Table of Contents
Introduction: Data Science vs Data Mining
In this article, we throw light on data science vs data mining. Data mining is the process of analyzing data and extracting helpful information whereas data science is the process of obtaining valuable insights from structured and unstructured data by using various tools and techniques.
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What is Data Science?
1: Which of the following algorithms is most suitable for classification tasks?
Data science is an interdisciplinary field focused on extracting knowledge from large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains. It uses complex machine learning algorithms to build predictive models. Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning.
The increase in the volume of data sources, and subsequently data, has made data science one of the fastest growing field across every industry. Hence, it has evolved as one of the most promising and in-demand career paths for skilled professionals. Data scientists construct questions around specific data sets and then use data analytics and advanced analytics to find patterns, create predictive models, and develop insights. These are used for decision-making within businesses. It is applied in various fields like E-commerce, healthcare, transportation etc.
What is Data Mining?
Data mining is the practice of analyzing large databases in order to generate new information. It is used to break down patterns and connections in data based on the information users request or provide. Data mining helps solve business problems by sorting through large data sets to identify patterns and relationships. It is considered as one of the core disciplines in data science.
Data mining is a market research tool which helps reveal the opinions of a given group of people. The information generated by data mining can be used in business intelligence and advanced analytics applications. It helps in planning business strategies and managing operations such as marketing, advertising, sales and customer support, manufacturing, supply chain management, finance and HR. It is also used in credit risk management, fraud detection, and spam filtering.
Data Mining vs Data Science Comparison
| Data Science | Data Mining |
| It includes working with a huge amount of data for building prescriptive analytical models. | It is a technique of extracting important and vital information and knowledge from a huge set of data. |
| The goal is to build data-dominant products for a venture.
|
The goal is to make data more vital and usable by extracting only important information. |
| It involves using statistical, machine learning, and programming techniques to extract insights and knowledge from data. | It involves using algorithms and statistical methods to discover hidden patterns and relationships in data. |
| Data science is a broad field that encompasses various techniques and tools for analyzing and interpreting data. | Data mining is a specific technique used within data science to extract patterns and knowledge. |
| The purpose of data science is to create data-centric projects. Data scientists explore, sort, and analyze data that helps businesses in decision-making. | Data mining aims to make the data we already have more helpful by extracting only valuable information from large amounts of data sets. They are then organized to discover meaningful patterns and structures. |
| It is mainly used for scientific purposes – to build predictive models, visualize data, and communicate findings to stakeholders. | It is mainly used for business purposes – to identify useful information such as customer behavior, product preferences, and market trends, that can be used to make better decisions. |
| Some of the main applications of data science are:
Fraud and risk detection in financial systems. · Targeted Advertisement · Airline Route Planning · Data Management in Healthcare · Advanced Image Recognition · Personalized recommendations in entertainment
|
Some of the main applications of data mining are:
· Market analysis for understanding market risks and predicting future trends · Financial analysis · Inspecting the performance of students in higher education · Fraud detection |
Skills Required for Data Science vs Data Mining
Skills Required for Data Scientists
As the demand for data scientists are on the rise, students and professionals are keen on brushing up on their skills to pursue a career in data science. The two main skills required for data science are – technical and non technical.
Some of the technical skills are:
- Programming
- Statistics
- Mathematics
- Processing large data sets
- Statistical analysis and computing
- Data Visualization
- Data Wrangling
- Data Extraction
- Machine Learning
- Model Deployment
- Database Management
- Cloud Computing
Programming
Programming knowledge is essential for data scientists. There are hundreds of languages in programming in which some are more suited for data science than others. Some of these languages are – Python, R programming, SQL, SAS etc.
Data Mining
It refers to the gathering, sorting, and analyzing of large datasets. Data scientists need to know various mining techniques like linear regression analysis, clustering analysis, and anomaly detection.
Data Wrangling
It is the process of cleaning and organizing complex data sets to make them easier to access and analyze. A data scientist should be able to extract data from different sources and transform it into suitable format for analysis. Useful tools for data wrangling are:
- Altair
- Talend
- Alteryx
Statistics and Probability
Data scientists should to be able to collect, interpret, organize, and present data. In order to comprehend concepts like mean, median, mode, variance, etc. one should have a clear idea of statistical concepts.
Data Visualization
Visualizing data is an important part of communicating the insights a data scientist has uncovered. It can be done using various visualization tools, from creating visualizations directly in Python, or using software like Tableau, Microsoft Excel, PowerBI.
Cloud Computing
The data is usually stored through cloud computing, so data scientists should know how to interact with the cloud, and understand the basic principles of how it works. Some cloud services are: Amazon Web Service (AWS), Microsoft Azure and Google Cloud.
Some of the non technical skills are:
- Strong Communication Skills
- Strong Business Acumen
- Decision Making
Communication Skills
Communication is the key to build strong work relationships and get information. One should involve in group discussions to polish their communication skills. One should also be able to collaborate with teams.
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Decision Making
Data scientists have to work independently and hence should be able to make effective decisions without having to be micromanaged.
Strong Business Acumen
Data scientists need to have a good understanding of business strategy. This will help them understand the needs of stakeholders and decision makers. You can learn this skill as part of any good data science bootcamp or through direct experience.
Skills Required for Data Miners
A data miner has to analyze and review a company or organization’s raw data with the goal of looking for patterns or other types of helpful information. Data mining not only includes data processing and management, but it also involves machine learning, statistics and database systems.
- Proficiency in programming languages
- Ability to process big data frameworks
- Proficiency with Linux
- Database knowledge
- Basic statistics knowledge
- Knowledge of data structure and algorithms
- Knowledge of data mining and machine language
- Communication skills
Programming Skills
Data mining relies heavily on programming and the language depends on the dataset you deal with. R and Python are the most popular programming languages for data mining. C++, Java and Matlab are also useful.
Ability to process big data frameworks
Data miners should be able to extract information and insights from large quantities of individual data points. Hadoop and Spark are the most implemented frameworks so far. Other frameworks include Storm, Samza and Flink.
Proficiency with Linux
Linux is the most popular operating system for data mining. It is a stable and efficient system for operating large data sets.
Database knowledge
To manage and process large data sets, one must have knowledge of relational or non relational databases. Relational database include SQL and Oracle, whereas non relational database include Cassandra, HBase; Document: MongoDB, CouchDB; Key value: Redis, Dynamo
Data mining and machine language
You should be able to understand the concepts, principles, and applications of various data mining and machine learning techniques. So you should be able to use data mining and machine learning frameworks and platforms such as TensorFlow, PyTorch, Scikit-learn, Spark MLlib, H2O.
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Data Mining vs Data Science: Hypothesis Testing
Hypothesis testing is a statistical technique used to verify results obtained through data mining. This helps to establish the validity of results found on smaller data to the larger outside world. It is an integral part of statistical inference. To implement new business strategies, we need to evaluate whether they will work or not. This is done by hypothesis testing. The main goal of hypothesis testing is to understand how well the predictions perform based on the sample data provided by the population.
In data science, hypothesis testing is done in various stages. The initial hypothesis testing is done to visualize the outcome in later stages. The next test will be done after the model is ready. Then the results from both the tests will be compared. This helps to build models that can help enhance business strategies.
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FAQs
What are the skills and qualifications needed to begin a career in data mining?
A bachelor’s degree in a relevant subject such as computer science, data science, math or statistics. Apart from this, you should also have database and statistics knowledge, knowledge of machine learning, data structure and deep learning algorithms and good communication and organizational skills.
Does Data Science Require Coding?
Yes, because majority of data science tasks are carried out by computers. From accessing data in a database to visualizing your conclusions, data science is dependent on programming languages like Python, R, and SQL.
