Data Science vs Data Analytics
Learn what are the key differences. This guide provides definitions, examples and practical advice to help you understand the differences between analytics vs data science.
Data science and data analytics are closely related but there are key differences. While both fields involve working with data to gain insights, data science often involves using data to build models that can predict future outcomes, while data analytics tends to focus more on analyzing past data to inform decisions in the present.
Data science is a broad field that encompasses data analytics and includes other areas such as data engineering and machine learning. Data scientists use statistical and computational methods to extract insights from data, build predictive models, and develop new algorithms. Data analytics involves analyzing data to gain insights and inform business decisions.
What Is Data Science?
Data Science is the application of tools, processes, and techniques such as programming, statistics, machine learning and algorithms towards combining, preparing and examining large datasets. The datasets are often a mix of structured and unstructured data.
The goal of data science is often to identify patterns and develop actionable insights but it can also be to produce broad insights by asking questions, finding the right questions to ask and identifying areas to study. Other examples of data science deliverables include recommendations based on collaborative filtering, predictions and forecasts based on prior activity, segmentation based on defining attributes, fraud detection based on identifying anomalies and automated decision making based on model parameters.
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Data Science Process
If you’re considering a career as a data scientist, and are wondering, “What does a data scientist do?”, here are the six main steps in the data science process:
- Goal definition. The data scientist works with business stakeholders to define goals and objectives for the analysis. These goals can be defined specifically, such as optimizing an advertising campaign or broadly, such as improving overall production efficiency.
- Data collection. If systems are not already in place to collect and store source data, the data scientist establishes a systematic process to do so.
- Data integration & management. The data scientist applies best practices of data integration to transform raw data into clean information that’s ready for analysis. The data integration and management process involves data replication, ingestion and transformation to combine different types of data into standardized formats which are then stored in a repository such as a data lake or data warehouse.
- Data investigation & exploration. In this step, the data scientist performs an initial investigation of the data and exploratory data analysis. This investigation and exploration is typically performed using a data analytics platform or business intelligence tool.
- Model development. Based on the business objective and the data exploration, the data scientist chooses one or more potential analytical models and algorithms and then builds these models using languages such as SQL, R or Python and applying data science techniques, such as AutoML, machine learning, statistical modeling, and artificial intelligence. The models are then “trained” via iterative testing until they operate as required.
- Model deployment and presentation. Once the model or models have been selected and refined, they are run using the available data to produce insights. These insights are then shared with all stakeholders using sophisticated data visualization and dashboards. Based on feedback from stakeholders, the data scientist makes any necessary adjustments in the model.
Data Scientist Skills and Tools
Data Scientist Role
Given the pace of change and the volume of data at hand in today’s business world, data scientists play a critical role in helping an organization achieve its goals. A modern data scientist is expected to do the following:
- Design and maintain data integration systems and data repositories.
- Work with business stakeholders to develop data governance policies and to improve data integration and management processes and systems.
- Fully understand their company or organization and its place in the market.
- Use BI or data analytics tools to investigate & explore large sets of structured and unstructured data.
- Build analytical models and algorithms using languages such as SQL, R or Python and applying data science techniques such as machine learning, statistical modeling, and artificial intelligence.
- Test, run and refine these models within a prescriptive analytics or decision support system to produce the desired business insights.
- Effectively communicate trends, patterns, predictions and insights with all stakeholders using verbal communication, written reports and data visualization.
Data Scientist Skills.
The ideal data scientist is be able to solve highly complex problems because they are able to do the following:
- Help define objectives and interpret results based on business domain expertise
- Manage and optimize the organization’s data infrastructure
- Utilize relevant programming languages, statistical techniques and software tools
- Have the curiosity to explore and spot trends and patterns in data
- Communicate and collaborate effectively across and organization
The below Venn diagram, adapted from Stephan Kolassa, shows how a data science consultant (in the heart of the diagram) must combine their skills in communication, statistics and programming with a deep understanding of the business.
With modern tools, data science is increasingly overlapping with analytics. Seen as one of the top 10 BI and data trends this year, this will enable citizen data scientists to do more.
What Is Data Analytics?
Like data science, data analytics is the use of tools and processes to combine and examine datasets to identify patterns and develop actionable insights. And, like data science, the goal is to help organizations make better, data-driven decisions. The key difference is that for data analytics, the focus is typically much more on answering specific questions than open exploration.
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Data Analytics Process
The primary steps in the data analytics process involve defining requirements, integrating and managing the data, analyzing the data and sharing the insights.
- Project Requirements & Data Collection. Determine which question(s) you seek to answer and ensure that you have collected the source data you need.
- Data Integration & Management: Transform raw data into clean, business ready information. This step includes data replication and ingestion to combine different types of data into standardized formats which are stored in a repository such as a data warehouse or data lake and governed by a set of specific rules.
- Data Analysis, Collaboration and Sharing. Explore your data and collaborate with others to develop insights using data analytics software. Then share your findings across the organization in the form of compelling interactive dashboards and reports. Some modern tools offer self-service analytics, which enables any user to analyze data without writing code, and offer conversational analytics which lets you use natural language to explore data. These capabilities increase data literacy so that more users can work with and get value from their data.
Here are three key aspects of modern data analysis:
Augmented analytics.
Today, a data analyst or business user’s intuition can be enhanced by AI and machine learning. Augmented analytics suggests insights and analyses for humans to perform, automates tasks and allows for search & natural language interaction.
Data visualization, reports and dashboards.
Stakeholders have an easier time understanding and collaborating on data when it’s visualized in the form of charts, graphs and maps. Interactive digital dashboards and reports let you share your findings and allow all users to easily interact with the data.
Embedded analytics.
Embedding data analysis capabilities within other applications, products, portals, workflows or processes helps people find insights and make better decisions faster. This is because rather than switching to a separate analytics application, all stakeholders can quickly access data and insights right within their workflows.
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- Combine data from all your sources
- Dig into KPI visualizations and dashboards
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Data Analyst Role and Skills Needed
If you’re considering a career in data analytics, you may be wondering, “What does a data analyst do?” Even with modern self-service data analytics tools as described above, there’s still a critical role for the data analyst in many organizations. Here is an overview of the role and the skills you’ll need.
Data Analyst Role
Today’s data analyst is expected to do the following:
- Design and maintain data integration systems and data repositories.
- Work with the IT team to develop data governance policies and to improve data integration and management processes and systems.
- Understand their company or organization and its place in context of external and competitive trends.
- Use a data analytics or BI tool to build apps and perform analyses, create dashboards and visualizations, and dive deep into the data to find relationships and insights.
- In the absence of a full-featured analytics or BI platform, use statistical tools to analyze data sets and find insights.
- Prepare dashboards and KPI reports for stakeholders that use data to effectively communicate trends, patterns, and predictions.
Data Analyst Skills.
In terms of the skills you’ll need, the ideal data analyst is able to effectively collaborate and communicate with all stakeholders in addition to having the necessary technical expertise. Business skills include helping define goals and providing KPI examples. Technical expertise includes skills in data integration and management, data modeling, R or SAS, SQL programming, statistical analysis, reporting, and data analysis. These skills typically come from a background in mathematics and statistics and sometimes includes a master’s in analytics.
Data Science vs Data Analytics
Data science and data analytics are closely related but there are key differences between the two fields.
While both fields involve working with data to gain insights, data analytics tends to focus more on analyzing past data to inform decisions in the present, while data science often involves using data to build models that can predict future outcomes.
Data science is a broad field that encompasses data analytics and includes other areas such as data engineering and machine learning. Data scientists use statistical and computational methods to extract insights from data, build predictive models, and develop new algorithms. Data analytics involves analyzing data to gain insights and inform business decisions.
Let’s ground our review of data science vs data analytics by revisiting the core definitions and processes:
- Data Science is the application of tools, processes, and techniques towards combining, preparing and examining large datasets and then using programming, statistics, machine learning and algorithms to design and build new data models.
- Data analytics is the use of tools and processes to combine, prepare and analyze datasets to identify patterns and develop actionable insights.
The main difference in data science vs data analytics is highlighted in bold in the first process diagram: data science involves data models.
The goal of both data science and data analytics is often to identify patterns and develop actionable insights. But data science can also seek to produce broad insights by asking questions, finding the right questions to ask and identifying areas to study.
Here’s an outline of the key differences of data science vs data analytics:
Purpose
| Data Scientists produces both broad insights by exploring the data and actionable insights that answer specific questions. | Data analytics is more focused on producing insights to answer specific questions and which can be put into action. |
Scope and Skills
| Data Scientists is a multidisciplinary field including data engineering, computer science, statistics, machine learning, and predictive analytics in addition to presentation of findings. | Data analytics is a broad field which includes data integration, data analysis and data presentation. |
Approach
| Data Scientists prepare, manage and explore large data sets and then develop custom analytical models and algorithms to produce the required business insights. They also communicate and collaborate with stakeholders to define project goals and share findings. | Data analysts prepare, manage and analyze well-defined datasets to identify trends and create visual presentations to help organizations make better, data-driven decisions. |
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