DATA SCIENCE: THE APPROACH TO EFFICIENT DATA

Posted by data science certification on March 13th, 2020

Everywhere we look, we see the data. The data is so vast that you need to know certain architecture to store such a huge amount of data. You also need to know how to handle any failures in the system where the data resides. The answer is Data Science. It is an advanced study of data. It deals with how data can be stored, easily retrieved, accessed and maintained.

There are many concepts in data science. Some of which are:

• Data mining
• Big data analytics
• Artificial intelligence
• Machine learning
• Programming
• Business engineering
• Statistical analysis

Artificial intelligence, big data, and programming deals with how the data is handled. Statistical analysis is a concept of what can be done with the data. Statistics is the most powerful tool in data science. It is used for technical analysis by the use of mathematics.

Statistical features – this is probably the first technique used to find out the details of the datasets. For example, to find the maximum, minimum, average, etc. You need to remember two things.

ü  If the plotted graph is short, it means that the data that is stored is similar.

ü  If the plotted graph is tall, it means the data is quite different from each other.

Probability distributions- probability is the degree of the occurrence of something. In data science, it’s the occurrence of data.

There are various sorts of distributions and a few are listed as follows:

• Normal Distribution: mean and standard deviation takes place primarily here. The mean is used to distribute spatially and standard deviation controls the distribution.
• Uniform Distribution: it consists of 2 values. On and Off. It distributes evenly when it is in “on” state.
• Poisson Distribution: it is similar to normal distribution but there is skewness that is added to it. Skewness is the distortion occurring in the graph of the performed analysis.

We need to always try to reduce the dimensionality of the data to reduce the complexity of storage. That introduces us to the concept of Dimensionality Reduction.

As we can see, there is a wide range of operations to be done while considering data statistically. This might look simple for us as regards easy and simple data. But when it comes to large and complex data, careful measurement of each detail of data has to be considered. It is not like a sport that you learn a few of the rules and starts implementing. It requires continuous learning and applicability in order to iterate the present system efficiently.

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As data is gaining its scope day by day, the need for knowledge and the iterating concept is much required because as the decades increase, data also increases and never goes down. If you build a career where the graph always goes up, so does your job opportunities. For this reason, 360DigiTMG brings up the Data Science Course in Houston to help you build your brighter future on a wide platform of data science.