Python Programming @ NTNU
Course Content

In the capacity of an urban Geographic Information Systems (GIS) researcher, one is confronted with the formidable challenge of dealing with vast and diverse datasets, some of which may be dynamically generated in real-time (streaming data) rather than being static in nature. Consequently, the initial inquiry that naturally arises pertains to the methods and tools available for the processing of "Big Data" or "Streaming Data" within the computational environment.
Python, being one of the most ubiquitous programming languages, offers an array of pragmatic packages and libraries. These package resources, meticulously designed and curated, not only expedite the execution of data analytics but also furnish an assortment of sophisticated visualization tools capable of captivating the attention of stakeholders and researchers alike.

Course Intro.

01 :: Course Introduction

Contents: (1) Course intro. (2) Grading policy (3) What is the probabilities (4) What is the statistics (5) Why you need to take this course? (6) What you will learn from this course?

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Environ Settings

02 :: Environ Settings

Contents: (1) Anaconda Install (2) Hello World (3) Terminal/ Windows Powershell (4) Git

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Variables & Operations

03 :: Variables & Operations

Content: (1) Variables (2) Data Types (3) Strings (4) Booleans (5) Operators (6) Assignments

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Comments & Markdown

04 :: Comments & Markdown

Content: (1) Comments (2) Markdown (2-1) Web Mode (2-2) LaTeX

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Collections

Content: (1) List (2) Tuple (3) Set (4) Dictionary (5) Assignments

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Flow Control

06 :: Flow Control

Content: (1) Conditions (2) If… Else… (3) For Loop (4) While Loop (5) Pass, Continue, Break (6) Try… Except…

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Function

07 :: Function

Content: (1) Functions (2) Recursive Functions

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Numpy

08 :: Numpy

Content: (1) Indexing (2) Dtype (3) Dimension (4) Shape (5) Reshape (6) Concatenation (7) Split (8) Sort (9) Random (10) Statistics (11) Normal Distribution (12) Central Limit Theory

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Pandas

09 :: Pandas

Content: (1) Make a DataFrame (2) Indexing (3) Merge & Concatenation (4) Groupby & Drop Duplicated (5) Data I/O (6) IsNA, Fillna & Dropna (7) Pandas.Series.str (8) Datetime Formation (9) Datetime Analysis

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Visualization

10 :: Visualization

Content: (1) Visualization Methods (2) Gallery (3) Matplotlib (4) Colormap (5) Line Plot (6) Export Figure (7) Subplot & Style Adjustment (8) Scatter & Bar Chart (9) Fill Between & Stack Plot (10) Histogram & Accumulated Histogram (11) Hexbin & Hist2D (12) Box & Violin Plot (13) Quiver (14) Contour & Contourf (15) Surface & Trisurf (16) Interpolaton for imshow (17) Seaborn (18) Pairplot (19) Plotly (20) Sankey Diagram

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Statistics

11 :: Statistics

Content: (1) Statistical Analysis Road Map (2) Normality Test (3) F Test (4) Levene's Test (5) T Test (6) Correlation Analysis (7) Assignment

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Regression Analysis

12 :: Regression Analysis

Content: (1) Scaling Problems (2) Ordinary Least Squares (3) Lasso Regression (4) Ridge Regression (5) Generalized Linear Model (6) Evaluation Metrics (7) Assignments

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Eigen & Singular Value Decomposition

13 :: Eigen & Singular Value Decomposition

Content: (1) Eigen Decomposition (2) Singular Value Decomposition

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Principal Component Analysis

14 :: Principal Component Analysis

Content: (1) Review (2) Why Do We Need Dimension Reduction? (3) Geometry (4) Linear Algebra (5) Characterisitcs (6) Principal Components (7) Variable Loading (8) Steps of PCA (9) sklearn.decomposition.pca (10) SVD (11) Eigen Decomposition (12) [CODE] Variable Loading

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GeoPandas

15 :: GeoPandas

Content: (1) Intro. to GeoPandas (2) File I/O (3) Coordinate Reference System (4) Make a GeoDataFrame for Point\Polyline (5) Basic Preprocessing in GeoPandas (6) Map Visualization (7) GeoProcessing :: Fundamentals

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GeoVisualization

16 :: GeoVisualization

Content: (1) Interactive Maps (2) Folium Fundamentals (3) Folium Advance

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