In this data-driven world usage of words like Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are common and are often used by the professionals in the field. Often these terms are confusing to a beginner and the terms seem similar to a novice in the field. This article aims at clarifying you the differences that these each term carries.
At the end of this article, you will come to know:
- What is Data Analysis?
- What is Data Mining?
- What is Data Science?
- What is Machine Learning?
- What is Big Data?
- What is the difference between Data Analysis, Data Mining and Data Science?
- What is the difference between Machine Learning, Data Science and Big Data?
The process of sourcing, cleaning, transforming and analyzing data to find out the meaningful pieces of information or insights out of big datasets which are useful to answer the big business questions is called Data Analysis. The solutions to the business challenges lie in Data Analysis. With the help of the meaningful information derived out of the datasets, businesses identify the core areas they need to work on and they need to improve on. Also with the help of Data Analysis businesses find out their weak areas as well.
The process of finding or extracting useful information out of the large datasets is called Data Mining. Underlying patterns in big datasets are explored using Data Mining.
Data Science is a broader field using various algorithms and processes to extract meaningful insights out of the unstructured and structured data. Deriving insights out of the unstructured datasets are not possible using conventional methods of Data Extraction and so Data Science is an important field on that part.
If you will look at the above definitions, you will find all these terms similar due to the common usage of the line- ‚?? finding relevant information‚??.
Now explore the differences these terms carry:
- Data Analysis vs Data Mining vs Data Science
Data Mining is a narrower term encompassing only the methods required to find the relevant information out of the big datasets.
Consider you have a data warehouse where all your data is kept and stored. This data is cleaned as well, so you do not require to remove the unnecessary data that is not relevant to your business.
What you will do now is Data Mining. You will extract the relevant information out of this dataset and identify the hidden patterns involved in it. This is data mining.
Data Mining is different from Data Analysis in a way that apart from finding and extracting the relevant information out of your datasets, you also analyze the patterns and find solutions to your business problems in Data Analysis which you do not find in Data Mining.
You need to find out how the sales department of your company performed in the last year and how effective it was as compared to this years'. Now, what will you do?
You will collect data from various sources, clean it by deleting the unnecessary data and transform it into a more readable or a different desired format. Data mining is the next step you will do with this data- You will find the hidden patterns that are lying and the necessary information that is contained in this dataset.
But this only won't tell you how effective the sales department of your company was unless you do not analyze the data here. Data Analysis is the next step you will take in this case. You will particularly analyze the dataset of the previous year and compare it with this year, and then draw the necessary information of which sales are high, which sales generated more profits and than concluding the effectiveness of the sales department.
THIS IS THE DIFFERENCE BETWEEN DATA ANALYSIS AND DATA MINING. You do not only find patterns but analyze it.
Note: Data Mining is one step involved in Data Analysis
Data Science is a broader concept from Data Mining and Data Analysis where you do not only find patterns and analyze it but also forecasts future events. With the help of data science, forecasting future events in businesses with the help of present and historical data is possible.
- What is Machine Learning?
It is the subfield of Artificial Intelligence by which machines perform specific complex tasks without the intervention of human beings. Using Machine learning, machines have become smarter to perform those tasks which earlier required the involvement of human beings.
Self-driving cars which have been made possible to run on the road are possible using Machine learning algorithms were using Machine learning algorithms the software and sensors inside the car are able to learn the objects that it encounters in the road. A car is able to identify the objects and people on the road using these Machine learning algorithms and then accordingly takes turns.
The large volumes of data comprising audio files, video files, images, text, numbers are called Big Data. The zettabytes of data that are created through social media, online platforms, finance, healthcare are the example of the Big Data.
- Machine Learning vs Data Science vs Big Data
Using the different methods of supervised, semi-supervised and unsupervised Machine learning, a machine is able to run and execute complex tasks. The learning from the big datasets easily come using a machine learning algorithm. Data Science is another field of extracting useful insights encompassing machine learning. Often it becomes difficult for a Data Scientist to explore and extract from large datasets which are easily extracted from using machine learning algorithms.
Big data is only data but existing in larger volumes, so it must not be confused with any of the terms like Machine Learning, Data Science or Data Analysis.