AID201 Programming for Data Science

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
AID201 Programming for Data Science 3 2 6 Thursday 15:00-17:50
Prerequisite None It is a prerequisite to

None

Lecturer Emine Yaman Office Hours / Room / Phone
Monday:
12:00-15:00
Wednesday:
12:00-15:00
Thursday:
10:00-12:00
B F2.7C
E-mail eyaman@ius.edu.ba
Assistant Nedim Kunovac Assistant E-mail nkunovac@ius.edu.ba
Course Objectives The objectives of this course are:
1. To introduce the concepts and applications of data science and Python
2. To teach the basics of Python programming and data structures
3. To teach how to use various Python libraries and tools for data science
4. To teach how to perform data analysis, visualization, machine learning, and deep learning tasks with Python
5. To teach how to apply Python skills to real-world data science projects and challenges
Textbook McKinney, W. (2018). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2nd ed. O'Reilly Media. VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media.
Additional Literature
  • Grus, J. (2019). Data Science from Scratch: First Principles with Python. 2nd ed. O'Reilly Media.
  • Klosterman, S. (2019). Data Science Projects with Python: A Case Study Approach to Gaining Valuable Insights from Real Data with Machine Learning. Packt Publishing.
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. understand the definition and scope of data science and Python
  2. write clear and efficient Python code for data science
  3. use various Python libraries and tools for data science
  4. perform data analysis, visualization, machine learning, and deep learning tasks with Python
  5. apply Python skills to real-world data science projects and challenges
Teaching Methods There will be a 1 hour of theory and explaining the background of the topic, then we will continue with the programming and practice.
Teaching Method Delivery Face-to-face Teaching Method Delivery Notes
WEEK TOPIC REFERENCE
Week 1 Introduction to course, installing Anaconda and interface, variables, numbers and Boolean values, strings, arithmetic operators, indexing, conditional statements Chapter 1,2
Week 2 Defining functions, conditional statements and functions, built in functions, sequences, lists, tuples, dictionaries, loops Chapter 3
Week 3 Introduction to Numpy, Numpy arrays, array indexing, operations in Numpy Chapter 4
Week 4 Introduction to Pandas, series, data frames, missing data, merging, joining and concatenating, operations, input-output Chapter 5
Week 5 Real life exercices
Week 6 Matplotlib and data visualization - Learn how to use Matplotlib pyplot module and object-oriented interface to create and customize various types of plots in Python . Learn how to use Matplotlib features such as axes, labels, titles, legends, annotations, colors, styles, grids, subplots, and interactive plots . Learn how to use Matplotlib to visualize different kinds of data such as histograms, bar charts, pie charts, line charts, scatter plots, box plots, etc Chapter 9
Week 7 Seaborn and Advanced Data Visualization - Learn how to use Seaborn library to create high-level statistical data visualization in Python . Learn how to use Seaborn features such as themes, palettes, facets, grids, distributions, regressions, categorical plots, etc. Learn how to use Seaborn to visualize different kinds of data such as correlations, distributions, relationships, comparisons, etc. Chapter 9
Week 8 Midterm Exam
Week 9 Data Cleaning and Preparation-Understanding data set, checking properties of dataset with main functions, missing values and possible solutions, outliers and possible solutions, visualization of missing values Chapter 7
Week 10 Linear Regression-Training a Linear Regression model, train test split, creating and training the model, model evaluation Chapter 5
Week 11 Logistic Regression-Exploratory data analysis, building a Logistic Regression model, train test split, training and predicting, model evaluation. Chapter 5
Week 12 KNN Algorithm-Standardize the variables, train test split, building KNN, predictions and evaluations, choosing a K value, model evaluation. Chapter 5
Week 13 Decision Trees- Exploratory data analysis, train test split, building a decision tree model, prediction and evaluation, tree visualization. Chapter 5
Week 14 Real life exercices
Week 15 Presentations of projects
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam 1 35 1,2,3,4,5
Semester Evaluation Components
Midterm 1 30 1,2,3,4,5
Homeworks 5 5 1,2,3,4,5
Term project and presentation 1 15 1,2,3,4,5
Lab assignments 10 15 1,2,3,4,5
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture hours 3 14 42 Homeworks 3 5 15
Active labs 2 10 20 Home study 2 14 28
Midterm exam study 15 1 15 Final exam study 15 1 15
Term project/presentation 15 1 15
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 18/10/2024

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