Welcome to the comprehensive guide to the differences between Data Science and Data Mining.
The vast universe of technology, along with its improvement and development, is now crowded with a wide array of new terminologies. Amongst them are different terms related to data. Data related terminologies and job offers came into existence when organizations and enterprises realized the profits they could make from the data they collected.
The Burgeoning Data Needs Handling
Data is everywhere, and with every passing second, new data keeps getting added. Would it surprise you to know that data is doubling? A person who can study the data has the power to transform the basic tenets of individual-enterprise interaction. A Forbes article predicts that by the end of 2020, for every human on Earth, there will be 1.7 billion new data every second. IBM speculated that approximately 2.5 billion gigabytes of information was created every day in the year 2012 alone.
Since you are here, it is but natural to assume that you are aware that data is multiplying rapidly and shows no signs of stopping. The consistent trend has led to the generation of numerous methods of processing and handling data with the two most prominent ones being Data Science and Data Mining.
The two terms Data Science and Data Mining are often used interchangeably since they both deal with data. However, they have a great number of dissimilarities which set them apart in two different leagues.
Data Science Vs Data Mining
Aspirants and students looking for a career in the field should know the individuality and uniqueness of each. Before we get to the details, let us have a quick look at the differences.
The Major Role:
Data Science derives insights from structured and unstructured data. It is a multi-disciplinary field used for qualitative analysis. It comprises of behavioural science, language processing, data visualizations, data mining, and statistics and unstructured data.
Data Mining analyzes data sets created from structured data to unearth anomalies and hidden correlations and patterns.
It is used for extracting data and generating predictions models. It is a subcategory of data science.
Understanding the domain:
Data Science is also referred to as data-driven science. It is a field or wide domain that is inclusive of the procedures of obtaining and analyzing data and gaining information from it.
Data Mining is also referred to as data discovery. It is a method and technique inclusive of data analysis. The focus is on discovering usable information in a dataset and using it to unearth covered patterns.
When did the concept become popular:
The data science team has been used since 1960.
Data mining concept became popular in the 1990s.
Data Science converts data bytes into usable data to find patterns and announce predictions.
Data Mining extracts usable information and eliminates redundant data through processes like statistical modelling
Data Science creates data-focused products for companies and drives decisions through the aid of data. It can be used across industries.
Data Mining centres on discovering data from multiple sources and converting the data into a useful tool. It can be used across industries
Data science is scientific research which paves the way for a project-, program- or portfolio-centric analysis.
In Data mining, the identified trends and patterns are used by organizations to formulate operations, marketing and financial strategies to fuel business growth.
In Data Science, from the point where data gets collected. It is a broader field which includes data mining
In Data Mining, once data sets are created. It is a subset of data science
But to get a clear understanding of the two, it is essential to understand what each term represents, along with its workings and tools. As is obvious from the above, Data Mining is one of the many processes of data science.
Understanding Data Science
Data Science is a domain of study incorporating behavioural science, statistics, data mining, mathematics, information analytics, and predictive analyses. It is a wider area of research which makes use of many algorithms and operations to derive informative insights from both structured and unstructured information.
Gaining information from unstructured data is not possible through the traditional processes of Data Extraction – this is how Data Science becomes an integral domain in itself. The procedure consists of accumulating data, comprehending it, and using this understanding to arrive at an analysis. It is thanks to this process that data scientists can create various applications and products which deal with, and are created on the basis of data.
The Importance of Data Science
The organizational and social imprint of Data Science is diverse and wide. An MIT paper shows that businesses using gathered data to arrive at decisions and strategies are 6% more successful than their competitors. It is no wonder that data-driven decisions are becoming a favourite for every smart and tech-driven business out there. Data Science is rapidly changing the world perception of marketing tactics, consumer affinity, business issues, supply chain, corporate connections, and predictive modelling.
Dresner’s research discovered that industries helming the spike in huge data investment were Healthcare (64% adoption), Finance (71% adoption), Advertising (77% adoption), Insurance (83% adoption), and Telecommunications (with a whopping 95% adoption). Data Science may be a widespread field, but its core aim is to obtain data to arrive at well-researched decisions.
How does Data Science work?
Data Science comprises the following steps:
- Accumulating the data: The procedure begins with the accumulation of data – this data may or may not have structure, and it may even be semi-structured.
- Wrangling the data: The next step is to work upon the data. The data obtained is cleaned and converted to a comprehensible format to gain maximum output from it. Data wrangling is quite a lengthy task. Almost 80% of the work period is spent on this step of the procedure.
- Analyzing the data: Post wrangling, it is time for analysis. Statistic models and algorithms are used for analyzing the converted data.
- Visualizing the data: In the context of huge amounts of data, data visualization becomes essential. Through visuals, such as graphs, outcomes are explored and conveyed most effectively.
- Using the data for predictions: For both efficient forecasting of patterns in the future and gaining insights, AI algorithms are the best resort. They are not only valuable for generating trend prediction; they also aid the creation of fresh and innovative procedures and products.
- Recapitulation of the Data: Data insights are immensely valuable as they assist the development of properties. This allows the model to consistently improve and provide punctual performance and deliver approximate results.
Tools used in Data Science
Data Science makes use of some of these essential tools:
- Python: This is the most favoured programming language in the Data Science world as well as the universe of software development. This is because python libraries for data science provide a diverse array of libraries.
- Apache Spark: An Advanced Tool for Big Data, Apache Spark offers data analyzing and data processing facilities. It is best known for its feature of carrying out stream processing, rather than the batch processing performed by its predecessor platforms.
- SAS: Statistical Analysis System – also known as SAS – has been created by the SAS Institute to carry out a multitude of statistical procedures. A close-source tool, it is the popular choice for many businesses due to its feasibility and stability.
- Tableau: A visualization software, Tableau aids the creation of interactive charts and graphs. It can chart out latitudes and longitudes on maps. Moreover, it also interfaces with SQL databases, spreadsheets, and OLAPs.
- R: An open-source programming language, R provides numerous statistical packages that help data visualization and data analysis.
- TensorFlow: A robust machine learning library, TensorFlow allows the implementation of deep learning algorithms. Since it is supported by GPUs (Graphical Processing Unit), TensorFlow is a rapid processing library. Learn more about data science tools.
Understanding Data Mining
The core purpose of Data Mining is to unearth important information in a dataset and make the best use of this to discover and decode future trends.
Data Mining involves the analysis of great amounts of past data which remained in the dark until they were discovered. It is this procedure of searching for and gaining worthwhile insights from big datasets which are called Data Mining. Through this process, the underlying trends in huge datasets are figured out.
The Importance of Data Mining
Data Mining involves a wide variety of methods included in Data Science. It is because of this reason that Data Mining is seen as a category within the larger domain of Data Science. Admittedly, there is a natural overlap, and like Data Science, Data Mining also incorporates data cleaning, pattern prediction, statistical analysis, data conversion, machine learning, and data visualization.
However, Data Mining is not solely focused on algorithms. The main aim of Data Mining is to obtain data from a great number of sources and to transform it into a more useful version of itself.
Learn More: Top Data Mining Algorithms
How does Data Mining Work?
Data Mining comprises the following steps:
- Cleaning the data: The first step is to clean the data and remove the irregularities.
- Integration of data: The second step is to accumulate and combine data gathered from all the various sources.
- Selection of the data: The next step is to sift out the usable data from all the integrated information, which can be used for Data Mining.
- Cleaning of the data: The obtained data may have some errors, such as inconsistency and absent values, which require cleaning. This process makes use of a variety of tools and methods.
- Conversion of the data: Some of the methods used for converting the data into a comprehensible format are aggregation, smoothing, and normalization.
- Mining the data: This is the part of the procedure where patterns are unearthed. Association analysis and clustering are some of the methods used in Data Mining for this purpose.
- Evaluating the data: Now, the irrelevant patterns are eliminated to avoid cluttering. The patterns left are analyzed, and this is an important part of the procedure.
- Using the data: The last part of the procedure makes use of the discovered data. This data unearthed during Data Mining is used to arrive at well-informed decisions.
Also read: Data Mining Applications in Real World
Tools used in Data Mining
Data Mining makes use of some of these essentials:
- Weka: An open-source software developed by the University of Wichita, Weka is a no-coding Data Mining GUI, which is user friendly. With Weka, AI algorithms can be called directly or be imported with Java code. Clustering, visualization, and classification are some of the tools provided by Weka.
- RapidMiner: One of the most loved Data Mining tools, RapidMiner needs no code for operation, and is Java-based. Moreover, it offers a variety of Data Mining facilities such as data representation, clustering, data processing etc.
- KNime: A powerful Data Mining platform, KNime is mainly used for ETL (Extraction, Transformation, and Loading), also known as data processing. Additionally, it combines numerous constituents of Data Mining and Machine Learning to deliver an inclusive suite for all fit operations.
- Oracle DataMining: A wonderful tool for classification, analysis, and prediction of data, Oracle DataMining lets its user carry out Data Mining on SQL databases for extraction of schemas and views.
- Apache Mahout: An extension of the Hadoop Big Data Platform, the Apache developers created Mahout to answer the increasing demand for analytical procedures and Data Mining in Hadoop. Consequently, it has facilities like clustering, classification, regression etc.
- TeraData: Warehousing is essential for Data Mining. Also known as TeraData Database, TeraData offers warehouse facilities that deliver Data Mining tools. It also conserves data as per usage – this means that quick access is provided to regularly used data.
- Orange: Best known for combining Data Mining facility and Machine Learning, Orange is software written in Python. It provides interactive and appealing visuals to its consumers.
Summing up the differences between the Data Science and Data Mining
The above analysis of the differences indicates that Data Science and Data Mining are two key concepts of data technology. They both revolve around dealing with the rapidly surging amount of data, but their involvement with data intermingles as Data Mining is one of the many processes of Data Science.
Both play key roles in helping organizations recognize opportunities and arrive at worthwhile decisions. Additionally, as has been discussed, the knowledge needed for procedures in both of these fields also varies. Hence, the analysis of the differences in their approach, tools used and steps applied – is worth knowing.
What do the differences mean for you as a student?
Understanding the differences between the two concepts is just the first step in recognizing your personal goal or ambition. Are you happy cleaning data and working on both structured and unstructured data? Or are you more inclined towards using data sets or databases to discover what the numbers and figures are hiding? Data is one of the most expensive materials available in the universe, notwithstanding the current global lockdown imposed by governments across the world.
It is the data which resulted in these decisions, and it is data which will help popularize a cure. But, the question is, do you want to collect, clean, extract, analyze, summarise and visualize the data as a scientist, or do you want to experience only the thrill of finding anomalies and correlations in the huge structured data shared with you?
If you are curious to learn about data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.