Data Science
Program Details
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Data Science Course Curriculum
The Lessons
- Basic R programming knowledge.
- How to use statistical ideas like probability, inference, and modeling in the real world.
- Learn how to use the tidyverse, including ggplot2 for data visualization and dplyr for data manipulation.
- Get to know fundamental tools for working as a data scientist, such as RStudio, git and GitHub, and Unix/Linux.
- Apply machine learning techniques.
- A thorough understanding of the core ideas in data science through inspiring real-world case studies.
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About your Data Science Course
The need for knowledgeable data scientists is expanding quickly in business, academia, and government. You are equipped with the intellectual background and practical abilities you need to take on real-world data analysis difficulties thanks to the HarvardX Data Science program. In addition to teaching you R programming, data manipulation with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document creation with RStudio, the program also covers concepts like probability, inference, regression, and machine learning.
Real Life Case Studies
We pose specific questions in each session, use relevant case studies, and learn by analyzing data to find the answers. Case studies include Forming a Baseball Team (inspired by Moneyball), Movie Recommendation Systems, US Criminal Rates, The Financial Crisis of 2007–2008, and Trends in World Health and Economics.
The R software framework will be used throughout the course. R, statistical ideas, and data analysis methods will all be covered at the same time. We think that learning how to address a particular problem helps you remember R knowledge.
A variety of techniques and algorithms are used in the discipline of data science to extract meaningful insights from unstructured data. It uses a variety of techniques for data modeling and other data-related tasks like data erasure, preprocessing, and analysis. Big Data refers to the massive volume of organized, unstructured, and semi-structured data that is produced through numerous organizations and channels. Giving operational insights into complicated business scenarios is one of the duties of data analytics. This foretells future chances that the organization can take advantage of.
Data science heavily relies on statistics. It is one of the most crucial disciplines to offer tools and techniques to uncover structure in data and to provide a deeper understanding of it. It greatly influences the collection, exploration, analysis, validation, etc. of data.
A variable’s hypothetical values can be estimated using extrapolation or interpolation based on other observations. In a sequence of values, interpolation is the estimation of a value between two known values, and extrapolation is the estimation of a value by extending a known sequence of values or data beyond the region that is unquestionably known.
Python would be the better option between R and other programming languages because it features the Pandas module, which offers fast data analysis tools and simple data structures. Nevertheless, depending on the intricacy of the data being examined, you can use either of these languages.
One of the most extensively used uses of machine learning in organizations is recommendation systems. A user can interact with a variety of products thanks to this system. Although contemporary recommenders include both strategies, the machine learning techniques used in recommender systems are often divided into two categories: content-based and collaborative filtering methods. While collaborative approaches derive similarity via interactions, content-based techniques are based on similarities in item properties.
