Introduction to Data Science

What is Data Science?

Data Science as the name suggests is a field of science that deals with data. It combines the power of computers and mathematics for analyzing data, extracting important information from it and process this information for getting a useful output.

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How can we use Data Science?

There are two ways in which we can use data science:

1. Finding a solution to a problem by analyzing the data.
2. Analyzing the data and come up with new ideas that can be implemented or come up with new problems that can be solved with it.

Classifications of Data Science

Data Science can be classified into the following:

1. Data Collection
2. Data Analysis
3. Data Visualization

We will take a brief look at each of these three…

Data Collection

In philosophy, we call the things that are known or are assumed as facts which makes the basics of reasoning and calculation as DATA. Collecting data has been one of the most common things that humans have been doing for ages.

Our ancestors used to collect data in rocks and stones for remembering the number of their cattle or to create memories about their life or the knowledge they have gained which they wanted to pass on to the next generation.

In the modern world, the basic purpose of collecting data is for using it to find solutions to existing problems.

We collect data mainly in these different forms like:

• Sound data 
• Visual data
• Text data

Types of data

The two main types of data are:

Structured data

Structured data is information that is organized. For example, a data set which contains names and roll numbers in two different column.

Unstructured data

These are a collection of information that is not processed. Examples are IoT sensor data, emails, chats, etc.

Data Analysis

Now that we have collected the data, for finding the solution to the problem that we have, we need to analyse the data.

The process of analyzing data using different tools like R, Python, MATLAB, etc. (We can use the libraries available in these programming languages for analyzing data by plotting graphs or charts) is called data analysis.                                     

For example, consider the problem of housing price prediction. Imagine we have a dataset containing the prices of houses over the past 10 years. We would like to predict the price of the house in the coming year using this data.

One way we could do this is by plotting a graph where on the x-axis we give the years and, on the y-axis, we give the price of houses. When we plot the data like that, we would be able to see a pattern in which the prices of the house are increasing or decreasing over time.

And now by using this trend we would be able to predict the possible increase in price for a house in the coming years.

Data Visualization

Data visualization is a tool that is used to explain the data using graphical representations of the data. It helps the data analyst to understand different patterns in data and outliers and trends in data.

Also, the data analyst can use the visualization techniques to present his findings to the customer in the form of graphs, charts and maps.

Some of the different libraries in python for data visualization are:

1. Plotly
2. Seaborn
3. Ggplot
4. Altair
5. Matplotlib
6. Bokeh
7. Folium

If we are not using a programming language for visualization, we can use below tools:

1. Google charts
2. Tableau
3. Xplenty
4. Hubspot
5. Whatagraph

Data visualization example