Nature of Data, Statistical Modeling, and Visualization - WordPress… · 2018. 12. 28. · An...
Transcript of Nature of Data, Statistical Modeling, and Visualization - WordPress… · 2018. 12. 28. · An...
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Kecerdasan Bisnis Terapan
Descriptive Analytics I
Nature of Data, Statistical
Modeling, and VisualizationHusni
Lab. Riset JTIF UTM
Sumber awal: http://mail.tku.edu.tw/myday/teaching/1071/BI/1071BI04_Business_Intelligence.pptx
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Business Intelligence (BI)
2
Introduction to BI and Data Science
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
1
3
5
4
Big Data Analytics
2
6 Future Trends
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Descriptive Analytics I: Nature of Data,
Statistical Modeling, and Visualization
3
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Outline
• Descriptive Analytics I
• Nature of Data
• Statistical Modeling
• Visualization
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Three Types of Analytics
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Data to Knowledge Continuum
6Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Simple Taxonomy of Data
7Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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Data Preprocessing Steps
8Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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An Analytics Approach to Predicting Student Attrition
9Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Graphical Depiction of the Class Imbalance Problem
10Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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Relationship between Statistics and Descriptive Analytics
11Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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Understanding the Specifics about Box-and-Whiskers Plots
12Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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Relationship between Dispersion and Shape Properties.
13Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Scatter Plot and a Linear Regression Line
14Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Process Flow for Developing Regression Models.
15Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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The Logistic Function
16Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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Predicting NCAA Bowl Game Outcomes
17Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Sample Time Series of Data on Quarterly Sales Volumes
18Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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The Role of Information Reporting in Managerial Decision Making
19Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Taxonomy of Charts and Graphs
20Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Gapminder Chart That Shows the Wealth and Health of Nations
21Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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Magic Quadrant for Business Intelligence and Analytics Platforms
22Source: https://www.tableau.com/reports/gartner
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A Storyline Visualization in Tableau Software
23Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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An Overview of SAS Visual Analytics Architecture
24Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Screenshot from SAS Visual Analytics
25Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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A Sample Executive Dashboard
26Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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Discovering, Analyzing,
Visualizing and Presenting Data
with Python in Google Colab
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Google Colab
30https://colab.research.google.com/notebooks/welcome.ipynb
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The Quant Finance PyData Stack
31Source: http://nbviewer.jupyter.org/format/slides/github/quantopian/pyfolio/blob/master/pyfolio/examples/overview_slides.ipynb#/5
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Iris flower data set
34Source: http://suruchifialoke.com/2016-10-13-machine-learning-tutorial-iris-classification/
setosa versicolor virginica
Source: https://en.wikipedia.org/wiki/Iris_flower_data_set
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Iris Classfication
Source: http://suruchifialoke.com/2016-10-13-machine-learning-tutorial-iris-classification/
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iris.data
36
https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
setosa
versicolor
virginica
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37Source: https://seaborn.pydata.org/generated/seaborn.pairplot.html
Iris Data Visualization
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Connect Google Colab in Google Drive
38
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Google Colab
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Google Colab
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Connect Colaboratory to Google Drive
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Google Colab
42
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Google Colab
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Google Colab
44
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Run Jupyter Notebook Python3 GPUGoogle Colab
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46
Google Colab Python Hello Worldprint('Hello World')
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Data Visualization in Google Colab
47Source: https://seaborn.pydata.org/generated/seaborn.pairplot.html
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48
import seaborn as sns
sns.set(style="ticks", color_codes=True)
iris = sns.load_dataset("iris")
g = sns.pairplot(iris, hue="species")
Source: https://seaborn.pydata.org/generated/seaborn.pairplot.html
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49
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pandas.plotting import scatter_matrix
# Load dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
df = pd.read_csv(url, names=names)
print(df.head(10))
print(df.tail(10))
print(df.describe())
print(df.info())
print(df.shape)
print(df.groupby('class').size())
plt.rcParams["figure.figsize"] = (10,8)
df.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
plt.show()
df.hist()
plt.show()
scatter_matrix(df)
plt.show()
sns.pairplot(df, hue="class", size=2)
Source: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
https://colab.research.google.com/drive/1KRqtEUd2Hg4dM2au9bfVQKrxWnW
N3O9-
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50
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pandas.plotting import scatter_matrix
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51
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
df = pd.read_csv(url, names=names)
print(df.head(10))
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52
df.tail(10)
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df.describe()
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print(df.info())
print(df.shape)
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df.groupby('class').size()
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plt.rcParams["figure.figsize"] = (10,8)
df.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
plt.show()
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df.hist()
plt.show()
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scatter_matrix(df)
plt.show()
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sns.pairplot(df, hue="class", size=2)
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Summary
• Descriptive Analytics I
• Nature of Data
• Statistical Modeling
• Visualization
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References
• Ramesh Sharda, Dursun Delen, and Efraim Turban (2017), Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson.
• EMC Education Services (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley
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