Difference of Data Analytics and Machine Learning

Data Analysis

“Data science is to gain actionable insights from data”. The data that we deal with can be historical data or Real-time data. Actionable insights are important, because at the end, every data analytics project is performed with an aim to make a decision making process easier. Anything that helps to make the decision making process easier is an Actionable Insight.

Examples:

 1. Providing a CEO with a comprehensive dashboard to monitor the market performance of a new product, by visualization sales data.

Figure 1: Where Data Analytics located in the cross domains.

2. Predicting the sales of the new product in the next quarter. 

Machine Learning

Machine Learning can be thought of as a collection of techniques which enables computers to perform expertise tasks.

Examples: 

1. Our Previous example, predicting the sales of a product, is one of the most common use-case of Machine Learning termed as predictive modelling.

2. Image Classification - Google photos can identify the objects in a picture and even the occasion in which the picture was taken. Explore Computer Vision to know more.


Similarities There are many task, predictive modelling for instance,that involve both Data Analysis and Machine Learning. Data Analysis is performed to clean, transform the data, and extract the right information from the data (Feature Engineering) to input it to the Machine Learning technique. Machine learning model performs the expert task on the input, in case of predictive modelling to predict a value or classify each input record.

Difference In both the fields there are some cases in which the involvement of the other is minimum. For Instance, our first example of dashboard does not involve Machine Learning, because there no expert task involved.Likewise, our last example of computer vision, have a very minimum involvement of data analytics, and much of Machine Learning (read neural networks).

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