Data Science and Data Analytics are the pillars of the data industry. Today, the whole world is dealing with massive data. According to the World Economic Forum, the daily global data generation may reach 44 zettabytes. And by 2025, this will touch the 463 exabytes mark.
These data include text, emails, tweets, user searches on search engines, social media chatter. Everything we do online fall under this category. Both the Data Science and Data Analytics work with big data, but there are some differences. Hence, today, we will discuss these differences in this article.
What is Data Science?
Before we go through the differences, you should know something regarding Data Science and Data Analytics. So, at first, let’s start with Data Science.
Data Science is a multidisciplinary field related to data mining, machine learning, and big data handling. It uses scientific methods like statistics and data analysis, to extract the basics from both structured and unstructured data.
What is Data Analytics?
The process of analyzing raw data to draw a conclusion and get valuable insight is known as Data Analytics.
Differences Between Data Science and Data Analytics
Now let’s discuss the main topic. We have chosen some topics to explain the differences.
1. Main Focus and Pattern of Work
Data Science deals with designing and constructing new processes for data modeling and production. However, the main goal of Data Science is to ask the right question. Data scientists also uses heavy coding to perform the task.
This field examines data sets of businesses to identify trends. The aim of Data Analytics is to find a way of growth for businesses. By examining the given data, data analysts also develop charts and create visual presentations. Therefore, the difference is, it does not include complex coding.
2. Typical Background
According to Drew Conway, a Data Science expert, data scientists should know mathematics, statistics, and ethical hacking. Also, they should have substantive expertise. They should also have well-depth knowledge of computer Science. A Master’s in Data Science is the degree which a data scientist should have.
On the other hand, data analysts should also know mathematics and statistics. You can also be a data analyst if you are not from a statistical background. You just have to learn some tools needed to make decisions with the number. However, a Master’s in Data Analytics is a higher degree in this field.
Here we will discuss elaborately the responsibilities related to Data Science and Data Analytics. Therefore, you will understand all the facts elaborately.
- Processing, cleaning, and validation of the data.
- Perform exploratory data analysis on large datasets.
- Perform data mining with ETL pipelines.
- Writing code for automation and built resourceful libraries for machine learning.
- To glean business insights using machine learning tools and algorithms.
- To identify new trends in data for making business predictions.
- Collection and interpretation of data.
- To identify relevant patterns in a dataset.
- Perform data querying using SQL.
- To experiment with different analytical tools like predictive Analytics, Prescriptive Analytics, and diagnostic Analytics.
- To use data visualization tools like Tableau.
- Core skill
4. Core Skill
As we have mentioned earlier, data Science deals with complex coding. That’s why data scientists should also be experts in mathematics, statistics, and programming languages (Python, R, SQL). Data Analytics deals with data mining, data modeling, data warehousing, and database management. Let’s talk in detail regarding the skills needed to get success in those fields.
A data scientist should have a deep knowledge of Statistics, Multivariate Calculus, and Linear Algebra. Data scientists should also be experts in handling programming languages like R, Python, Java, Scala, Julia, and SQL. Also, most importantly, data scientists should have experience in database management and handling Big Data platforms.
To go with Data Analytics, one should know R or Python programming. A data analyst should be well-versed in Excel and SQL databases. Additionally, the data analyst should know data visualization.
5. Major Field of Application
Data Science also has a wide-spread field of application. It includes Machine learning, artificial intelligence, Search engine engineering, and corporate Analytics.
It works with relatively small fields like healthcare, gaming, travel, etc. These are the industries that have immediate data need.
As you have understood, Data Science and Data Analytics are close to each other. They are the two sides of the same coin. However, Data Science is a broad field, having lots of opportunities. Whereas the working field of Analytics is somehow less. Hence, the profession has a high demand. So, it is completely on the choice of the individual.