What are the core subjects for data science?

Data Science, AI and ML
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The range of subjects which falls under the umbrella of data science ranges from learning the basics of statistics & probability to learning programming language such as SAS, R to finally learning effective communication skills to present the findings to business stakeholders. Therefore, the subjects are quite diverse – still, the core subjects are at least one programming language (preferably Python), Machine learning, statistics & probability, software engineering & finally effective communication.
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In my view core subjects for Data Science include Linear Algebra, Statistics, Machine Learning, Deep Learning and Neural Networks, Artificial Intelligence, Databases, Business Intelligence, Computer Programming, Computer Architecture, Numerical Methods, Parallel Programming, Natural Language Processing(NLP), Computer Vision and Operating Systems. A good data scientists will need some background in all of these areas to be able solve wide variety of data science problems.

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A data scientist is a person with a unique set of talents who can both decipher data and tell a spectacular story with it.

Programing Knowledge: Python, R, and Java are regarded as programming subjects that contribute to implementing data science methodologies. Python is the most recommended subject due to its extensive library and ease of understanding syntax and semantics, data visualization tools, compatibility with Hadoop, which is used to manage big data, and interactive support of the jupyter notebook tool.

Statistical knowledge: In the realm of Data Science, Machine Learning (ML) is all about algorithms, matrices, linear algebra, probability, calculus, Exploratory Data Analysis, and other techniques for extracting data-driven insights and supporting decision making. Data scientists are motivated to work because of machine learning.

These are the two most important topics in data science. Aside from that, SQL (Structured Query Language), Hadoop, Apache Spark, PowerBI, and Machine Learning Model Deployment on GCP are all available (Google Cloud Platform).

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