We are part of the digital economy where 17.8 billion devices are now connected to the internet. And the fact that every click we make generates data, the amount of data generation per day has reached 328.77 million terabytes. This massive data, which is commonly referred to as big data, is a goldmine for all organizations. Data science and data analytics practices help businesses mine value out of this data.
Although data science and data analytics are seen as synonymous practices by many data aspirants. Even though some processes are similar, fundamentally the objective of both the practices and the job description differs by a huge degree. Want to learn those differences, keep reading this blog further on Data Science vs. Data Analytics. Below are the topics we will comprehensively discuss throughout this post:
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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.
Based on the process, Data Scientists provide invaluable support to businesses. They usually start by using methods like web scraping to collect data from databases or the internet. After obtaining the data, they analyze it to identify its primary features. After that, they use machine learning methods to create models that, using the data, may forecast future events or patterns. Still, their contributions go beyond prediction. They also deal with a variety of challenges, such as fraud detection and interpreting sentiment in data. 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 data, it needs to be processed and prepared. Data is frequently raw and needs to be transformed to be utilized. After cleaning and organizing the data, industry experts look over its key features. They analyze the data for patterns and insights using tools like R, Tableau, Python, and Power BI. Finally, they create a visualization that can be presented to the business stakeholders. A data analytics process workflow is highlighted in the image below:
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:
| Aspect | Data Analytics | Data Science |
| Focus | Primarily, focuses on analyzing historical data to identify trends and make informed business decisions | Focuses on extracting knowledge and insights from complex and unstructured data to solve complex problems |
| Goal | To provide insights for making operational decisions and improving business processes | To derive actionable insights, build predictive models, and create data-driven strategies |
| Methods | Use descriptive and diagnostic analytics techniques to answer “what happened” and “why it happened” questions | Employs a broader range of techniques, including predictive modeling, machine learning, and advanced statistical methods to answer complex questions and make predictions |
| Skills | Strong proficiency in SQL, Excel, data visualization tools, and basic statistical analysis | Requires a deep understanding of programming languages (Python, R), statistics, machine learning, and domain knowledge |
| Tools | Excel, Tableau, Power BI, Google Analytics, etc. | Python, R, Hadoop, Apache Spark, TensorFlow, scikit-learn, etc. |
| Typical Role | Data Analyst, Business Analyst, and Business Intelligence Analyst | Data Scientist, Machine Learning Engineer, Statistician, and Data Engineer |
| Use Cases | Market research, A/B testing, customer segmentation, reporting, and dashboard creation | Predictive modeling, recommendation systems, natural language processing, computer vision, deep learning, etc. |
| Data Scope | Generally, deals with structured data and predefined metrics | Handles both structured and unstructured data, often dealing with big data and real-time streams |
| Decision-Making | Helps in making informed decisions based on historical data patterns | Provides insights, predictions, and prescriptive recommendations for strategic decision-making |
| Time Frame | Typically, focuses on short to medium-term insights | Involve long-term strategic planning and forecasting |
| Scale | Often operates at a smaller scale and may not require big data technologies | Often involves big data technologies and distributed computing for large-scale processing |
| Certainty | Generally deal with relatively certain outcomes based on historical data | Deals with a degree of uncertainty, especially in predictive modeling and future projections |
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).
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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
Data Analyst Skills
Source: Amazon Job Description LinkedIn
| Categories | Data Science | Data Analytics |
| Programming Languages | Python, R, and Scala | SQL, Python, and R |
| Data Manipulation | Pandas and NumPy | SQL and Excel |
| Statistical Analysis | SciPy and Statsmodels | Excel and SPSS |
| Machine Learning | Scikit-learn, TensorFlow, and PyTorch | Scikit-learn and RapidMiner |
| Data Visualization | Matplotlib, Seaborn, and Plotly | Tableau, Power BI, and QlikView |
| Big Data Processing | Apache Spark, and Hadoop | Apache Spark, Hive, and Pig |
| Version Control | Git and GitHub | Git and GitHub |
| Data Warehousing | Amazon Redshift and Google BigQuery | Snowflake and Amazon Redshift |
| Cloud Computing | Amazon Web Services (AWS) and Microsoft Azure | AWS and Google Cloud Platform (GCP) |
Learn about the difference between Data Science, Data Analytics and Big Data in our comparison blog on Data Science vs Data Analytics vs Big Data.
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
- 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.
- 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.
- Exploratory Data Analysis (EDA): EDA is a tool that Data Scientists use to study data characteristics and find patterns, anomalies, and correlations. This step uses data visualization software and other significant statistical techniques to get insights.
- Model Development: Data scientists build descriptive or predictive models using machine learning algorithms and statistical techniques. This includes evaluating, training, and selecting the right model, as well as improving features for better predictions.
- Model Deployment: To help users make predictions or get insights, professionals deploy a developed and tested model into a production environment. This might include integrating the model into already-existing software platforms or developing APIs through which end users can interact with the model.
Data Analytical Process
- Data Collection: The first step in the data analytics process is to collect relevant information from a variety of sources. Data extraction from flat files, databases, or APIs may be required for this. Surveys and manual data entry are more methods for gathering data.
- Data Cleaning: Preparing the data for analysis and cleaning it up is the main responsibility of data analysts. This step handles missing values, gets rid of duplicates, standardizes formats, and fixes data inconsistencies.
- Exploratory Data Analysis and KPI Configuration: Data analysts do exploratory data analysis to identify trends, patterns, and correlations, as well as to observe the characteristics of the data. They also put up Key Performance Indicators (KPIs) to track and measure particular metrics relevant to the investigation’s goals.
- Analyzing Data with Power BI and Tableau: Data analysts may use Power BI and Tableau to do in-depth data analysis, create visualizations, and generate insightful reports and dashboards. The dynamic and interactive visualizations provided by these technologies enable data-driven decision-making.
- Creating Presentations for Communicating Findings to Stakeholders: Data analysts create reports and presentations to effectively communicate their understandings and results to 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.
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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 1,21,000 Data Analyst jobs available in India. Whereas in the United States, there are over 2,26,000 jobs available.
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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).
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Frequently Asked Questions (FAQs)
Who earns more data scientist or data analyst?
In India, the average compensation for a data scientist is INR 10.5 LPA, compared to INR 6 LPA for data analysts. Both job descriptions have plenty of options for career development.
Can a data analyst become a data scientist?
Working as a data analyst initially might be a great way to get started in the field of data science.
Does a data analyst require coding?
Yes, if you want to pursue an online degree in data analytics, you must know how to code. It does not need extremely sophisticated programming knowledge. However, learning the fundamentals of R and Python is required. Additionally, having considerable knowledge of querying languages like SQL is more than required.
What should I learn first, data science or data analytics?
A beginner without programming experience could find that starting with data analytics tools like Microsoft Excel and Power BI is an appropriate choice.
Is data analytics harder than data science?
Data science is more difficult because it requires a greater knowledge of statistics, mathematics, and machine learning. Data analysis is needed for the same degree of statistical knowledge as well as familiarity with using software programs like Tableau or Excel.