Table of Contents
ToggleData science and Data Analytics becomes popular during a time.
Today, data is the new oil for businesses to gather critical insights and improve business performance to grow in the market.
As per Economic Forum that by the end of 2020, the daily global data generation will reach 44 zettabytes. By 2025, this number will reach 463 exabytes.
of data!.
Big Data includes everything β texts, emails, tweets, user searches (on search engines), social media chatter, data generated from IoT and connected devices β basically, everything we do online.
The data generated every day via the digital world is so vast and complex that traditional data processing and analysis systems cannot handle it.Thus the role of Data science and Data Analytics comes.
Data analysts and data scientists represent two of the most in-demand, high-paying jobs in 2021. This exponential growth has led organizations of all sizes to wonder how they can leverage information to realize business benefits.
Both data analysts and data scientists work with data, but they do so in different ways.
One of the biggest differences between data analysts and scientists is what they do with data.
Data analytics refers to the process and practice of analyzing data to answer questions, extract insights, and identify trends. This is done using an array of tools, techniques, and frameworks that vary depending on the type of analysis being conducted.
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Data analysts typically work with structured data to solve tangible business problems using tools like SQL, R or Python programming languages, data visualization software, and statistical analysis. Common tasks for a data analyst might include:
Whereas data analytics is primarily focused on understanding datasets and gleaning insights that can be turned into actions, data science is centered on building, cleaning, and organizing datasets. Data scientists create and leverage algorithms, statistical models, and their own custom analyses to collect and shape raw data into something that can be more easily understood.
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Hereβs a detailed table comparing Data Analytics and Data Science in a consolidated format:
Aspect | Data Analytics | Data Science |
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Definition | Inspecting, cleansing, transforming, and modeling data to extract useful information for decision-making. | Using scientific methods, algorithms, and systems to extract knowledge and insights from data. |
Focus | Analyzing historical data to discover patterns and trends. | Building models, algorithms, and systems to predict future outcomes and automate processes. |
Objective | Solve specific business problems using existing data. | Explore unknown patterns, develop new tools, and solve complex, predictive tasks. |
Approach | Diagnostic, Descriptive, Predictive analysis. | Predictive, Prescriptive, and Exploratory with machine learning. |
Techniques | Statistical analysis, business intelligence, data visualization. | Machine learning, AI, deep learning, data engineering, and hypothesis testing. |
Tools Used | Excel, SQL, Tableau, Power BI, Google Analytics. | Python, R, TensorFlow, Hadoop, PyTorch. |
Type of Data | Mostly structured data (e.g., transactional data, sales, etc.). | Structured, unstructured (text, images, videos), and semi-structured data. |
Output | Dashboards, trend reports, KPI summaries. | Predictive models, AI-driven applications, automation systems. |
Nature of Work | Answering predefined questions, producing reports, and supporting decision-making. | Conducting research, creating algorithms, and building intelligent systems. |
Programming Skills | Basic coding (SQL, some Python/R for manipulation). | Heavy programming (Python, R) for data manipulation, model building, and ML. |
Data Sources | Internal business systems (ERP, CRM), transactional databases. | Web data, social media, IoT devices, sensors, and large-scale external data. |
Data Volume | Typically handles smaller, structured datasets. | Works with massive volumes of unstructured and semi-structured data (Big Data). |
Complexity of Analysis | Simplistic patterns and trend analysis, often retrospective. | Advanced models, deep learning, AI for future insights and automation. |
Required Background | Statistics, business intelligence, and basic data management. | Mathematics, statistics, advanced computer science, and machine learning. |
Applications | Sales reports, customer segmentation, operational efficiency dashboards. | AI-driven apps, recommendation engines, fraud detection, and predictive analytics. |
Deliverables | Dashboards, visualizations, simple data reports. | Complex models, AI systems, predictive solutions, and automation tools. |
Career Roles | Data Analyst, Business Analyst, BI Analyst. | Data Scientist, Machine Learning Engineer, AI Specialist. |
Career Path | Data Analyst β Senior Analyst β Analytics Manager. | Data Scientist β Senior Data Scientist β AI/ML Expert. |
Industries | Retail, finance, marketing, healthcare, operations. | Technology, R&D, pharmaceuticals, autonomous systems, AI-driven sectors. |
Timeframe | Short-term analysis for immediate business decisions. | Long-term strategies for future growth and innovation. |