What is the difference between Data Science and Data Analytics?

With connected devices reaching a worldwide total of 17.8 billion, our digital world generates around 328.77 million terabytes of data each day. Such big data is invaluable for organizations, and any insights demand data science and analytics. More often than not, they confuse the two and consider them similar fields. However, many areas of working procedures are overlapping between them, but the core mandates and roles are entirely different.

Table of Content

• What is Data Science?
• What is Data Analytics?
• Differences Between Data Science and Data Analytics
• Data Science vs. Data Analytics: Which One is Right For You?
• Data Science vs. Data Analytics: Future Prospects
• Conclusion
• FAQs: Data Science vs. Data Analytics

What is Data Science?

Have you ever wondered how Netflix recommends movies that often resonate with what you like? How does Google Maps predict the exact duration required to reach a particular destination? Well, it’s all thanks to Data Science.

Data science is the art of using statistical concepts, programming, and machine learning algorithms to solve modern-day business problems using vast amounts of data. Every data science process starts with questions – What occurred, Why it happened, What will happen further, and how the configured results could be used for the advantage of a business. 

According to the process, it is recognized that Data Scientists are the kind of support businesses need most. They often begin with techniques like scraping to get information for example from databases or from the web. Once they have the data in their possession they evaluate it with a view of finding out some of the most informative characteristics. They then employ machine learning techniques to develop models that, using the data, may predict what may happen in the future. However, that is not all that they offer; On this, Lehnartz et al.’s work greatly adds to this discourse. They also solve many issues for instance fraud, in data sentiment interpretation. Here’s what a data science process fundamentally looks like:

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What is Data Analytics?

While data science tries to solve business problems, data analytics tries to uncover critical insights by churning the data to help businesses make winning and successful business decisions.

To understand the problem, data analysis involves looking at both numbers (quantitative data, such as profits) and descriptions (qualitative data, such as surveys). For example, a company may utilize sales data to guide choices and feedback from consumers to improve products or services.

Before analyzing the data, it needs to be preprocessed and prepared. Often raw, data needs transformation to be utilized. Only after cleaning and organizing, the key features of this data are looked over by industry experts. They begin analyzing the data for its patterns and insights using various tools such as R, Tableau, Python, and Power BI. The last step is to design a visualization that can then be presented to the business stakeholders. A data analytics process workflow is depicted in the following image

Differences Between Data Science and Data Analytics

While both Data Science and Data Analytics commonly include working with data to derive insights, there are several key differences:

Here’s an example to help you understand the difference better:

Data science: Predicting what a consumer will likely buy next by looking at their browsing history and previous purchases (similar to a shopping recommendation engine).

Data Analytics: Analyzing previous sales data to figure out which items go well together (e.g., discovering common combinations of peanut butter and jelly).

Data Science vs. Data Analytics: Core Skills

We have gone through multiple job descriptions for Data Scientists and Data Analysts on LinkedIn. Below is the basic skill requirement schema followed by major organizations:

Data Scientist Skills

Source: TCS Job Description LinkedIn

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Data Analyst Skills

Source: Amazon Job Description LinkedIn

Data Science vs. Data Analytics: Tools

Data Science vs. Data Analytics: Process

The following explains how the processes used in data science and data analytics differ from one another:

Data Science Process

1. Web Scraping and Data Collection: There are a range of sources from which the experts get their data. As this is the first step in the Data Science process, it’s all done using Database access, web scraping, and APIs.

2. Data Management: After gathering the Data, experts focus on managing and organizing the data. This includes activities such as data integration, data transformation, data cleaning, and handling missing values.

3. Exploratory Data Analysis (EDA): EDA is that tool which Data Scientists utilize to study data characteristics and find patterns, anomalies, and correlations. This step uses data visualization software and other significant statistical techniques to get insights.

4. Model Development: The data scientists design either a descriptive or predictive model based on machine learning algorithms and statistical techniques. It involves the evaluation, training, and selection of the correct model and improvement of features to make better predictions.

5. Model Deployment: The data scientists design either a descriptive or predictive model based on machine learning algorithms and statistical techniques. It involves the evaluation, training, and selection of the correct model and improvement of features to make better predictions.

Data Analytical Process

1. Data Collection: The very first step of the data analytics process is to collect relevant information from various sources. It may include data extraction from flat files, databases, or APIs. More methods for gathering data include surveys and manual data entry.

2. Data Cleaning: It is basically preparing the data for analysis and cleaning. The job is mainly carried out by data analysts. This usually deals with missing values, removal of duplicates, standardization of formats, and fixing inconsistencies in data.

3. Exploratory Data Analysis and KPI Configuration: In an exploratory data analysis process, data analysts look for patterns, trends, and even correlations in the data but also observe the nature or characteristics of the data set. They also establish certain performance indicators, known as key performance indicators (KPIs), to track a specific metric relevant to an investigation’s goals.

4. Analyzing Data with Power BI and Tableau: Power BI and Tableau are technologies used by data analysts to carry out comprehensive data analysis, creating visualizations and reports and dashboards that can help them create insightful reports. Such dynamic and interactive visualizations can assist in making decisions based on data.

5. Creating Presentations for Communicating Findings to Stakeholders: Data analysts create reports and presentations to effectively communicate their understandings and results to the management teams, customers, and other stakeholders. At this point, the key findings are outlined, illustrations are provided, and useful recommendations grounded on the analysis are made.

Data Science vs. Data Analytics: Which One is Right For You?

Which degree is best for you will depend on your professional and personal goals. If you’re interested in statistical modeling and data processing, a degree in data analytics could be perfect for you. If big data and machine learning are topics that interest you, consider getting a degree in data science.

Regarding salary, both data science and data analytics provide very competitive salaries. According to Glassdoor, the average base pay range for data scientists ranges from  ₹8 lakh to ₹20 lakh. While the average salary for a Data Scientist is ₹12,60,325 in India.

According to Glassdoor, for a Data Analyst, the average base pay ranges from ₹4 lakh to ₹10 lakh. While the average salary of a Data Analyst is ₹6,60,750 in India.

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Data Science vs. Data Analytics: Future Prospects

According to LinkedIn, 122000+ Data Scientist jobs are available in India. Whereas, in the United States, there are 191000+ jobs available.

According to LinkedIn, there are over 1,32,000 Data Analyst jobs available in India. Whereas in the United States, there are over 2,23,000 jobs available.

Conclusion

Data science and Data analytics play equal roles in today’s data-driven environment by giving organizations practical tools for managing the present and planning for the future. You can select the field that best suits your interests and skill set based on whether you’re more interested in studying the past to better the present (data analytics) or in investigating the future (data science). For more such interesting information, check out our Data Science Course.

Frequently Asked Questions (FAQs)