeholders. Stakeholders can be as diverse as product managers, marketing managers, data architects, business intelligence teams, even CEOs.

The following steps sum up the data analysis process:

What is the difference between data analytics and data science?

You may have noticed that the terms "data science" and "data analytics" tend to be used interchangeably. However, they are two different fields with two distinct career paths. More importantly, they impact the business or organization in different ways.

A Data Analyst is more concerned with answering questions about problems already known to the business. For example, identifying trends or patterns to answer questions about the length of time users spend browsing the company website. To do this, data analysts analyze large datasets and visualize their findings using charts, graphs, and dashboards.

Data Scientists, in contrast, consider what questions the business should be asking using data models, algorithms, and predictive models. For example, data scientists might build a machine to monitor and test data continuously, and as new patterns and trends emerge, improve and optimize that machine wherever possible. Building machines is a big part of the data science toolkit. The use of the word machine is a reference to machine learning.

What is machine learning?

In short, machine learning is a collection of techniques that use algorithms to automate analytical model building. Data scientists use these models to learn from the data, identify patterns and make decisions with minimal help from humans.

Both "data science" and "machine learning" are popular buzzwords and tend to be used interchangeably. However, even though data science includes machine learning, it is a vast field with many different tools.

What skills do you need to get started as a data analyst?

To get started as a data analyst, you may need to learn programming languages such as SQL, Python, or R. Some data analysts use analytics software, data visualization software, and data management programs without programming languages. For example, Python or R is standard for modern tech companies using a programmatic approach. At the same time, many businesses use Excel and other software such as Tableau or PowerBI, which use graphical user interfaces(GUIs). As a result, it is not uncommon for data analysts to know how to use programming languages and GUIs when performing data analysis.

Data Cleaning

Regardless of the tools, data analysts will need to become skillful at cleaning and preparing data. Most of the time spent as a data analyst will involve cleaning, manipulating, and preparing data in one way or another. Without this skill, the data will not become information, and thus the data analyst will fail to unlock the value hidden inside the raw and unprocessed data. Therefore, data cleaning is paramount to data analysts and data scientists.

Data Exploration and Analysis

After cleaning and preparing, data analysts perform exploratory data analysis to identify initial trends and characteristics. Data exploration can lead to refining the hypotheses as needed. Again, a basic foundation of probability and statistics will help guide your exploratory data analysis.

Keep in mind, the exact level of statistical knowledge required will significantly depend on the demands of your particular role and the data at hand. Nevertheless, it is common for data analysts to work with probability distributions and statistics to carry out hypothesis testing.

The type of data analysis you use depends on your goal. Generally speaking, data analysis will fit into one of the following:

Data Visualization

Visualizing data is a powerful skill that allows data analysts to present trends and patterns hidden inside a giant spreadsheet or large datasets. As a data analyst, you'll need to create graphs, plots, and charts to communicate your findings visually. Creating clean, visually compelling charts will help others understand the data quickly and without much effort.

Visualizations play a significant role when exploring data. For example, data can hide trends or patterns when looking at countless numbers. On the other hand, graphs, plots, and charts allow you to visualize trends or patterns hidden in large amounts of data.

Dashboards and/or Reports

As a data analyst, you'll need to present your insights to key stakeholders in your organization so they can make critical decisions based on your findings. Combining visualizations with dashboards and reports gives your organization access to trends and patterns hidden in the data by removing technical barriers.

Dashboards and reports might take the form of a PowerPoint presentation. For example, presenting simple charts with a date filter, all the way to complex visualizations changing over time, with critical events marked in chronological order.

Human Development Cycle
Image credit: Eleanor Lutz

Thank you for reading my blog post, and I hope you found it helpful. I leave you with this quote:

"The goal is to turn data into information, and information into insight." โ€“ Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.

Follow me on Twitter @\ai_data_science for more on Data Analytics, Data Science, and Machine Learning. As always, happy analyzing! ๐Ÿ‘‹๐Ÿผ