Antalya Science University
Course Name: Introduction to Data Science
Course Code: CS 447
Language of Course: English
Course Coordinator / Instructor: Şadi Evren ŞEKER
Schedule: Tuesday 15.00 – 18.00
Course Description: This course is an introduction level course to data science, specialized on machine learning, artificial intelligence and big data.
- The course starts with a top down approach to data science projects. The first step is covering data science project management techniques and we follow CRISP-DM methodology with 6 steps below:
- Business Understanding : We cover the types of problems and business processes in real life
- Data Understanding: We cover the data types and data problems. We also try to visualize data to discover.
- Data Preprocessing: We cover the classical problems on data and also handling the problems like noisy or dirty data and missing values. Row or column filtering, data integration with concatenation and joins. We cover the data transformation such as discretization, normalization, or pivoting.
- Machine Learning: we cover the classification algorithms such as Naive Bayes, Decision Trees, Logistic Regression or K-NN. We also cover prediction / regression algorithms like linear regression, polynomial regression or decision tree regression. We also cover unsupervised learning problems like clustering and association rule learning with k-means or hierarchical clustering, and a priori algorithms. Finally we cover ensemble techniques in Knime and Python on Big Data Platforms.
- Evaluation: In the final step of data science, we study the metrics of success via Confusion Matrix, Precision, Recall, Sensitivity, Specificity for classification; purity , randindex for Clustering and rmse, rmae, mse, mae for Regression / Prediction problems with Knime and Python on Big Data Platforms.
Course Objective and Learning Outcomes:
1. Understanding of real life cases about data
2. Understanding of real life data related problems
3. Understanding of data analysis methodologies
4. Understanding of some basic data operations like: preprocessing, transformation or manipulation
5. Understanding of new technologies like bigdata, nosql, cloud computing
6. Ability to use some trending software in the industry
7. Introduction to data related problems and their applications
List of course software:
· Python Programming with Numpy, Pandas, SKLearn, StatsModel or DASK
This course is following hands on experience in all the steps. So attendance with laptop computers is necessary. Also the software list above, will be provided during the course and the list is subject to updates.
Reading, Attendence and Discussions: 30%
|Week 1 (Feb 19): Introduction to Data, Problems and Real World Examples:Some useful information:DIKW Pyramid: DIKW pyramid – WikipediaCRISP-DM: Cross-industry standard process for data mining – WikipediaSlides from first week:week1|
|Week 2 (Feb 26): Introduction to Descriptive Analytics
Repeating the first week for majority of the class and starting the concept of end to end data science projects. Weight and Heigh Sample project and Data Set for Knime work flow. Brief introduction to algorithms: K-NN, Naive Bayes, Decision Trees, Linear Regression
Week 3 (Mar 5): Introduction to Data Manipulation
Week 4 (Mar. 12): Introduction to Python Programming for Data Science and an end-to-end Python application for data science
You can install anaconda and Spyder from the link below:
Also we have covered below topics during the class:
For further information I strongly suggest you to read the below documentations:
Week 5 (Mar 19): Classification Algorithms
|Week 6 (Mar 26): Regression Algorithms
concepts of prediction algorithms, implementing the algorithms in Knime and coding in python. Algorithms covered are:
Support Vector Regressor
Regression Trees and Decision Tree Regressor
|Week 7 (Apr 2): Clustering Algorithms
concepts of clustering algorithms, implementing the algorithms in Knime and coding in python. Algorithms covered are:
|Week 8 (Apr 9): Association Rule Mining
concepts of association rule mining (ARM) and association rule learning (ARL) algorithms, implementing the algorithms in Knime and coding in python. Algorithms covered are:
|Week 9 (Apr 16): Concept of Error and Evaluation Techniques
n-Fold Cross Validation , LOO, Split Validation
RMSE, MAE, R2 values for regression
RandIndex, Silhouet, WCSS for clustering algorithms
Accuracy, Recall, Precision, F-Score, F1-Score etc. for classification algorithms
|Week 10 (Apr 23): Collective Learning : Ensemble Learning, Bagging, Boosting Techniques, Random Forest, GBM, XGBoost, LightGBM|
|Week 11 (Apr 30): Consensus Learning and Clustering Algorithms|
|Week 12 (May 7): Project Presentations First Group.
Presentations will be picked randomly during the class and anybody absent will be considered as not presented.
Project Deliveries (until May 6): Project Presentation, Project Report (explaining your project, your approach and methodologies, difficulties you have faced, solutions you have found, results you have achieved in your projects, links to your data sources). Knime Workflows (in .knwf format) and python codes (in .py format). Please make all these files a single .zip or .rar archive and do not put more than 4 files in your archive.
|Week 13 (May 14): Project Presentations Second Group|