Columbia Engineering Applied Data Science – 3 Months Online


Job Overview

Data has been called the new global currency, and its meteoric rise is transforming entire industriesand driving the demand for practitioners who can wield its power. From health care and finance to entertainment, cybersecurity and beyond, the need for data scientists continues to grow in tandem with opportunities for career advancement within the field. To help fill this talent gap and further the use of data science to solve real-world problems, Columbia Engineering Executive Education has partnered with Emeritus to create the Applied Data Science course. Since this course requires an intermediate knowledge of Python, you will spend the first part of this course learning Python for Data Analytics taught by Emeritus. This will provide you with the programming knowledge required to do the assignments and application projects that are part of the Applied Data Science course. Program Overview Explore the theory, languages and concepts of this in-demand field while acquiring the Python programming knowledge you need to solve real-world data challenges. At the end of the course, you will be able to: – UTILIZE Python programming language to code your own algorithms or analytical models to examine data. – MANAGE and manipulate a large amount of data using Python packages. – REVEAL hidden yet important characteristics of any dataset using visual tools already built into Python. – FORMULATE explanations for past events and determine if it is true using available data. – DISCOVER hidden trends and draw useful insights using software packages designed for use with Python, like NumPy and Pandas – UNEARTH quality information from analysis of text-data like Facebook or Twitter posts and comments – CLASSIFY data points in a larger dataset; for example, assigning genres to one billion songs. – IDENTIFY relationships between data points to form groups in a larger data set; for example, grouping customers into segments using their previous buying patterns. –

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