In today’s rapidly evolving digital landscape, the ability to extract value from data has become critical for businesses across all industries. Two disciplines often stand at the forefront of this data revolution: data science and data analytics. While these fields may appear similar at first glance, they differ significantly in their scope, methodology, and required skill sets. Understanding these differences is essential when planning your career path or developing your organization’s data strategy.
What Is Data Science?
Data science is an interdisciplinary field that uses structured and unstructured data to make future predictions, identify meaningful patterns, and optimize decision-making processes.
Data science combines mathematics, statistics, programming, domain expertise, and data engineering. Its primary goal is to solve complex problems and create value from data. Data scientists typically seek answers to questions like “what will happen?” and “why will it happen?”
According to Gartner, data science is “the discipline that uses predictive analytics, machine learning algorithms, and statistical modeling to uncover hidden values in data sets.” The outputs of data science projects are generally automated systems, predictive models, or strategic insights.
Data Science Process
Data science projects typically follow these steps:
- Problem Definition: Clearly identifying the business problem that needs to be solved. This stage defines the project’s objectives and success criteria.
- Data Collection: Determining and gathering the data necessary to solve the problem. This data can be structured or unstructured and may come from various sources.
- Data Cleaning and Preparation: Transforming raw data into an analyzable format. This step includes filling in missing data, detecting outliers, and performing data transformations.
- Exploratory Data Analysis: Preliminary analysis to understand patterns, relationships, and trends in the data.
- Model Development: Selecting, training, and evaluating machine learning algorithms. In this step, the most appropriate model for the data is chosen.
- Model Optimization: Applying techniques such as hyperparameter optimization and feature engineering to improve model performance.
- Interpretation of Results: Evaluating the model’s outputs from a business perspective and extracting meaningful insights.
- Implementation and Monitoring: Deploying the model to a production environment, monitoring its performance, and updating it when necessary.
According to McKinsey’s research, for data science projects to be successful, the process must be iterative and closely aligned with business objectives.
Data Scientist Skills and Tools
An effective data scientist should master the following skills and tools:
Technical Skills:
- Programming Languages: Python, R, Scala
- Statistics and Mathematics: Regression analysis, probability theory, linear algebra
- Machine Learning: Unsupervised and supervised learning algorithms, deep learning
- Big Data Technologies: Hadoop, Spark, Hive
- Database Systems: SQL, NoSQL
- Data Visualization: Qlik, Matplotlib, ggplot2,
Business/Soft Skills:
- Analytical problem-solving
- Communication and presentation skills
- Business understanding and domain knowledge
- Project management
- Curiosity and continuous learning desire
Common Tools:
- Jupyter Notebook, RStudio
- TensorFlow, PyTorch, scikit-learn
- Apache Hadoop, Spark
- PostgreSQL, MongoDB
- Qlik
- Git, Docker
What Is Data Analytics?
Data analytics is the process of examining raw data to discover meaningful patterns and understand the current situation. Data analytics typically seeks to answer questions like “what happened?” and “why did it happen?”
Data analytics takes a more retrospective approach, using existing data to evaluate company performance, identify trends, and improve operational efficiency. Data analysts provide insights that support business decisions and transform data into understandable reports.
Forrester defines data analytics as “the systematic examination, cleansing, transformation, and modeling of data to support business decisions.”
Data Analytics Process
The data analytics process typically includes these steps:
- Question Formulation: Determining the business question or analysis need. This step defines the focus of the analysis.
- Data Collection: Identifying relevant data sources and obtaining the data. This data usually comes from structured databases, Excel files, or CSV files.
- Data Cleaning: Filling in missing data, detecting outliers, and bringing data into a consistent format.
- Data Analysis: Exploring the data, calculating descriptive statistics, and examining relationships. This step often involves querying, filtering, and pivoting operations.
- Data Interpretation: Making sense of the analysis results and evaluating them from a business perspective.
- Communication of Results: Reporting, visualizing, and presenting findings to relevant stakeholders.
According to Qlik’s approach, effective data analytics should be a continuous cycle that directly influences business decisions.
Here are four key aspects of modern data analysis:
In today’s business environment, data analytics is divided into four fundamental areas that provide different perspectives:
- Descriptive Analytics: Answers the question “what happened?” It uses historical data to understand the current situation. Examples include sales reports, website traffic statistics, or financial performance indicators.
- Diagnostic Analytics: Focuses on “why did it happen?” It examines in depth the causes of a specific event or trend. Examples include analyzing the reasons for a decline in sales or understanding the reasons for the failure of a marketing campaign.
- Predictive Analytics: Seeks to answer “what will happen?” It uses historical data to predict future events. Examples include future sales forecasts, customer behavior predictions, or risk assessments.
- Prescriptive Analytics: Focuses on “what should we do?” It suggests steps to take to achieve the best outcome. Examples include optimal resource allocation strategy, optimal pricing, or the most effective marketing channels.
In Gartner’s analytics value chain model, these four types of analytics form a hierarchy from basic to advanced. Data analysts typically focus on descriptive and diagnostic analysis, while data scientists tend toward predictive and prescriptive analysis.
Data Analyst Role and Skills Needed
Data analysts are professionals who answer data-related questions from different departments in organizations and support decision-making processes. An effective data analyst should possess the following skills:
Technical Skills:
- Data Querying: SQL, Excel, Access
- Data Visualization: Qlik, Excel charts
- Basic Statistics: Mean, median, standard deviation, correlation
- Data Manipulation: Pivot tables, filtering, sorting
- Basic Programming: Python, R (basic level)
Business/Soft Skills:
- Analytical thinking
- Effective communication
- Problem-solving
- Attention to detail
- Data storytelling ability
Common Tools:
- Microsoft Excel, Google Sheets
- SQL Server, MySQL, Oracle
- Qlik Sense
- SPSS, SAS
- Jira, Confluence
According to IDC’s research, data analysts spend approximately 80% of their time on data preparation and cleaning operations, and 20% on analysis and reporting.
Data Science vs Data Analytics
We can summarize the key differences between data science and data analytics as follows:
Purpose and Focus:
- Data Analytics: Understanding the current situation, evaluating past performance, and supporting operational decisions.
- Data Science: Making future predictions, discovering patterns, and supporting strategic decisions.
Data Types:
- Data Analytics: Typically works with structured data (databases, Excel files).
- Data Science: Uses both structured and unstructured data (text, images, audio).
Methodology:
- Data Analytics: Focuses on descriptive and diagnostic analysis.
- Data Science: Concentrates on predictive and prescriptive analysis.
Skill Set:
- Data Analytics: Data querying, reporting, and visualization skills are important.
- Data Science: Requires advanced programming, machine learning, and statistical modeling skills.
Outputs:
- Data Analytics: Reports, dashboards, visualizations, insights.
- Data Science: Predictive models, automated systems, algorithms.
Time Horizon:
- Data Analytics: Typically focuses on the past and present.
- Data Science: Creates predictions and forecasts for the future.
Complexity:
- Data Analytics: Deals with more structured and defined problems.
- Data Science: Tries to solve complex, unstructured, and ambiguous problems.
According to Forrester’s 2023 report, the boundaries between data analytics and data science are becoming increasingly blurred with the advancement of technology and the proliferation of self-service tools. Many organizations have begun to develop holistic data strategies that encompass both disciplines.
Data science and data analytics are two complementary disciplines. Businesses need data analytics to understand the current situation, and data science to shape the future. Both fields play a critical role in organizations’ data-driven decision-making processes.
When planning your career or creating your organization’s data strategy, considering the differences and strengths of these two fields will help you direct the right resources to the right areas. Is data science or data analytics the right choice for you? The answer to this question will vary depending on your interests, skills, and career goals.
Understanding the differences between these two disciplines is important to develop your data strategy, close the talent gap in your team, or plan the next step in your career. A data team equipped with the right talent and tools will lead your organization to success in its digital transformation journey.
Resources
- Gartner, “Analytics Value Chain, 2023”
- McKinsey, “The Age of Analytics: Competing in a Data-Driven World”
- Qlik, “Data Science vs Data Analytics”