Subject: Subject Sylbus: Introduction to Data Analysis in Python - 099102

Introduction to Data Analysis in Python - 099102
Credit
Points
2.0
 
Given In
Semester
a
 
  Lecture Exercise Laboratory Project or
Seminar
House
Work
Weekly
Hours
2        

Determination of the grade according to progress during the semester and the submission of the final thesis


Prerequisites: Statistics for Managers 098740


Python. Our Focus Will Be on Classification Algorithms (Svm, Logistic Regression), Regression Algorithms (Linear Regression, Lasso), and Ensemble Methods (Random Forest), in Addition to Model Evaluation Techniques. Moreover, We Will Explore the Software Tools That Consitute the Python Data Science Ecosystem. This Course Introduces Students to Machine

Learning Outcomes
At the End of the Course the Student Will Know to Write Basic Code in Python. Use Standart Data Science Packages in Python, E.G. Pandas, Numpy and Scikit-Learn. Conduct Exploratory Data Analysis. Employ Predictive Algorithms for Regression and Classification. Understand and Analyze the Challenges in Model Evaluation.




Textbooks
PublishedPublisherAuthorsBook
2016o'reilly mediaj. vanderplaspyton data science handbook: essential tools for working with datap

Created in 06/03/2021 Time 01:36:38