Data Science blends multiple disciplines such as machine learning, algorithms, data inference, programming, mathematics, and statistics to extract useful information that solves complex problems.
The Forbes article ‘6 Predictions About Data In 2020 And The Coming Decade’ published in 2020 urged the importance of data science in managing the 44 zettabytes of data generated by the end of the year. It claimed that data science would be necessary to make that information useful and applicable in practical scenarios. However, one needs to be familiar with a few data science prerequisites to become a successful data scientist.
The Five Data Science Prerequisites
The five essential data science prerequisites needed to start your journey are
Leading companies like Facebook, Google, Uber, etc have made the importance of machine learning in today’s world very clear. These companies have made machine learning a core part of their companies. ML has also become a significant competitive differentiator for many other companies providing a view of customer behaviour patterns and relevant business strategies accordingly.
Successful data scientists need to have a solid understanding of machine learning with a basic knowledge of statistics.
Data modeling is the method of producing a descriptive diagram of relationships between various types of data and information. An essential aim of data modeling is to create efficient methods for storing this information and enabling complete access and reporting.
Data modeling enables quick calculations and predictions possible from the available data. Its ability to identify algorithms best suited for solving problems makes it an essential part of data science.
One of the fundamental tools of data science is statistics. Data scientists need to analyze and prepare reports from large amounts of structured and unstructured data. Statistical data science can identify patterns and relevant information within the data. The identified patterns become practical and generate value for a business when applied to industry-specific strategies. Thus, a good understanding of statistics can achieve good results for a data scientist.
Programming is needed to perform data science tasks and projects successfully. It is necessary to learn because programming languages assist the machine learning aspect of data science. Data scientists most commonly use Python and R as their programming languages. Python is the more popular programming language in data science because the language is easy to learn. Supporting multiple data science and machine learning libraries also add to its popularity.
Data scientists need to be capable of reading databases. Understanding how databases work, managing them, and extracting their data are essential skills for a data scientist.
Many database platforms are modeled after SQL making it the standard for many database systems. This makes SQL the most widely used programming language while working with database-related issues. Thus, learning SQL for data science aspirants is very important.
Data science has become an essential part of many industries. The massive amounts of data that is generated has made it one of the hottest topics in IT. And the growing popularity has encouraged companies to start implementing data science techniques to grow their business.
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