Data Science

Data Science is a discipline that combines mathematics, statistics, specialized programming, machine learning, visualization, and technical communication skills to design methodologies that allow the uncovering of actionable insights that may not be visible in plain sight.

This section will discuss the different Data Science roles, content related to different Data Science methodologies, the most commonly used tools, various Guided Projects & examples, and more.

We live in an era where reliable data has become an invaluable asset. We’re constantly bombarded with information from…
In today’s world, we’re constantly being bombarded with information from countless sources – news outlets, social media, blogs, etc….
Machine learning has revolutionized the way we approach problem-solving and decision-making. As a subset of artificial intelligence, it has…
Data science has its roots in statistics, computer science, and data analysis in the 1960s. It has since evolved…
In the last segment of this 5-piece Portfolio Project, we discussed what sentiment analysis is and the types of approaches for…
Sentiment analysis is a Natural Language Processing (NLP) technique which consists of identifying the emotional tone behind a body…
Of all the libraries belonging to any Data Scientist’s toolbox, Pandas may be the most important one; it’s built on top…
In the last part of this 3-segment Guided Project, we introduced the concept of Exploratory Data Analysis (EDA). We…
Exploratory data analysis (EDA) is a scientific technique developed by the mathematician John Tukey in the 1970s widely used in…

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