Chapter I - The Beginning

Data science, in basic understanding, is the place where voluminous data meet insights. At present, roughly around 2.5 Quintilian bytes of data is generated everyday. This data is of little significance until we can extract meaningful information from them. The insights drawn from the data available, can be leveraged to make informed decisions.

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Few varied applications of data science are - 

1. Companies analyzing the customer data to understand market preferences

2. Bank drawing insights from customer transactions to understand probability of churn
3. Teams analyzing the players statistics to improve team dynamics
4. Google making use of data science technology to deliver best search results
5. E-commerce websites making use of recommender systems to attract customers
6. Facebook making use of face recognition algorithm to enable 'tag your friend' feature and many more

The data available in the ecosystem can be in structured or unstructured form. Unstructured/Unorganized data can be in the form of text and multimedia content. E-mail messages, word documents, audio files and web pages are some of the examples. Structured data, on the other hand, are organised data that can be easily read. Examples of structured data includes data stored in databases, excel files and csv files.

Analyzing data is a comprehensive process. One must dive deep into the data pool to extract interesting and useful insights. Data processing is an extensive and an iterative process which can lead us to meaningful interpretations.

Steps that are commonly followed during data analysis are -
1. Data gathering
2. Data understanding
3. Data cleaning
4. Exploratory Data analysis to draw insights
5. Machine Learning (advance analytics)


P.S - We will discuss all the steps in detail in upcoming blogs