360DigiTMG – Data science course in Trichy

Introduction

Why Data Analytics?

Suppose there is a mall in a metropolitan area. A customer shows up and asks for a product. The inventory manager needs to search for that particular item, locate its position and then convey this to the customer. If this process is done manually, it takes so much time in a huge mall. Then, the customer will be unsatisfied with the service. If the manager had the information of all the products at his fingertips, i.e. in a machine, then it would take a fraction of a second to negotiate with the customer. 

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What is Data Analytics?

Data analysis is a process of:

• collecting
• inspecting
• cleansing
• transforming
• modeling 

The Data for:

• discovering useful information
• predicting the future trends
• finding the best course of action

Data analytics is the newest, hottest job skill in the last few years. As the data is multiplying due to the eruption of faster internet providers, it also has become important to analyze this data and smoothen the day-to-day experience of consumers. Then it becomes important to operate on this data wisely and effectively. The subject of data analytics is all about such operations on data. Data analytics has three different co-domains, namely descriptive analytics, predictive analytics and prescriptive analytics. 

Descriptive analytics is the simplest analytics. It involves the description of data in tables and graphs. Predictive analytics is a bit more advanced type of analytics. As the name suggests, by seeing the trends and patterns in past information and data, the future trends in data can be predicted using advanced coding tools and statistical skills. Prescriptive analytics is the super set of both descriptive and predictive analytics. It also consists of the decision making and observing the effects of that decision in the future and suggesting the best solution or choice depending on the type of problem. 

Google self-driving cars are the best examples of prescriptive analytics. It involves analyzing the environment and surrounding data variables, making decisions whether to reduce the speed or apply the brakes and also what will happen if brakes are applied, or if just the speed should be reduced.

What actually happens in data analytics?

The very first step is analyzing the problem and deciding the problem statement. Then the main fuel for the process, i.e. the data, is collected from various sources like web servers, logs, databases, API’s, and online repositories. This is also known as data acquisition. The next step involves the data preparation for analysis. It involves data cleaning, which is a very important step in data analytics and data transformation. Talend and Informatica are the tools used for data transformation. 

Then comes the exploratory data analysis that involves the definition of variables and features for the data model development. Then some algorithms like KNN, Naïve Bayes and Decision Tree are applied to the data to decide the perfect data model for it. Following that is testing which involves the process of providing some training datasets to the model and deciding the best out of all. This process is extensively done in Python, R or SAS languages. 

The last, yet very important part, is data visualization. Representing data in visuals for better understanding of clients is very important. Tools like Tableau, Power BI and Qlik View are extensively used for data visualization and communication. 

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