AID101 Fundamentals of Data Science
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Course Code  Course Title  Weekly Hours*  ECTS  Weekly Class Schedule  
T  P  
AID101  Fundamentals of Data Science  3  2  6  
Prerequisite  None  It is a prerequisite to  None 

Lecturer  Fahir Kanlic  Office Hours / Room / Phone  
fkanlic@ius.edu.ba  
Assistant  Assistant Email  
Course Objectives  The course will equip students with theoretical and practical knowledge, including technical skills related to data science as a rapidly growing field by using popular programming language. It will introduce students to the latest concepts, principles and tools of data science (including data types, data structures, data manipulation techniques), data modelling, data visualization, machine learning algorithms and techniques, etc. The course emphasizes a handson approach to learning data skills, offering a number of interactive exercises by using reallife datasets from a variety of disciplines with the aim of applying many techniques and concepts. By the end of the course, students will improve their theoretical and practical knowledge. Students will gain the skills to apply specific analytics tools and interpret solutions to many problems. By the end of this course, students will be able to: 1. Understand the main steps and challenges of a data science project 2. Apply appropriate methods and tools to collect, clean, explore, and visualize data 3. Perform basic statistical analysis and hypothesis testing on data. 4. Implement and evaluate common machine learning algorithms for classification and regression 5. Communicate and present data science results effectively and responsibly 

Textbook  1. Grus, J. 2019, Data Science from Scratch: First Principles with Python, 2nd edition, O'Reilly Media 2. McKinney, W. 2017, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd edition, O’Reilly Media  
Additional Literature 


Learning Outcomes  After successful completion of the course, the student will be able to:  


Teaching Methods  Combination of lectures (theory and explaining the background of the topic) and practical exercises (practical work by programming and practicing by using the learned algorithms to a realworld dataset)  
Teaching Method Delivery  Facetoface  Teaching Method Delivery Notes  
WEEK  TOPIC  REFERENCE  
Week 1  Introduction to Data Science  Define data science and its applications  Explain the data science workflow  Identify the types and sources of data  Install and use Python and Jupyter Notebook  
Week 2  Data Collection and Wrangling  Use Python libraries to read, write, and manipulate data  Perform data cleaning and preprocessing  Handle missing values and outliers  Apply web scraping and APIs to collect data from the web  
Week 3  Data Exploration and Visualization  Use descriptive statistics to summarize data  Use Python libraries to create various types of plots  Explore the distribution, correlation, and relationship of data  Apply dimensionality reduction techniques to reduce data complexity  
Week 4  Statistical Inference and Hypothesis Testing   Understand the concepts of population, sample, parameter, and statistic  Apply sampling methods and calculate sampling errors  Perform hypothesis testing and confidence intervals  Interpret pvalues and significance levels  
Week 5  Linear Regression   Understand the concept of linear regression and its assumptions  Implement simple and multiple linear regression using Python  Evaluate the performance and accuracy of linear regression models  Identify and handle multicollinearity, heteroscedasticity, and nonlinearity  
Week 6  Logistic Regression   Understand the concept of logistic regression and its applications  Implement logistic regression using Python  Evaluate the performance and accuracy of logistic regression models  Use confusion matrix, ROC curve, and AUC to measure classification performance  
Week 7  Classification Algorithms   Understand the concept of classification and its applications  Implement knearest neighbors (KNN), decision trees, and random forests using Python  Compare the advantages and disadvantages of different classification algorithms  Tune hyperparameters and optimize classification models  
Week 8  Clustering Algorithms   Understand the concept of clustering and its applications  Implement kmeans, hierarchical clustering, and DBSCAN using Python  Compare the advantages and disadvantages of different clustering algorithms  Evaluate clustering results using internal and external metrics  
Week 9  MIDTERM  
Week 10  Association Rule Mining   Understand the concept of association rule mining and its applications  Implement Apriori algorithm using Python  Interpret association rules using support, confidence, lift, and leverage  Apply association rule mining to market basket analysis  
Week 11  Text Mining   Understand the concept of text mining and its applications  Perform text preprocessing using Python (tokenization, stemming, lemmatization)  Apply bagofwords (BOW) and term frequencyinverse document frequency (TFIDF) to represent text data  Implement sentiment analysis using Python  
Week 12  Natural Language Processing (NLP)   Understand the concept of natural language processing (NLP) and its applications  Apply regular expressions to extract information from text data  Implement named entity recognition (NER) using Python  Use word embedding to capture semantic meaning of words  
Week 13  Neural Networks   Understand the concept of neural networks and its applications  Explain the structure and components of a neural network (input layer, hidden layer, output layer)  Implement a simple neural network using Python (feedforward propagation, backpropagation)  Use activation functions, loss functions, optimization algorithms in neural networks  
Week 14  Deep Learning  Understand the concept of deep learning and its applications  Explain the difference between shallow and deep neural networks  Implement convolutional neural networks (CNNs) using Python for image recognition  Implement recurrent neural networks (RNNs) using Python for sequence modeling  Use TensorFlow and Keras to build and train deep learning models  
Week 15  Data Science Ethics and Social Issues   Understand the ethical and social issues of data science, such as privacy, fairness, accountability, and transparency  Identify and address potential biases and harms in data collection, analysis, and use  Apply ethical principles and frameworks to data science projects  Communicate and present data science results in a responsible and trustworthy manner 
Assessment Methods and Criteria  Evaluation Tool  Quantity  Weight  Alignment with LOs 
Final Exam  1  30  
Semester Evaluation Components  
Midterm  1  30  
Quizzes  2  10  
Term project and presentation  1  10  
Lab assignments  10  20  
*** ECTS Credit Calculation *** 
Activity  Hours  Weeks  Student Workload Hours  Activity  Hours  Weeks  Student Workload Hours  
Lecture hours  3  14  42  Assignments  2  10  20  
Active labs  2  14  28  Home study  1  14  14  
Interm exam study  11  1  11  Final exam study  11  1  11  
Term project/presentation  2  12  24  
Total Workload Hours =  150  
*T= Teaching, P= Practice  ECTS Credit =  6  
Course Academic Quality Assurance: Semester Student Survey  Last Update Date: 20/02/2024 