授業の主題
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Basic programming techniques necessary to implement programs dealing with numerical computations, data processing and machine learning in Python 3.
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学修目標（到達目標）
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The goal of this class is to gain a solid programming foundation that is necessary to use computers effectively to automate simple tasks, perform numerical computations and analyze data. It should allow you to continue studying more advanced programming techniques required for research work in numerical analysis. The hope is that you will be able to implement various algorithms in numerical mathematics, as well as do simple data and statistical analysis, including machine learning and AI techniques. The programming techniques we will learn are general and apply to many programming languages, but in this course we will be using Python 3.
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授業概要
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Part a
1. Programming and numerical computation
2. Variables, types, control flow
3. Strings
4. Working with strings
5. Lists
6. List algorithms: searching
7. List algorithms: sorting, complexity
8. Extra topics
Part b
1. More data types: dict, set
2. More data types: stack, queue
3. More data types: exercises
4. Classes and objects
5. Working with data and text
6. Structured data, files: CSV
7. Structured data, files: SVG (vector graphics)
8. Extra topics
A part of each lecture will be devoted to individual solving of exercises including programming tasks. Each student is required to bring a laptop to every lecture.
Students are expected to spend about 3 hours of self-study per week.
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評価方法と割合
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評価方法
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Grade will be decided holistically as below, based on the following terms/rates.
「S(Academic achievement 90%~100%)」,「A(80% or more, less than90%)」,
「B(70% or more, less than80%)」, and「C(60% or more, less than70%)」 are indicators of passing, 「不可(less than 60%)」is an indicator of failure.(Standard rating method)
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評価の割合
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Attendance to at least two-thirds of classes is required
- 75% Report
- 25% Short test
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授業時間外の学修に関する指示
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予習に関する指示
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★1. On-demand materials (entire class content)
Preparation for each lecture (if any) will be discussed during the previous lecture. It is expected that students review the content of the previous lectures and can apply it during practical exercises that are part of each lecture. (about 0.5h/week required)
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予習に関する教材
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オンデマンド教材（授業内容の全体）
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復習に関する指示
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★1. On-demand materials (entire class content)
The students are expected to solve the exercises given in each lecture (about 2.5h/week required).
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復習に関する教材
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オンデマンド教材（授業内容の全体）
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教科書・参考書
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教科書・参考書補足
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Reference texts will be discussed during lectures.
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オフィスアワー等（学生からの質問への対応方法等）
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I do not specify office hours. Please feel free to arrange a meeting using email (npozar@se.kanazawa-u.ac.jp) or visit me in my office directly.
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履修条件
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The students are expected to have passed 計算科学[Computational Science] course. The students are expected to sign up for both part a and part b.
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