CS447 – Introduction to Data Science 2020

Antalya University

Course Name: Introduction to Data Science Spring 2020

Course Code: CS 447

Language of Course: English

Credit: 3

Course Coordinator / Instructor: Şadi Evren ŞEKER

Contact: intrds@sadievrenseker.com

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

Tools:

List of course software:

·       Excel,

·       KNIME,

·       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.

Grading

One individual term project covering all the topics covered in the course : %100

Course Content:

Week 1 (Feb 11): 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 18): 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 (Feb 25): Introduction to Data Manipulation Concept of Data and types of data : Categorical (Nominal, Ordinal) and Numerical (Interval, Ratio). Basic Data Manipulation techniques with Knime: 1.Row Filter and Concept of Missing Values 2.Column Filter 3.Advanced Filters 4.Concatenate 5.Join 6. Group by , Aggregation 7. Formulas, String Replace 8. String Manipulation 9. Discrete, Quantized Data, Binning 10. Normalization 11.Splitting and Merging 12.Type Conversion (Numeric , String)
Week 4 (Mar. 3): Introduction to Python Programming for Data Science and an end-to-end Python application for data science Brief review of python programming Introduction to data manipulation libraries: NumPY and Pandas Introduction to the Sci-Kit Learn library and a sample classification You can install anaconda and Spyder from the link below: Also we have covered below topics during the class:
  • Data loading from external source using Pandas library (with read_excel or read_csv methods)
  • DataFrame slicing and dicing (using the iloc property and the lists provided to the iloc method)
  • Column Filtering (with copying into a new data frame)
  • Row Filtering (with copying into a new data frame)
  • Advanced row filtering (like filtering the people with even number of heights)
  • Column or row wise formula (we have calculated the BMI for everybody)
  • Quantization (discretization or binning): where we have applied the condition based binning
  • Min – Max Normalization (we have implemented MinMaxScaler from the SKLearn library)
  • Group By operation (we have implemented the groupby method from pandas library)
Click here to download the codes from the class For further information I strongly suggest you to read the below documentations:
Week 5 (Mar 10): Classification Algorithms concepts of classification algorithms, implementing the algorithms in Knime and coding in python. Algorithms covered are: K-NN Naive Bayes Decision Tree Logistic Regression Support Vector Machines 2nd Python Code of the course for the classifications Knime Workflow for the classification algorithms
Week 6 (Mar 17): Regression Algorithms concepts of prediction algorithms, implementing the algorithms in Knime and coding in python. Algorithms covered are: Linear Regression Polynomial Regression Support Vector Regressor Regression Trees and Decision Tree Regressor Python code for the Regression Knime Workflow and the BIST 100 data set for the Regression Algorithms  The Data Set obtained from : finance.yahoo.com
Week 7 (Mar 24): Clustering Algorithms concepts of clustering algorithms, implementing the algorithms in Knime and coding in python. Algorithms covered are: K-Means DBScan Hierarchical Clustering Knime Workflow Python Code
Week 8 (Mar 31): 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: A-Priori Algorithm Click Here To Download Apyroiri Library for the Python Codes click for python code  click for knime workflow Homework : Link for Kaggle, instacart
Week 9 (Apr 7): 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 We also got an introduction to dimension reduction with PCA (principal component analysis) and Neural networks with MLP (multi layer perceptron) Please don’t forget to install Keras for next week.
Week 10 (Apr 14): Collective Learning : This content has moved to previous week because of the holiday
Week 11 (Apr 21): Collective Learning and Consensus Learning and Clustering Algorithms: Ensemble Learning, Bagging, Boosting Techniques, Random Forest, GBM, XGBoost, LightGBM Some links useful for the class: Readings and resources: Python Codes from the class : Gradient Boosting: XGBoost (for running the code install XGBoost by the command prompt: conda install -c conda-forge xgboost Install XGBoost extension for Knime  
Week 12 (Apr 28): 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 5): Project Presentations Second Group If you haven missed the project presentations in the first week, please contact me for further details.
Week 14( May 12): TBA
Week 15( May 19): TBA