Week | Topics | Study Materials | Materials |
1 |
Examples, data science articulated, history and context, technology landscape
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2 |
Data Science Tools : Introduction to R basics, installing packages
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3 |
R Data Types and reading data in and writing out, lists, vectors, matrices
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4 |
Control structures and Loops in R Functions and Libraries
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5 |
Control structures and Loops in R Functions and Libraries (Con’t)
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6 |
Data Sources: How to obtain data, transform and manage
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7 |
Midterm Exam
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8 |
Data Preparation with R,Data visualization
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9 |
Statistics with R, random variables
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10 |
Analytics: Topics in statistical modeling: basic concepts, experiment design, pitfalls
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11 |
Linear Models
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12 |
Topics in statistical modeling (Con’t), Regression
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13 |
Exercises with Data Sets
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14 |
Exercises with Data Sets
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15 |
Discussion
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16 |
Final Exam
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