Data Coursework

Data Coursework-83
Data Mining: This course shows you how to discover patterns in structured data.You’ll also learn to retrieve information from unstructured data sources, such as natural language text.

Data Mining: This course shows you how to discover patterns in structured data.You’ll also learn to retrieve information from unstructured data sources, such as natural language text.

Other relevant 500-level coursework may be taken for elective credit, with approval of the program director.

Students must take a total of at least 9 credits of MATH coursework and at least 9 credits CS/CSP coursework to graduate, not including the capstone practicum course.

They were no longer just crunching numbers; they were involved in designing the mechanisms for collecting the information and helping business executives and government leaders figure out how to interpret it– and data science was born.

At the University of Michigan, incoming professor C. Jeff Wu took a look at the field in 1997 and decided that it had broadened to encompass three overlapping, interdisciplinary pursuits: He dubbed the new field “data science” and called for the creation of masters and doctoral programs to offer degrees in it, with courses covering not just statistics, but the other disciplines required as well.

Many bridge courses are regular college classes, part of existing graduate or undergraduate programs at the institution where they are offered.

Bridge courses typically take a full quarter or semester to complete and students are charged the regular fee for the credits.

Candidates who are weak in basic computer science or statistics may decide to take Massive Open Online Courses (MOOCs) or enroll in a data science bootcamp to help brush up on those skills.

Both these options allow a greater degree of flexibility in preparation: However, the education gained in a MOOC or bootcamp is less certain preparation than that of a bridge course.

Bridge courses for data science masters degree programs are typically oriented around covering one of two possible deficiencies in a candidate’s educational background: Candidates with prior math or engineering education may have a good grounding for the statistical concepts they will encounter in a data science masters program, but not enough programming experience to keep up with the modeling and analysis aspects.

Conversely, those with a computer science background may have the coding chops to keep up with the modeling and scripting, but lack the math background to understand how to design the models.

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