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The growth of data has been exponential. With the internet, we have more data now than ever before. According to an IBM report, 2.5 quintillion bytes of data is created every day. This has led to the creation of a new field called Data Science.
What Is Data Science?
Data Science is a technology rather than a science. A data scientist is one who collects, cleans, process and makes information out of data, or makes data speak.
There are two types of data scientists- Type A and Type B. The Type A data scientist can code well enough to work with data but is not necessarily an expert. The Type B data scientist can be an expert in experimental design, forecasting, modelling, statistical inference or things typically taught in the statistics department.
At Google, Type A data scientists are known variously as statisticians, quantitative analysts, decision support engineering analysts, and more. Type B data scientists share some background with Type A but they are also very strong coders and may be trained software engineers. Type B data scientists build models which interact with users, often serving recommendations.
The data in data science is real-time data, the most common example being google searches. Google generates a vast amount of data in the form of search queries which has to be cleaned and processed for insights.
Understanding Machine Learning
Machine Learning is a subspeciality of computer science where a machine (computer) evolves over time. Given a task T, the performance P of the machine increases with experience E.
Suppose you love to hear rock music on Spotify. After the first song you hear, the next song which Spotify suggests will be a random song. But this time, you select a rock genre song. The machine learns that this user likes to listen to rock music and it starts suggesting rock music.
Given a data set, a machine learning algorithm trains on the data set to create a model. This model is used to make predictions or take actions in order to optimize some systems. For instance, in machine learning, there are algorithms to classify potential clients into good or bad prospects, for loan purposes, based on historical data. In the above case, the data set is the songs which the users generate.
Word learning means that the algorithms depend on some data, used as a training set, to finetune a model or algorithmic parameters. This is done through many techniques like regression, supervised clustering etc. Basically, the machine learns through data.
So where do Data Science and Machine Learning intersect?
Data Science is much broader than machine learning. It starts from simple data visualization and descriptive statistics to get insights and manipulations like cleansing to prepare data for machine learning algorithms. Data visualization and pre-processing are out of machine learning scope.
Machine learning needs some data to create a model. The algorithm trains on the data and uses it to make predictions. Data Science plays a role in providing the data for machine learning algorithms. Data Scientists collect the data, process it and use it to create models to predict behaviour. The creating model part is what is called ‘machine learning’.
Machine learning creates systems that can learn from data, and thus improves over time. It can be used to teach robots to walk or use them to answer phone calls.
Data Science is concerned with the extraction of knowledge from data and is used mainly to make sense of data.