Here are some skills you need to become a Data Scientist:
Python Coding
Python is a general-purpose programming language. Python coding can be used in many things like web development, AI, machine learning, operating systems, mobile application development, and video games. It is easy to learn because the syntax is easy and the code is readable. You can also learn Python on your own. Python coding is important to learn to become a data scientist because it has many useful and easy-to-use libraries like pandas, Numpy, sciPy, TensorFlow, and many more.
Hadoop Platform
Hadoop is a must for data scientists. The major functionality of Hadoop is to hold the big data. It also allows the users to store all forms of data, both structured data and unstructured data. Hadoop also gives modules like Pig and Hive for the examination of large-scale data. It is an open-source software framework that provides the processing of large data sets across clusters of computers using simple programming models.
SQL Database/Coding
SQL Database is an extremely significant tool in a data scientist’s toolbox since it is crucial in accessing, revising, injecting, utilizing, and amending data. It helps in conveying with relational databases to be eligible to know the dataset and use it properly. Everyone thinks python is the most important, but without SQL Database, data science is meaningless. It is a programming language used for querying and managing data in relational databases. It is one of the most requested skills in data science.
Apache Spark
Apache spark supports many languages and permits the developers to write applications in Java, Scala, R, or python. It reserves the data in the RAM of servers which allows quick access and in turn stimulates the speed of analytics. Apache spark is a set of archives for parallel data processing on computer assortments and, at the time of this writing, it is the most vigorously refined open-source engine for this task.
Machine Learning and AI
Machine Learning and AI are the set of algorithms that enable machines to understand and analyze the correlations between various data elements. Machine Learning helps to access large slabs of data, reducing the tasks of data scientists in an automatic process, and is achieving a lot of prominence and recognition. AI is a variety of technologies that excel at removing insights and structures from large sets of data, then making predictions based on that information. Machine Learning and AI are important skills for data science.
Data Visualization
Data visualization is the demonstration of data in a pictorial or graphical structure. It allows decision-makers to see analytics demonstrated visually so they can understand difficult concepts or recognize new patterns. Data visualization is important for adequate analysis, immediate action, observing patterns, revealing errors, comprehending the story, analyzing business insights, understanding the latest trends. Data visualization is another important skill to be a data scientist.
Unstructured Data
Unstructured data is a form of data that is usually not as effortlessly searchable, involving formats like audio, video, and social media postings. A data scientist must be capable of work with unstructured data. Because of its complexity, unstructured data is referred to as dark analytics. A data scientist must have the skill to understand and utilize unstructured data from various platforms.
: By- Sananda Kumari
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