The world of data through the eyes of data science

Posted by Mansdeliansde Amgajdihnfm on April 20th, 2019

Data science also called statistics, data analytics, machine learning is increasingly in demand in the last quarter century, because of huge data collection possibilities and a rise in computational power. As a result, there is a demand for multiple job profiles like engineers, computer scientists, mathematicians, statisticians, etc. Each and every branch of science, engineering, and business is dipped in the technology of data analytics. If you are here, you too are interested in becoming a data science or have an interest in data science.

Technical requirements to become a data scientist

• Programming

The first thing that comes to our mind when reading the word technical is programming. Any technology to be learned is always supported by programming. So, you need to have good knowledge of various programming languages. Some of the most essential are Python, Java, C/C++, Perl, and SQL. All the current data science roles use Python as their primary programming language.

• Knowledge of analytical tools

Valuable insights can be extracted from raw data with the help of analytical tools. Various popular analytical tools are Hadoop, Pig, Hive, Spark, and R.

• Handling unstructured data

Data inflows from various sources in different formats. This data is unstructured data. Handling data of various formats and from multiple resources is the key requirement of a data scientist.

Non technical requirements to become a data scientist

• Communication skills

You understand the data and its insights efficiently if you are a good data scientist. You must also be able to express and explain your understanding of the nontechnical user of the data.

• Strong business understanding

 In order to create a successful business model, you should know how and on what points the business work, what opportunities need to be explored, what potential challenges and problems the business face, etc

• Data intuition

It is one of the most important skills needed. With experience and proper analyzation, one can understand the important and unimportant data. The sixth sense and intuition play an important role here. The human brain is the best reasoning source. The features of data can be understood with the help of these reasoning abilities of a human.

Machine Learning and Data Science

Machine learning is an important part of data science. Machine learning cleanses the data and extracts meaningful information with the help of several statistics and algorithms. Big data is very big in terms of volume and variety and hence it becomes difficult for a data scientist to work on it.

Machine learning helps to solve this problem. Machine learning uses techniques like classification, regression, clustering, and more to create business models.

You can see the use of machine learning and data science in daily life as well. When you watch videos on Netflix or YouTube, you start getting recommendations based on the videos you have watched.

Skills needed to be a machine learning expert are programming languages, statistics, probability, modeling skills.

Data science includes the aspects of machine learning for its underlying functionality. Machine learning, data analytics, artificial intelligence are all interrelated to each other.

Resource box

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