Want to start your career in Data science? But don’t know what data science meaning is?
Don’t worry, we are here to help you! Reading this article will help you to get a clear understanding of what Data science meaning is, the role of data scientists in Data Science, the difference between business intelligence and Data science, the life cycle of Data science, and much more.
So, let’s read further to learn more about all these topics.
What is Data Science Meaning?
Data science meaning is in the word “Data Science” itself, it is a combination of two words Data and Science.
The term Data refers to the raw facts and figures, available in large volume, that are collected from various sources such as databases, websites, social media platforms, and more. At the same time, Science implies a systematic and organized approach to gathering, analyzing, and interpreting data using scientific methods, statistical techniques, computational algorithms, and much more.
Combining these two elements, “data science” means the systematic study of data to extract valuable insights, patterns, and knowledge from it. That can be used for making informed decisions, improving processes, developing predictive models, and generating new business opportunities.
Data Science Life Cycle
After understanding Data Science meaning, let us now understand the life cycle of Data Science models. The data science life cycle refers to the systematic process that data scientists follow to extract insights and value from data. Here are the key stages of the data science life cycle:
Data Ingestion- This involves the process of collecting and importing raw data from various sources such as databases, APIs, files, web scraps, or streaming platforms into a data storage or processing system. This step ensures that data is captured in a good and efficient manner, ready for further processing and analysis.
Data Storage- This stage include the storage and management of structured or unstructured data in a secure and accessible manner. Various types of storage systems such as relational databases, NoSQL databases, data warehouses, and cloud storage services are used based on factors like data volume, variety, and access patterns. The goal of data storage is to ensure data is stored good manner with high availability for future analysis.
Data Processing- Data Preprocessing involves transforming, cleaning, and enriching raw data to make it suitable for analysis and decision-making. This step includes processes like handling missing values, deleting duplicate values, and much more. Techniques such as data cleaning, transformation, feature engineering, integration, enrichment, and summarization are applied to process data efficiently and accurately.
Data Analysis– This stage involves the exploration and interpretation of processed data to find valuable insights from it. These valuable insights are further used for trend analysis and informed decision making.
Communication- This last step of the life cycle involves tasks like- effectively conveying insights, findings, and recommendations derived from data analysis to stakeholders, decision-makers, and end-users. This step ensures that data-driven insights are understood, trusted, and actionable, leading to informed decision-making and business value.
Data Science Meaning vs Data Scientist
The terms Data Science and Data Scientist are quite related to each other, where data science is considered a field and Data Scientists are considered the expert practitioners of that field. Let us understand it in detail.
Data science is a field related to making sense of large amounts of Data available on various platforms, it’s kind of like Imagine you have a huge pile of puzzle pieces, and your job is to put them together to reveal a picture. That’s what data scientists do with data, they find connections, patterns, and insights that can help businesses and researchers understand things better. Data scientists use math, computer, and analytical skills to dig into the data and find answers to important questions. They play a bit like detectives, exploring data to uncover valuable information that can guide decisions and solve problems.
Data Science vs Business Intelligence
Data science and business intelligence (BI) are both about using data to help companies make better decisions, but they have different focuses and roles. Data science is like a detective, digging deep into data to find patterns and insights that can predict future trends or solve complex problems. It uses math, statistics, and computer programming to analyze large amounts of data. On the other hand, business intelligence works moreover on visualizing data using stats and graphs to give a clear picture of what’s happening in a business right now. It focuses on creating easy-to-understand reports that help managers and executives understand how their business is performing and what they can do to improve their business performance.
Data Science Tools
Data Science relies on a variety of tools for different purposes, some of the common and most popular tools used by data scientists include:
| Tools | Their uses in Data Science |
| Python & R | These are programming languages used for Data analysis, data visualization, and much more. |
| SQL | Used for querying and managing structured data in relational databases. It’s essential for data storage and transformation. |
| Apache, Hadoop | They are big data processing frameworks used for Distributed computing mainly for batch processing of Data. |
| TensorFlow, Pytorch | These are deep-learning libraries used for building and training neural networks. TensorFlow is widely used for its flexibility and production readiness, while PyTorch is used for its dynamic computation graph. |
| Tableau and Power BI | These tools are mainly used in Business intelligence, They allow users to create interactive dashboards, charts, and reports to gain insights. |
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Data Science Meaning FAQs
What is data science?
Data Science is about using math, statistics, and computer skills to find patterns and insights in large volumes of Data. These insights help businesses make better decisions and solve critical problems efficiently.
What skills are needed to become a data scientist?
Some important skills needed to become a Data Scientist include-
Good programming skills (Python, R)
Good statistical analysis skills
Knowledge of machine learning tools
Knowledge of data visualization tools.
Good knowledge of SQL operations and commands.
What is a Data Scientist's Salary?
The average salary of a Data Scientist in India is around 12 Lakh per annum. Which may vary according to experience, knowledge, industry, and geographical location.
If you are a fresher in this field with no prior experience, you can expect a salary of around 4-8 LPA depending on the company and your knowledge.
Is data Science a good career?
In today’s data-driven world, there is a high demand for good data Scientists in the market, as every company wants to extract valuable insights from the tons of data available over the internet.
