Data Science is an interdisciplinary field of scientific methods, systems, and processes which helps the extraction of knowledge or insights from data. It deals with the identification, representation, and extraction of meaningful information from a huge volume of the data source to be used for business purposes. When an enormous amount of data is generated each minute, the requirement to extract the meaningful insights becomes a necessity in business. The function of data scientist is to establish the database in order to facilitate data mining and data munging.
Machine learning is the application of artificial intelligence (AI) where the computers are enabled to learn automatically. It has the ability to improve from experience without being explicitly programmed. The machine learning engineers focus on the development of computer programs which would be able to access the data and use it to learn for themselves. It includes observation of the data, identification of the patterns in the data and makes decisions in the future based on the examples provided earlier. Here the primary aim is to enable the computers to learn automatically without human intervention.
Data science and machine learning are closely related to each other. Machine learning is actually a major area of data science. This article is an effort to compare the role and functions of data science versus machine learning. If you would like to know more about them then visit our website at https://upgrad.com/data-science/.
The scope of data science is to create an insight from data dealing with all real-world complexities. The task of the data scientist is to understand the requirement and extract the data. Data science creates insights from data dealing with all real-world complexities. The scope of machine learning is to accurately identify, classify and predict the outcome of new data points by learning patterns from the past. It can be said that machine learning accurately classifies or predict the outcome of new data point by learning patterns from historical data. Here the computer uses the mathematical models to interpret the results.
- Input Data
In case of data science, most of the input data is generated as human consumable data like images, videos, and tabular data. They are to be read, identified and analysed by human beings. In case of machine learning, the input data would be transformed and formulated specifically for algorithm use. A few examples of the machine learning input data are feature scaling, word embedding and adding polynomial features.
- Performance Measure
In case of data science, the measure of performance is not standardised. It tends to change according to the case. In data science, typically there will be an indication of data timelines, querying capability, data quality, interactive visualisation capability, etc. In case of machine learning the measure of performance is crystal clear. Here each algorithm would have a measure to indicate how good or bad was the model in describing the given data. For example, the root mean square error, is an indication of an error in the linear regression model.
- Programming Languages
The most common programming languages used in data science are SQL, SQL like syntax languages like HiveQL, SparkQL, perl, awk, sed, etc. Some framework specific languages like Java for Hadoop and Scala for Spark are also used by the data scientists. In case of machine learning, Python, SQL, and R are the most commonly used languages. Nowadays, Python is gaining more momentum as all the new deep learning researches are converted to Python.
- Development Methodology
The data science projects are mostly developed like an engineering project with a clearly defined milestone. The machine learning projects, on the other hand, are more research-based. They start with a hypothesis and try to get it proved by the data available at its resources.
In case of data science, the end result can be visualised in the form of popular graphs like the bar graph, pie chart, etc. In case of machine learning, the end result is also represented in the form of a mathematical model of trained data.
The above was a brief comparison of data science and machine learning.