Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.
It is a multidisciplinary field that uses tools and techniques to manipulate the data so that you can find something new and meaningful.
Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence. More additional information on Data Science Online Training
Data Science Requirement:
A few years back, data was scarce and mostly available in a structured form, which could be easily stored in Excel sheets, and processed using BI tools.
Now, let us understand what are the most common types of problems in data science and how to solve the problems. So in data science, problems are solved using algorithms, and below is the diagram representation for algorithms applied to possible questions:
Is it A or B? ,
We can refer to this type of problem as having only two definite solutions like yes or no, 1 or 0, may or may not be. And this type of problems can be solved using classification algorithms.
Is it different? ,
We can refer this type of question which deals with different pattern, and we need to find odd one among them. These types of problems can be solved using anomaly detection algorithms.
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How much or how much
There is another type of problem that asks for numerical values or figures such as what is the time today, what will be the temperature today, can be solved using regression algorithms.
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How is it organized?
Now if you have a problem that needs to deal with the organization of data, it can be solved by using clustering algorithm.
Clustering algorithms organize and group data based on features, colors or other common characteristics.
data science lifecycle
The life-cycle of data science is explained in the form of diagram below.
life cycle of data science
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The main stages of the data science life cycle are given below:
1. Search: The first step is search, which involves asking the right questions. When you start a data science project, you need to determine what the basic requirements, priorities, and project budget are. In this step, we need to set all the requirements of the project such as number of people, technology, time, data, an end goal, and then we can frame the business problem at the first hypothesis level.
2. Data preparation: Data preparation is also known as data munging. In this step, we need to do the following:
data cleansing
lack of data
data integration
data transformation,
After doing all the above operations, we can easily use this data for our further processing.
3. Model Planning: In this step, we need to determine the various methods and techniques to establish the relationship between the input variables. We will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools to understand the relationships between variables and to see what data can inform us. Common tools used for model planning are:
SQL Analysis Services
R
mother-in-law
Python
4. Model Building: The process of model building begins in this phase. We will create dataset for training and testing purpose. We will apply various techniques such as association, classification and clustering to build the model.
The following are some common model building tools:
SAS Enterprise Minor
WEKA
SPCS Modeler
Meaning
5. Operations: In this phase, we will deliver the final report of the project with briefing, code and technical documents. This step gives you a clear overview of the entire project performance and other components on a small scale before full deployment.
6. Communication Outcome: In this step, we will check whether we reach the goal that we have set in the initial phase. We will communicate the findings and final result with the business team.
Applications of Data Science:
Image Recognition and Speech Recognition:
Data science is currently using for image and speech recognition. When you upload an image to Facebook and you get suggestions to tag your friends. This automatic tagging suggestion uses image recognition algorithms, which are part of data science.
When you say something using “Ok Google, Siri, Cortana” etc., and these devices respond according to voice control, it is possible with speech recognition algorithm.
Gaming world:
The use of machine learning algorithms is increasing day by day in the gaming world. EA Sports, Sony, Nintendo, are extensively using data science to enhance the user experience.
Types of Data Science Job
If you learn data science, then you get the opportunity to find the various exciting job roles in this domain. The main job roles are given below:
Data Scientist
Data Analyst
Machine learning expert
Data engineer
Data Architect
Data Administrator
Business Analyst
Business Intelligence Manager
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