ADVANCED PROGRAMMING

AIM

The objective of this course is to improve programming and problem solving capabilities and skills of students using Python with an emphasis on programming practice, efficiency and data science. Pyhton is widely used language in education, scientific computing and data science with a large number of libraries. Students will learn, design, develop and test efficient programs that take advantage of built-in libraries developed for AI and data science without having to know about complex logic and mathematics behind them. Topics include programming efficiency and analysis, study and analysis of some basic algorithms, graphical user interfaces, advanced featues of Python, Python Data Structures, Loading Datasets from Different Data Stores, Array-Oriented Programming with NumPy, High-Performance NumPy Arrays, Pandas Series and DataFrames, Regular Expressions and Data Wrangling, Time Series and Simple Linear Regression, Natural Language Processing (NLP), Web Scraping, Data Mining Twitter: Sentiment Analysis, Machine Learning: Classification, Regression and Clustering, Deep Learning Convolutional and Recurrent Neural Networks, Recommendations with Collaborative Filtering, Optimization.

CONTENT

This course contains; Developing Efficient Algorithms,Analysis of Searching and Sorting Algorithms,Python Data Structures,Data Analysis and Visualization,Array-Oriented and Scientific Programming with NumPy and SciPy,Data Manipulation with Pandas,Data Loading, Storage, and File Formats; Data Visualization,Time Series and Simple Linear Regression,Natural Language Processing (NLP), Web Scraping,Data Mining Twitter: Sentiment Analysis, JSON and Web Services,Machine Learning: Classification, Regression and Clustering,Deep Learning Convolutional and Recurrent Neural Networks,Collaborative Filtering, Making Recommendations,Optimization.

1. 1

   6- Summarize, visualize and analyze data.

2. 2

   1 - Design, implement and test efficient programs.

3. 3

   2 - Improve programming skills by learning, analyzing, solving and developing program code for different problems.

4. 4

   3 -Learn how to design, develop and implement modular programs by using structured programming, abstract data types, classes and objects.

5. 5

   4 - Take advantage of capabilities of built-in and third party libraries available in many areas.

6. 6

   5 - Learn how to store, load, manipulate and explore data.

7. 7

   7 - Write programs for a wide variety problems in math, science, engineering, financials, artificial intelligence and games.

8. 8

   8 - Learn how to use and apply some of the machine learning, data mining, and optimization libraries on several examples.

WEEKLY PLAN

1. WEEK 1

   Developing Efficient Algorithms

2. WEEK 2

   Analysis of Searching and Sorting Algorithms

3. WEEK 3

   Python Data Structures

4. WEEK 4

   Data Analysis and Visualization

5. WEEK 5

   Array-Oriented and Scientific Programming with NumPy and SciPy

6. WEEK 6

   Data Manipulation with Pandas

7. WEEK 7

   Data Loading, Storage, and File Formats; Data Visualization

8. WEEK 8

   Time Series and Simple Linear Regression

9. WEEK 9

   Natural Language Processing (NLP), Web Scraping

10. WEEK 10

    Data Mining Twitter: Sentiment Analysis, JSON and Web Services

11. WEEK 11

    Machine Learning: Classification, Regression and Clustering

12. WEEK 12

    Deep Learning Convolutional and Recurrent Neural Networks

13. WEEK 13

    Collaborative Filtering, Making Recommendations

14. WEEK 14

    Optimization

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

WORKLOAD

READING

• Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud, Paul Deitel, Harvey Deitel, Pearson, 2020
• - Toby Segaran, Programming Collective Intelligence, O Reilly Press, 2007. - Brad Miller and David Ranum, Luther College, Problem Solving with Algorithms and Data Structures using Python, Franklin, Beedle & Associates, 2011

TEACHING STAFF

• Prof.Dr. Selim AKYOKUŞCOORDINATOR
• Prof.Dr. Selim AKYOKUŞ
• Malik GEYLANİ