Data is the fuel that powers the modern digital economy. Data volume, velocity, and variety have increased exponentially, especially in the last decade. The total amount of data generated will reach 181 zettabytes in 2025.
Disciplines such as data science and data analytics have become instrumental in leveraging this rampant growth. These two terms, often used interchangeably, have distinct differences. Data science is used to solve complex business issues, while data analytics is used to answer specific predefined business problems.
The data science and analytics markets, in correlation with data, grew aggressively. The data science market was valued at $95.31 billion in 2022, growing at a CAGR of 27.6%. Moreover, the data analytics market was valued at $49.0 billion in 2022 and would reach 202.8 billion in 2028.
Image by Author using Python
This article compares data science and data analytics; we will first define the objectives and lifecycle steps of projects in both fields. Then, we will understand the differences and similarities between the two disciplines.
What is Data Science?
Data science is a broad data discipline in which the practitioner, known as a data scientist, identifies the problem. Moreover, a data scientist uses various tools such as machine learning, statistical modeling, and data visualization. The main aim of a data scientist is to explore and find patterns in preprocessed data and make predictions on unseen data.
What Types of Problems Does Data Science Solve?
Data scientists solve various problems, such as predictive analysis, recommendation systems, anomaly detection, etc., for various industries. For example, data scientists make models that predict stock prices, make personalized recommendation systems for platforms like Amazon and Netflix, and detect fraudulent transactions in banking.
Opportunities for innovation are endless with data science. For example, in a recent Data Science conference held at the Data Science Salon, ML Engineer Chong Dang explained how chatbots coupled with AR/VR technology give rise to Intelligent Virtual Assistants (IVA).
Data Science Process: From Problem Identification to Model Development
Problem Understanding and Definition
The first step in data science is to engage with stakeholders to understand the business context and objectives. Through a detailed understanding of the organizational context, objectives, and pain points, data scientists formulate a well-defined problem statement.
Data Acquisition and Preprocessing
Once the problem is postulated, it is time to identify and collect all the relevant data from various sources. After data acquisition, the data is preprocessed into the form necessary for exploration. Preprocessing can include resolving formatting inconsistencies, imputing missing values, etc.
Exploratory Data Analysis (EDA) and Feature Engineering
Once the data is ready, it is time to dive deep into it to uncover hidden patterns, relationships, and anomalies. Various statistical and visualization tools and libraries are used in this step. Insights obtained in EDA can be used to guide stakeholders about the business strategy. Moreover, these insights are also helpful in feature engineering.
Feature engineering means adding new features or modifying current features to get an enhanced model performance. For example, adding time (dates and month of year) can be a helpful feature in predicting e-commerce sales to capture holiday and special occasion seasonality better.
Model Development, Validation, and Deployment
After feature engineering, the next step is model fitting. This step divides the data into training, validation, and testing datasets. Various ensembling, blending, and stacking techniques are used for an optimized model.
All models are wrong, but some are useful — George Box
Once the model is ready, it's time to deploy it to test it on unseen data. Moving this step forward, the data scientist monitors model performance and, if possible, improves it.
Data Scientist Skills and Tools
Expertise in machine learning, statistics, and big data is mandatory to become a data scientist. Specific tools needed to become a data scientist are as follows:
- Programming Language: Python (preferred) and R are the languages to do data science.
- Data Processing Libraries: NumPy and Pandas libraries; are used for data manipulation and processing.
- Data Visualization Tools: Python libraries, such as Matplotlib and Seaborn, or BI tools, such as Tableau, can be helpful for visualization and EDA.
- Machine Learning Libraries: The most important machine learning library to learn and master is ScikitLearn.
- Deep Learning: Expertise in libraries such as Keras or PyTorch is needed for deep learning tasks such as image recognition.
- Big Data Technologies: Apache Hadoop and Apache Spark are used for handling big data.
What is Data Analytics?
Data analytics, performed by data analysts, involves using statistical methods and business intelligence (BI) tools to analyze and find patterns in historical data to facilitate data-driven decision-making. The core objective of data analytics is to address predefined business problems.
What Types of Problems Does Data Analytics Solve?
Data analytics assists companies in gathering actionable insights from historical data. For example, in sports, analytics can uncover areas of improvement given an athlete’s data (past performance considering various parameters). In e-commerce, data analytics can identify past trends, such as sales seasonality, to help decision-making for future strategies.
Data Analytics Process: From Understanding Business Problems to Actionable Insights
Business Requirement Analysis
In this step, the data analyst engages with stakeholders to understand the business context and the specific problems they are trying to solve. For instance, the specific problem could be increasing the churning rate.
Data Collection and Preparation
Similar to the data science life cycle, the data analyst identifies and collects data from all the data sources in this data analytics step. Converting data to a format that is ready for intended statistical analysis and visualization is called data preparation.
Data Analysis and Interpretation
This step uses statistical analysis and visualizations to explore the data. The objective is to answer the reason for the problem and find opportunities for future strategic decision-making. For example, finding reasons for increasing churn rate and suggesting remedies to overcome that.
Interpretation and Communication of Results
Translating analytical outputs into comprehensible insights is a crucial step. The aim is to effectively communicate insights through visualizations and reports that answer business problems and suggest data-informed strategies.
Data Analyst Skills and Tools
Data cleaning, statistical analysis, data visualization, and effective communication are required for data analytics. The tools needed to become a data analyst are as follows:
- Spreadsheet Tools: These tools allow data manipulation and basic analysis. Popular options are Microsoft Excel or Google Sheets.
- Data Visualization Software: These tools enable data analysts to convert data into visual formats such as graphs or dashboards, making them more accessible and understandable for the stakeholders. Tableau and QlikView are used for this purpose.
- Programming Language: Programming language, mainly Python, is required for complex data manipulation and statistical analysis.
- SQL Databases: These are essential tools for data analysts to retrieve, manipulate, and manage data efficiently from relational databases. Tools include MySQL and PostgreSQL.
Is Data Science the Same as Data Analytics?
No, data science is not the same as data analytics. Salient data analytics and data science differences are as follows:
What is the Difference Between Data Science and Data Analytics
Discovering insights, identifying patterns, and developing prediction models
Analyzing historical data patterns to answer specific questions.
Predictive and prescriptive analytics, forecasting future trends.
Descriptive analytics, summarizing past events and analyzing them for decision-making.
Expertise in machine learning, statistics, and big data
Data cleaning, statistical analysis, data visualization, and effective communication.
1. Python or R
2. Pandas, NumPy
3. Matplotlib, Seaborn
5. Keras or PyTorch
6. Hadoop, Spark
1. Excel, Google Sheets
2. Tableau, QlikView
3. Python, or R
4. MySQL, PostgreSQL
5. SPSS, Stata
Used in product innovations and developing algorithms.
Used in routine business intelligence and answering specific business queries.
Similarities Between Data Science & Data Analytics
- Data-Driven Decision-Making: Both fields rely on data rather than intuition to inform and decision-making.
- Collaboration with Stakeholders: Collaboration with stakeholders is the primary step in data science and data analytics.
- Ethical Considerations: Ensuring that data is used ethically, maintaining data privacy, and making unbiased prediction is emphasized in both disciplines.
- Overlapping Tools: Both data scientists and data analysts can use tools such as SQL databases like MySQL and PostgreSQL and visualization tools like Tableau or Seaborn.
Data Science VS Data Analytics: Which is Better?
“Better” is highly context-dependent, as each field has unique purposes and applications. Data analytics is the right choice for organizations addressing a particular pain point, such as declining e-commerce sales. Data science is the way to go if organizations want to develop new products, such as recommendation systems.
Is Data Analytics Easier than Data Science?
In comparing data analytics vs data science, data analytics is easier considering the complexity. The learning curve is also steeper in data science than in data analytics because of the range of advanced topics and tools needed to become a data scientist.
Data Science vs Data Analytics: Concluding Thoughts
If data is the new oil, then data analysts and data scientists are the drillers and refiners. To summarize, data analytics answers a particular business problem. On the other hand, data scientists extract insights by building and deploying predictive models. Both rely on data, engage with stakeholders, and follow ethical guidelines for using data. An organization must critically review its needs before choosing between a data scientist or a data analyst.