Introduction to Data Science



Milind Kulkarni, Associate Professor in the School of Electrical and Computer Engineering

Course Outcomes

A student who successfully fulfills the course requirements will have demonstrated the abilities to write data analyses in Python, to build statistical models and use them for prediction, and to design analyses/models to solve engineering problems.

After taking this course, you will be able to 

  • Explain data analysis and modeling algorithms like sampling, estimation, and regression; write basic data analyses in Python, taking advantage of language features such as higher-order functions (map/reduce) and complex data structures (including NumPy arrays); 
  • Use tools to propose, design, and implement a set of data analyses to solve engineering problems, visualize and present the results.

Course topics include 

Python basics (loops, functions, arrays, lists); histograms; higher-order functions, closures, map/reduce; distribution; N-grams; estimation: sampling, mean, variance, confidence intervals, significance tests; numpy arrays and matrices; linear regression and prediction; dealing with missing data; classification; clustering; and neural networks

CEUs: 1.5

For further information and to register, please email