Course Learning Outcome: (at the end of the course, student will be able to do:)

CLO1 To demonstrate proficiency with statistical analysis of data.
CLO2 To develop the ability to build and assess data-based models.
CLO3 To apply data science concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively

Lecture Slides

Week 1_L1_Fall2024.pptx

Week 1_L2_Fall2024.pptx

Week 2_Lesson 1.pptx

Week 2_Lesson 2.pptx

Week3_Lesson 1.pptx

Week3_Lesson2.pptx

Week 4_Lesson 1.pptx

Books

Textbook:

  1. Kotu, V, and Deshpanda, Data Science: Concepts and Practice, Morgan Kaufmann (2019).

  2. Igual, L. and Segui, S, Introduction to Data Science, A Python Approach to Concepts, Techniques and Applications, 2 eds, Sringer (2024).

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Reference Books:

  1. Devore, J. L., Probability and Statistics for Engineering and the Sciences. 9 eds, Cengage Learning (2015)

    th

  2. Lantz, B. Machine Learning with R, 3 eds, Packt Publishing (2019)

    rd

  3. Dunham, M, H; Data Mining, Prentice Hall (2003).

  4. Lind, D. A. et al. Statistical Techniques in Business and Economics, 18 eds, McGraw-Hill (2021)

    th

7_Laura_Igual,_Santi_Seguí_Introduction_to_Data_Science_A_Python.pdf

8.Dunham - Data Mining.pdf

9_Brett_Lantz_Machine_learning_with_R_Packt_Publishing_2013.pdf

10_Statistics_for_Biology_and_Health_David_G_Kleinbaum,_Mitchel.pdf