Data Mining vs Data Science – An Easy Guide In Just 3 Points

INTRODUCTION 

Data Mining Vs Data Science

“Data is a precious thing and will last longer than the systems themselves”- Tim Berners Lee.

In today’s fast-paced world, data is the new buzzword. Technology is expanding leaps and bounds. 

Data is almost everywhere and has its presence in various domains. Businesses are now able to understand the importance of data and its contribution. Data, when transformed into information, provides highly valuable insights for decision making. 

Data is a vital business asset in today’s world. 

Many data-related terms have come into existence, such as Data Analytics, Data Science, Data Mining, Data Warehousing, etc.

Data mining and Data Science are the two most important concepts in technology. Both these fields revolve around data.

1. WHAT IS DATA MINING?
2. WHAT IS DATA SCIENCE?
3. DIFFERENCE BETWEEN DATA MINING AND DATA SCIENCE 

1) WHAT IS DATA MINING?

Data Mining is a process of analyzing data from different perspectives, discovering hidden trends and patterns in large amounts of data, and summarizing the results into useful information. It is a subset of Data Science.

Data Mining can very well be said to be the process of mining knowledge from data.

Data mining can be divided into six stages:

• Data Cleansing: This is the initial stage in the data mining process. In this step, inaccurate and tricky data are identified and removed from the available data set.

• Data integration: Data Mining gathers data from various sources to make it usable. Thus, in this step new set of information is integrated with the existing data.

• Data Transformation: This is the third step in which data is transformed from one format to another using techniques such as, Smoothing, Aggregation, Generalization, Normalization, and Attribute Construction.

• Data Discretization: It is a process whereby a large number of data values are converted into smaller chunks of data so that the evaluation and management of data become easy. Some famous data discretisation techniques are Histogram Analysis, Binning, Cluster Analysis, Decision Tree Analysis, etc.
• Concept Hierarchies: A concept hierarchy constitutes a sequence of mappings with a set of more general concepts to specialized concepts. Similarly mapping from lower-level concepts to higher-level concepts. In other words, top-down mapping and bottom-up mapping.

• Pattern Evaluation and Presentation: After going through the above stages, when patterns and trends are identified, the data is then presented in the form of graphs, charts, and diagrams so that they can be easily understood with minimum statistical knowledge by the users (e.g., Clients and Customers).

Just like every coin has two sides, Data mining also has its pros and cons.

Pros:

• Better customer relationship management.
• Provides a competitive edge.
• Accurate forecasting of market trends.

Cons:

• Initial deployment is costly.
• Privacy and security issues.

2) WHAT IS DATA SCIENCE?

Data Science is a field that uses scientific methods and processes for extracting knowledge from large unstructured and structured datasets. It is an amalgamation of different domains such as Mathematics, Computer Science, Statistics, and Business acumen.

In simple words, data science is data-driven science.

The process involved in data science can be summed up as follows:

• Understanding of the business: This is the first step in which a thorough understanding of the business and its objectives is to be obtained. To use data science techniques, a defined problem is a pre-requisite. Thus, only after a proper understanding of the business can we set a specific goal for analysis that is in synchronization with the business’s objective.
• Understanding the data: After business understanding, the next task is to understand the data. All the available data is to be collected in this step. The data scientists can look up to the business team as they are more aware of the data present in the organization. In this step, the data is described, relevant data is filtered, data structure and data type are defined. The data is explored in and out using graphical tools.
• Preparation of the data: This is the most time-consuming step in the process of data mining but at the same time, the most important step as well. It involves data filtering, mergers of datasets, cleaning the data, checking the erroneous data, and correcting them.

• Exploratory Data Analysis: In this step, some solutions are conceptualized, and factors affecting them are analyzed before building a model.
• Data Modelling: In this step, relationships are drawn between various information types to be stored in a database. One of the objectives of data modelling is to create the most efficient method of storing information.
• Model Evaluation: In this step, the model is evaluated to check whether it is ready for deployment. The model is tested on carefully thought-out metrics. The evaluation is required to be made until satisfactory results are achieved. Thus, the process of model evaluation helps in choosing and building a perfect model.
• Model Deployment: This is the final step in the data science life cycle or process. After rigorous evaluation, the model is finally deployed. The model is applied to make predictions using the data.

3) DIFFERENCE BETWEEN DATA MINING AND DATA SCIENCE 

Data Mining Vs Data Science

CONCLUSION

Data mining and Data science are often used interchangeably, but from the above discussion on data mining vs data science, it can be figured out that both concepts are different.

The data revolution is expanding day by day and is making its mark in almost every sector of the economy. It is also opening doors to many new professions and has come up with many new career opportunities.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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