Data Science vs Data Analytics: What’s the Differences?

There is often a misunderstanding regarding the difference between data science vs data analytics. However, they are quite different in how they work and what they want to accomplish. 

Data science is the branch of science that focuses on deeply analyzing data through statistical analysis and programming techniques to gain in-depth insight into data.

In contrast, data analytics focuses more on understanding and interpreting data to make better decisions in a business or organization. 

To avoid misunderstandings, let's look at the differences between data science and data analytics in the following article!

What is Data Science? 

Data science uses tools, processes, and techniques like programming, statistics, machine learning, and algorithms to combine, prepare, and analyze big data. 

These databases frequently include a combination of unstructured and structured data. Data science aims to find patterns and develop information that can be used as consideration for decision-making.

You will use many different tools and methods in data science to comprehend and explore the possibilities of big data.

What is Data Analytics?

Data analytics evaluates data sets using certain tools or techniques to identify patterns and develop information before taking certain actions.

Just like data science, data analytics aims to help companies make accurate and data-driven decisions. The main difference is that data analytics is usually used to find which answers specific questions. 

In data analytics, you will use techniques and tools to explore data to find answers to pre-defined questions.

Data Science vs Data Analytics 

Once you've learned the fundamentals of data science vs data analytics, let's dig into the differences between the two. 

Here are the six key differences between data science vs data analysis, along with full explanations.

1. Scope and Purpose

Although they look similar, data science vs data analytics differ in scope and purpose. Data science involves more than just data analysis. It also includes predictive modeling, machine learning, and statistical analysis. 

The goal is to use available data to find information, predict patterns, and move decision-making forward.

However, data analytics analyzes historical data to offer insights before direct action. This process includes descriptive analysis, summarizing previous events, and explaining why they occurred. 

2. Process

Data science vs data analytics differ in their focus and processes. In data science, the process begins after initial investigation and data analysis. 

The analysis uses various techniques, including machine learning, statistics, and AI. Here's a detailed explanation of the process:

• Defining Purpose: The first step in the data science process is to collaborate with business stakeholders to define the objectives and goals of the analysis.
• Data Collection: Data scientists ensure that data collection and storage go smoothly.
• Data Integration and Management: Data scientists use best practices for data integration to transform raw data into clean, analytically ready information.
• Data Investigation and Exploration: Data scientists use data analysis platforms or business tools to conduct preliminary data investigations and exploratory analyses.
• Model Development: Selecting analytical models and algorithms with programming languages like SQL, R, or Python.
• Model Implementation and Presentation: The analysis results are shared with all stakeholders through data visualization.

On the other hand, data analytics is primarily concerned with using analytical software for data exploration, teamwork, and discussion. 

Although some analyses are being conducted, the primary focus is on analysis to better understand the data. Here is a more detailed explanation of the data analytics process.

• Data Collection Requirements: Specify the questions to be answered and ensure that all necessary data has been collected.
• Data Integration and Management: Transform raw data into clean, usable business information. This process entails replicating and ingesting data to combine various data types into standard formats stored in repositories such as data warehouses or data lakes.
• Data Analysis, Collaboration, and Discussion: Using data analysis software, you can explore data and collaborate with others to create information. 

3. Role

Data science and analytics play a critical role in today's business world. They do, however, serve distinct functions or levers. Here is the distinction between data science vs data analytics.

Data science is more concerned with using machine learning, statistical modeling, and artificial intelligence techniques to extract useful information from data. 

Data scientists are also responsible for developing complex analytical models and algorithms to forecast trends, patterns, and behaviors underlying data. 

Data analytics can help stakeholders make data-driven business decisions by compiling dashboards and KPI reports.

4. Skills 

The following are the skills that distinguish data science from data analytics:

Data scientists have advanced programming skills, including machine learning and complex statistics. A data scientist must also be able to perform advanced programming tasks. 

Instead, data analysts typically use processing skills, statistical analysis, and data visualization to generate information. 

Furthermore, data analysts are familiar with data manipulation skills such as SQL and Excel, basic statistical analytics, and the ability to communicate analysis results to stakeholders.

5. Salary

The salary between data science vs data analytics roles can be important when pursuing a career in data analysis. Here are the income ranges for careers in data science vs data analytics: 

• Junior Level: The salary range for a junior-level data science vs data analytics specialist is between Rp8,000,000 and Rp15,000,000.
• Middle Level: Reach approximately Rp15,000,000 - Rp25,000,000, allowing you to deepen your skills in particular fields, such as pharmaceutical or FMCG.
• Senior Level: Between Rp25,000,000 - Rp35,000,000. Technical and soft skills such as leadership, teamwork, and negotiation are important at this level.

6. Career Growth

Overall, the gap in career progression between data science vs data analytics reflects the respective fields' focus and responsibilities.

Generally, data analysts focus on using their results to help business decision-making and delve deeper into data analysis. 

Meanwhile, data scientists often research and develop complex models to extract valuable information from data. 

While there are some similarities in roles and responsibilities, data science vs. data analytics provides excellent career opportunities according to your interests, skills, and aspirations. 

This is a complete explanation of data science vs data analytics. Data science and data analytics are very useful for turning data into information and valuable assets for companies in various industries.

Aside from data processing, another valuable asset that is equally important is the ability to market products both digitally and organically. 

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