**Data Science Overview**

The simplest Data Science meaning would be, applying some scientific skills on top of data so that we can make this data talk to us.

Now, what exactly mean by ‘apply scientific skills on top of data’? Well, to put it precisely, Data Science is an umbrella term that encompasses multiple skills and scientific techniques.

Techniques which Data Science comprises are:

When we combine all of these scientific skills into one, what we get is nothing but Data Science. Now, let’s go ahead and have a look at these different scientific techniques in this blog on ‘Introduction to Data Science’.

**Watch Data Science Online Course video by Intellipaat:**

**Go through the Data Science Course in Hyderabad to get a clear understanding of the Data Science Technique.**

**Data Visualization**

We’ll start with data visualization. Data visualization is an essential component of a Data Scientist’s skill set. So, in simple terms, data visualization can be considered an amalgamation of science and design in a meaningful way.

**Data Manipulation**

Next technique in Data Science is data manipulation.

Normally, the raw data which we get from different sources is extremely untidy, and drawing inferences from this untidy data is too difficult. This is where data manipulation comes in. Data manipulation techniques help us refine the raw data and make it more organized so that finding insights from the raw data becomes easy.

### Watch this Data Science for Beginners Tutorial video

**Interested in learning Data Science? Click here to learn more about this Data Science Training in Bangalore!**

**Statistical Analysis**

Next up in this blog on ‘Introduction to Data Science’ is statistical analysis.

Simply put, statistical analysis helps us understand data through mathematics, i.e., these mathematical equations help in understanding the nature of a dataset and also in exploring the relationships between the underlying entities.

**Machine Learning**

Finally, we have Machine Learning.

Machine Learning is a sub-field of Artificial Intelligence, where we teach a machine how to learn on the basis of input data. This is where we build scientific models for the purpose of prediction and classification.

Now that we have properly understood the Data Science meaning, it’s time to look at the life cycle of Data Science in the below section: ‘Life Cycle of Data Science’.

**Become Master of Data Science by going through this online Data Science course in Toronto.**

**Life Cycle of Data Science**

Let’s look at the stages involved in the life cycle of Data Science.

- Data Acquisition
- Data Pre-Processing
- Model Building
- Pattern Evaluation
- Knowledge Representation

Now, let’s go ahead and understand each of these stages in detail.

*Learn more about the basics of Data Science in the Data Science Tutorial!*

**Data Acquisition**

We already know that data comes from multiple sources and it comes in multiple formats. So, our first step would be to integrate all of this data and store it in one single location. Further, from this integrated data, we’ll have to select a particular section to implement our Data Science task on.

So, in this step, we are acquiring data.

**Learn complete Data Science Course at London in 40 Hrs.**

**Data Pre-processing**

Once the data acquisition is done, it’s time for pre-processing. The raw data which we have acquired cannot be used directly for Data Science tasks. This data needs to be processed by applying some operations such as normalization and aggregation.

**Prepare yourself for the Top Data Science Interview Questions and Answers Now!**

**Model Building**

Once pre-processing is done, it is time for the most important step in the **Data Science life cycle**, which is model building. Here, we apply different scientific algorithms such as linear regression, k-means clustering, and random forest to find interesting insights.

**Are you interested in learning Data Science courses from Experts?**

**Pattern Evaluation**

After we build the model on top of our data and extract some patterns, it’s time to check for the validity of these patterns, i.e., in this step, we check if the obtained information is correct, useful, and new. Only if the obtained information satisfies these three conditions, we consider the information to be valid.

**Knowledge Representation**

Once the information is validated, it is time to represent the information with simple aesthetic graphs.

Thus, we conclude this comprehensive introduction to Data Science.

**If you have any doubts or queries related to Data Science, do a post on Data Science Community.**