Before diving into one of the most lucrative data science sectors, it is vital to know the key differences between machine learning and data science and how they go hand in hand. Machine learning is a subset of Data science but when it comes to the type of work, ML engineers and a data scientist does is poles apart but they both work towards the same goal, providing critical skillsets to help each other in the process. 

The best way to start is by understanding the origin of Data Science. Earlier businesses and other institutions dealing with data used conventional methods to store most of the data in excel sheets. Simple business intelligence tools were capable of analyzing and processing this data. The reason behind it was the presence of a lesser amount of data. But over time, the amount of data available to be analyzed kept increasing. DOMO Incorporation, a computer software company, predicts that by 2020, 1.7MB of data will be created every second for every person on earth. This is the scale of data that will be available in the future. Most of it will be either semi-structured or unstructured data. For processing the data of this magnitude, we need more sophisticated and advanced tools and techniques. This is where Data Science comes into play with its deep dives at a granular level of data to mine and understand uncertain behaviors and trends. It can bring out hidden insights that can help entities to make smarter business decisions. Companies like Netflix mines data related to its customers' viewing patterns and understands what drives user interest; based on the findings, it produces original series. P&G utilizes time series models to understand future demand more clearly, which helps to plan for production levels more optimally.

Now let us understand what machine learning is?

The idea behind machine learning is that you teach machines by feeding them data and letting it learn on its own without any human intervention. The learning process begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the measures that we provide. The primary aim here is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. To avoid confusion, let us first see the fields of Data Science. Data science covers a broad spectrum of domains, and machine learning is one of them. Apart from machine learning, Artificial Intelligence and deep learning are also significant domains under Data Science and a subset of Machine learning. So machine learning, Deep Learning, and Artificial Intelligence are all used in data science for analysis of data and extraction of useful information from it.

To understand how is Machine Learning used in Data Science, let us see the Data Lifecycle and the stage at which machine learning is used. Suppose you want to create a recommendation system for your e-commerce website. This system recommends products to the customers based on their shopping patterns. To build such a recommendation system, you may use the data related to customer's browsing history, previous purchases, reviews, ratings, profile details, card details, etc. During the development process, you will go through the different stages of Data Science Lifecycle. You will begin with the Business requirement stage where you will understand the problem which you are trying to solve. Through technology, we are trying to increase sales with the help of our recommendation system, where you will reach the Data Acquisition stage to identify different sources from which the data will be acquired for your recommendation system to work. User ratings, comments, cart history etc are some examples. Then you will reach the Data Processing or data Cleaning stage. In this stage, the raw data will be transformed into the desired format so that it becomes possible for you to perform operations on it. Then comes the Data Exploration stage, where a data analyst uses visual exploration to understand what is in a dataset and the data's characteristics. These characteristics can include size or amount of data, completeness, the correctness of the data, possible relationships amongst data elements, etc.

The fifth stage is where you incorporate machine learning in data science. This stage is called Data modeling. Now let us see how machine learning is implemented in the Data Modelling Stage- First, the previous phases' data is imported and should be in the proper structure. Table or CSV formats are some preferred formats. After this, the data is further cleaned to get rid of any inconsistencies. The data model is then built where the data is split into two sets, one for training and the other for testing. The training dataset builds the model, and various Machine Learning algorithms are also used. In the next stage, the model is trained using the training dataset, after which; it is then evaluated by using the testing data set. At this stage, the model is fed new data points and it must predict the outcome by running the new data points on the Machine learning model that was built earlier. After the model is evaluated using the testing data, its accuracy is calculated, and various methods then improve the accuracy.

 So this was the role of the machine learning process in the Data Science lifecycle. After the machine learning stage, the final model is deployed onto a production environment for conclusive user acceptance.

We hope that now you understand how Data Science and machine learning go hand in hand and you can take a call with which one you would choose to build your career in: as a Data scientist or a machine learning engineer.