{"id":9338,"date":"2020-01-02T11:43:51","date_gmt":"2020-01-02T06:13:51","guid":{"rendered":"http:\/\/ivyproschool.com\/blog\/?p=9338"},"modified":"2021-06-11T13:54:13","modified_gmt":"2021-06-11T08:24:13","slug":"a-beginners-guide-to-data-science","status":"publish","type":"post","link":"https:\/\/ivyproschool.com\/blog\/a-beginners-guide-to-data-science\/","title":{"rendered":"A Beginner’s Guide to Data Science"},"content":{"rendered":"

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\"beginners<\/h2>\n

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Introduction to Data Science<\/span><\/strong><\/h2>\n

This article is a beginner’s guide to Data Science for all the aspirants, covering information from utilizing an abundance of data to careers in Data Science. <\/span><\/p>\n

Today, the Internet is a vital part of our life. Half the world’s population is already using it. Youtube, Netflix, Google searches, Twitter, Facebook, Instagram, Amazon, etc are some big beasts that have exploded the generation of data.<\/p>\n

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\"Fun<\/p>\n

The big question that comes up is what do we do with so much data.!! What should be the goal to capitalize on this booming resource? You got it.<\/span><\/p>\n

\"Data<\/p>\n

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Looks so easy! Isn’t it? But who does this, and how? The Data Scientists do this with their knowledge of Data Science. By all means, they are clash with all the structured and unstructured data, beat it, shape it, model it, till it speaks insights out of itself.<\/span><\/p>\n

What does Data Science include<\/span><\/strong><\/h2>\n

Data Science is an interdisciplinary field. From a bird’s eye view, it comprises of Artificial Intelligence, Machine Learning, and Deep Learning.<\/span><\/p>\n

Artificial Intelligence –<\/span><\/h3>\n

AI is a field that aims at enabling machines in the replication of human-like intelligence by using Natural Language Processing, Deep Learning, etc. Chatbots is a recent example of fast gaining wide usage.\u00a0<\/span><\/p>\n

Machine Learning –<\/span><\/h3>\n

Machine Learning provides machine an ability to learn without requiring any external programming. The various supervised and unsupervised algorithms are implemented on any classification and regression problem. This makes the field of Data Science even more interesting.<\/span><\/p>\n

You can also visit the Advantages and Disadvantages of Machine Learning<\/a> to get a better understanding of Machine Learning.<\/p>\n

Deep Learning –<\/span><\/h3>\n

Deep Learning is a subset of Machine Learning in the sense that it essentially uses Artificial Neural Networks to mimic how a human brain works. The driverless car is a popular example of Deep Learning.<\/span><\/p>\n

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\"Data<\/p>\n

At the same time, this is how the Data Science landscape looks like from a little closer.<\/span><\/p>\n

Mathematics & Statistics knowledge –<\/span><\/h3>\n

A Data Scientist must have a strong foundation of Mathematics (Probability) and Statistics concepts. That is the stepping stone into the world of Data Science.<\/span><\/p>\n

Descriptive, Predictive and Prescriptive Analytics –<\/span><\/h3>\n

All the problems in the realm of Data Science essentially require working on the below areas.<\/span><\/p>\n

When we try to dig deeper into the past data by applying various methods to understand what has happened, that is called\u00a0<\/span>Descriptive Analytics<\/span><\/strong>.<\/span><\/p>\n

When we try to predict the likelihood of any future outcome of a given scenario based on historical data by applying Statistical and Machine learning knowledge, that is called\u00a0<\/span>Predictive Analytics<\/span><\/strong>. Understanding about the future in short.<\/span><\/p>\n

When we try to identify and prescribe the next course of action based on predictive analytics, that is called\u00a0<\/span>Prescriptive Analytics<\/span><\/strong>.<\/span><\/p>\n

Technical Knowledge –<\/span><\/h3>\n

Implementing the theoretical knowledge with technology is the most exciting part. The journey of a Data Scientist generally starts with mastering programming languages like\u00a0<\/span>R<\/span><\/strong>\u00a0and\u00a0<\/span>Python<\/span><\/strong><\/a>. Additionally,\u00a0<\/span>SAS<\/span><\/strong>\u00a0and\u00a0<\/span>Excel<\/span><\/strong>\u00a0are also very popular statistical tools that can be learned. Data Visualization tools like\u00a0<\/span>Tableau<\/span><\/strong>\u00a0and\u00a0<\/span>Power BI<\/span><\/strong><\/a>\u00a0are in high demand being an essential element in the life cycle. Understanding of SQL, NoSQL, etc and\u00a0<\/span>Big Data<\/span><\/strong>\u00a0along with Spark, Scala, Hadoop for Data assembly will be an added advantage.<\/span><\/p>\n

Domain Knowledge –<\/span><\/h3>\n

Since every industry demands a Data Scientist these days, the icing on the cake would be to be an industry-specific Data Science expert. For example Healthcare, Finance, HR, Insurance, Energy and Utility, Defense, Education, Media, etc. Nothing like it.<\/span><\/p>\n

\"DataScience<\/em><\/p>\n

We read about the WHAT of Data Science. Now let us proceed further in this guide and dive into HOW of it.<\/span><\/p>\n

Data Science life cycle
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\"Data<\/p>\n

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Data collected are available in various formats like text, audio, video, images, etc. Additionally, they are either structured or unstructured data. Often, we do not have complete data. It might also happen that the data collected is not even relevant to the problem we are aiming to solve. With all these challenges upfront, a Data Scientist commences the Data Science life cycle for a project and maneuvers through the below steps.<\/span><\/p>\n

1) Business Understanding –<\/span><\/h3>\n

A Data Scientist must view every problem with a lens of business to understand the purpose clearly. Mere application of Data Science techniques does not help.<\/span><\/p>\n

2) Data Acquisition –<\/span><\/h3>\n

The various sources to assemble data and that too relevant data must be clear.<\/span><\/p>\n

3) Data Understanding –<\/span><\/h3>\n

A thorough understanding of the data collected is required. This is to identify if we have assembled all the relevant data required to solve the problem or not.\u00a0<\/span><\/p>\n

4) Data Preparation –<\/span><\/h3>\n

Here comes a very important step in the life cycle. Data needs to be cleaned and prepared in a manner such that machine learning models can be applied to it. To put it another way, it is said that this particular step consumes a lot of time and must be done thoroughly.<\/span><\/p>\n

5) Data Exploration –<\/span><\/h3>\n

Perform Exploratory Data Analysis to identify outliers, hidden patterns, and missing values.<\/span><\/p>\n

6) Data Modeling –<\/span><\/h3>\n

At this moment, we are set to apply machine learning algorithms using R, Python or any other statistical tool.<\/span><\/p>\n

7) Model Evaluation –<\/span><\/h3>\n

Another important step is to evaluate the results fetched from applying various algorithms and recognize the best model which suits the problem.<\/span><\/p>\n

Very well. You now not only know about data but also all the operations performed on it to solve a problem. So what kind of insights you get out of it for which Data Scientists are highly in demand. Let us look at that.<\/span><\/p>\n

The demand for Data Science<\/span><\/strong><\/h2>\n

Suppose you are an Area Manager for Baskin Robbin’s ice creams. You are working on sales strategies for the upcoming summer season in your area. All the piles of data are overwhelming. What if someone comes in and says –\u00a0<\/span>\u201cFor every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%.\u201d\u00a0<\/span><\/em>Would not that be stunning? That is the power of Data Science. It brings insights to the surface which helps make informed and better decisions. And hence the demand.<\/span><\/p>\n

IBM predicts demand for Data Scientists will soar 28% by 2020.<\/span><\/p>\n

“The job of a data scientist has only grown sexier,”<\/span><\/a>\u00a0said Andrew Flowers, an economist at Indeed, based in Austin, Texas, and author of the Indeed report. “More employers than ever are looking to hire data scientists.”\u00a0<\/span><\/p>\n

Furthermore, Forrester Research analyst Brandon Purcell said demand for data scientists will only grow, as organizations increasingly rely on data-driven insights.<\/span><\/p>\n

The rapid rise in the successful use cases of AI and Machine Learning has also increased the demand for Data Science. Some thrilling examples are provided below.\u00a0<\/span><\/p>\n

Various Applications of Data Science<\/span><\/strong><\/h2>\n