Data Science vs Data Analytics

All over the world, more and more data is being produced every day. But most of that data is unprocessed and raw. Ever wonder how the right information reaches us?

Like any other process, it goes through multiple different steps. Certain tools help us to process and analyze unprocessed data and information. The two main processes are data science and data analytics.

This article takes a closer look at these two disciplines and will help you to understand the similarities and differences between the two.

Data Science Explained

Data science is the concept of studying data and merging statistics, numbers, and domain knowledge together. Data science as a subject or career choice is not limited to one field; it functions as an interdisciplinary field that aims to extract knowledge from unstructured information.

For example, data science can be applied to help organize a poorly structured data model. It provides a framework to process large amounts of information, extract patterns from that data, and provide us with useful insights. In simpler terms, it helps us to find answers and solve problems.

Listed below are a number of terms you’ll often hear used in the field of data science. These terms refer to certain techniques data scientists use to extract or interpret information.

Data Mining: As the term suggests, data mining is about locating certain patterns in data collection. Data sets have patterns, and this process helps with recognizing them and transforming the given information into a comprehensible structure. The two most important steps involved in data mining are:

1. Creating a database
2. Data management

Deep Learning: The word “deep” here indicates that the process includes multiple layers. The process goes through different layers to extract complex features in the data. For example, in computer science, deep learning uses layers to identify the edges of an image as it is being processed.

Big Data: Big data refers to data that is otherwise difficult to handle with common software programs and packages.

Date Analytics Simplified: Data analysis is a process that predominantly focuses on scrutinizing, transforming, and cleaning existing data. This unorganized data is transformed into organized datasets useful for decision-making.

The process of data analysis goes through certain steps which include:

1. Data requirement
2. Data collection
3. Data processing
4. Data cleansing
5. Communication

Data requirement: The requirement of the data is the basis of the analysis. It can be found in numbers (numerical) or categories, so the data can be of any type.

Data collection: Generating data starts with the process of data collection. Data collection can be done through face to face interviews or online.

Data processing: The data collected is then further processed using statistical or other software.

Data cleansing: The collected and processed data needs to be revisited to ensure it’s free of errors and duplication. Data cleansing is the process of identifying and correcting inaccurate data. For example, textual data spell checkers are used to identify misspelled words. Data cleansing is also used in financial information to determine inaccurate numbers.

Communication: Now that the data is analyzed, the next important step is the representation of data. This tends to be an ongoing process of delivering the data, obtaining feedback, then using that feedback to further analyze the data.

What’s the Difference between Data Science and Data Analytics?

The two terms are often used interchangeably, however the core difference is that data science encompasses all of the tactics used to organize large datasets, while data analytics is a more focused process of handling and scrutinizing data. Additionally, data science is concerned with exploring data on a macro level to uncover insights, whereas data analysis is comparatively more focused and a little less broad.

Data analysis deals with discovering answers to specific questions, often termed as additional analysis.

Data Science:

• Broad approach
• Aims to ask questions
• Uses a multi-layered approach to provide data

Data Analytics:

• Focused approach
• Aims to find actionable data
• Collects, cleanses, and communicates data

You can see why it’s easy to confuse the two terms. It’s also now generally accepted that the two can’t be disconnected from each other. They’re essentially interconnected and are two sides of the same coin.

Data Science and Data Analytics as a Career Choice

A company’s use of data is critical to its success and at the heart of decision-making. However, according to research from Forrester’s, companies use only about 12% of the existing data.

How to Begin a Career in Data Science

The best way to begin a career in data science is to have a strong background in math, computer science, statistics, engineering, and programming languages such as Python. To pursue a career in data science, you’ll need to be well versed in the following platforms:

• Hadoop: If you are a newbie, Hadoop offers courses for data exploration or data sampling, used in research methodologies as well.
• Apache Spark: It helps with managing massive amounts of data and sampling using the MapReduce programming model.    
• Machine Learning: Most universities offer courses on machine learning and computer vision.
• Data Visualization: This will help you create a graphical representation of the complex data, which would further help to solve business problems using new tools such as PowerBI.

How to Begin a Career in Data Analysis

Like data scientists, data analysts are generally in high demand. Various industries need data analysts, for example large investment banks and private equity firms that need analysis of industry trends before making investments. To pursue a career in data analytics, you should consider enrolling in a computer science degree, as well as a management degree. Data analytics can be studied at undergraduate, postgraduate, and doctorate levels. You’ll need to become familiar with:

• Querying language
• Statistical language
• Excel
• Scripting language

Data analysts can earn excellent salaries, however they vary depending on your particular specialization. Below is a list of job titles in the field of data analytics and an estimate of their typical annual salaries.

• IT System Analyst - $85,599
• Healthcare Data Analyst - $65,033
• Operations Analyst - $61,384
• Quantitative Analyst - $85,042
• Data Analytics Consultant - $84,351
• Digital Marketing Manager - $68,059
• Transportation Logistics Specialist - $61,363

Additional Skills for Data Science and Data Analytics

Most of us understand that data science and data analytics come under the broad umbrella of scientific professions. However, there’s also a significant amount of human interaction that goes into these roles.

Consider, for example, the endeavor of training software to emulate human skills. An expert data scientist or data analyst will actually benefit from having a keen understanding of human psychology and sociology in order to understand how people interact with software. Along with courses like math and computing, you could also benefit from taking a short course in communication, sociology, or psychology.

Next Steps

If you’d like to get more information about how to get hired as a data pro, Thinkful’s blog covers both data science and data analytics for those early on in their career change.

If you’re relatively new to the world of data, you can take advantage of a fast-tracked, full-time Data Analytics curriculum to learn the skills employers are looking for. Or, try our part-time Data Analytics program for a more flexible option that works around your schedule.