Data science as a concept came up after the internet boomed and was filled with tons of junk data, which later went on to be termed as big data. To combat the overwhelming amount of data that was available online and to help make more sense of the information online, which was causing a huge amount of distress to go through when there was essential information needed to be found, data science came up as a necessity.
What data science is?
In essence, data science is the blend of statistics, machine learning, data analysis and other similar methods in order to comprehend and compile data into a more meaningful manner that is better suited to be understood. Data scientists are required to sort through structured and unstructured data to acquire necessary information.
What do they have in their skill set?
Data scientists tend to be highly educated individuals. An estimate of eighty eight percent have a masters degree while forty eight percent have a doctorate. It has become important to have a good education to develop the required deep understanding of a variety of topics. Most data scientists are well versed in the usage of Hadoop and big data queries.
What is required of them?
A large part of their job is to create algorithms that help in sorting and pulling out the data while figuring out patterns to make the piles of data more meaningful and presentable. Hence, it becomes really important to know, and be, extremely versatile in coding. A few languages that are absolutely necessary are:
Python
Python is among the most capable languages. When combined with languages like Java, C and C++, python becomes extremely adaptive and suitable to use. Python is by far the most used tool needed by a data scientist.
Hadoop (Big Data) platform
Albeit not being an absolute requirement everywhere, Hadoop could be very required in the field in multiple situations, especially when your system cannot handle the amount of incoming data. Hadoop can be used in such events. Hadoop can also be used for data sampling, filtering and summarizing of the acquired data.
SQL
SQL was designed to assist a user in communicating and accessing data. SQL aids a user by providing insight on the database when a query is pressed. It uses concise commands that save time and decrease the quantity of coding. It also helps by giving one a better understanding of a database and improves one’s reputation as a data scientist.
Machine learning and Artificial Intelligence
To stand out as a prolific data scientist being well versed in machine learning is an essential. Knowing the techniques like logistic regression, decision trees, etc. can aid the process. There are very few competent individuals who are well versed in time series, supervised and unsupervised machine learning, outlier detection, survival analysis, computer vision, etc.
Unstructured data
A data scientist should be able to work with unstructured data. This comprises data that has undefined content and doesn’t fit databases like videos, blog posts, etc.
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