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Python basics.pdf B Installing andManaging Python Installing Python via Anaconda Managing Packages �6� �66 Appendix B. Installing and Managing Python Conda Pip �6� Work�ows Text Editor + Terminal �68...

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Python basics.pdf
B Installing andManaging Python
Installing Python via Anaconda
Managing Packages
�6�
�66 Appendix B. Installing and Managing Python
Conda
Pip
�6�
Work�ows
Text Editor + Terminal
�68 Appendix B. Installing and Managing Python
Jupyter Notebook
Integrated Development Environments
C NumPy Visual Guide
n
Data Access
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__MACOSX/._Python basics.pdf
analytics.pptx
Introduction to Analytics
and Big Data Visualization
1
OVERVIEW     
Data-driven
Data Science
Data Analytics
What is the difference?
What is
Data-driven?
    
Being data-driven means
Making business decisions
Managing processes
        based on      facts        
                 insights    derived from data
OVERVIEW     
Why
Data-driven?
OVERVIEW     
Most companies
have information systems
ut are not data-driven
OVERVIEW     
These companies
have information systems
ERP system
CRM module
Accounting system
OVERVIEW     
But not aware if the data is
accurate
up-to-date
OVERVIEW     
Moreover, they may not know
how to make
the best use of the data
OVERVIEW     
They may be
        doing transactions and
        making decisions
with not accurate
with not up-to-date
                    data
OVERVIEW     
They may be
        doing transactions and
        making decisions
with not accurate
with not up-to-date, o
ignoring the         data
OVERVIEW     
Making decisions ignoring the data?
OVERVIEW     
Making decisions ignoring the data?
Based on feelings
Based on personal experiences
OVERVIEW     
Making decisions ignoring the data?
Based on feelings
Based on personal experiences
OVERVIEW     
iased
.
OVERVIEW     
.
OVERVIEW     
A data-driven organization
puts data at the core of thei
usiness processes
        using facts, insights    derived from data
                to drive their decision-making
OVERVIEW     
A data-driven organization
moves from guessing and
assumptions
    to using data and analytics
            to make faster and better decisions
OVERVIEW     
Relevant and accurate data are
at the core
of a data-driven organization
OVERVIEW     
What is
Data Science?
INTRODUCTION     
Data Science is the field of study that combines
computer science
statistics
usiness
to find useful information
from raw data.
INTRODUCTION     
Why
Statistics?
DATA SCIENCE     
Statistics = data analysis
DATA SCIENCE     
Statistics is data
collection
cleaning
organization
visualization
analysis
modeling
presentation
DATA SCIENCE     
Statistics is data
collection
cleaning
organization
visualization
analysis
modeling
presentation
DATA SCIENCE     
data visualization
static
interactive
animation
DATA SCIENCE     
IBM Burning Glass Tech Report
STATIC DATA VISUALIZATION    
Interactive Data Visualization Example 1
INTERACTIVE DATA VISUALIZATION     
Interactive Data Visualization Example 2
INTERACTIVE DATA VISUALIZATION     
Data Animation Example
DATA ANIMATION     
Statistics is data
collection
cleaning
organization
visualization
analysis
modeling
presentation
DATA SCIENCE     
data modeling
Generalized linear models
Bayesian modeling
cluster analysis
time series modeling
principal components
partial least squares
spatial analysis
DATA SCIENCE     
Statistical tools     to understand, analyze the data

Random variables
density functions
Outliers
Covariance, co
elation
Probabilities
Bootstrapping
Confidence and Prediction Intervals
DATA SCIENCE     
Why
Computer Science?
DATA SCIENCE     
Computer Science tools
                to collect, process, store the data

Data Wrangling     (unstructured to structured data)
Data Warehousing (repo of structured data)
Cloud computing
Big data
Machine learning models
Web developing (front-end)
DATA SCIENCE     
Why
Business?
DATA SCIENCE     
Business domain knowledge
        to make the right questions about

Customer reqs
Products
Processes
Variables
KPIs
Environment variables
DATA SCIENCE     
    Business domain = Industry

Retail
Health care
Financial
Manufacturing
Government
Services
DATA SCIENCE     
    Business domain = Science

Biology
Medicine
Physics
Materials science
Chemistry
DATA SCIENCE     
What is
Data Analytics?
DATA ANALYTICS    
Data Analytics professional is someone whose
focus is on
collecting     
summarizing        data
analyzing     
        to find answers to business questions
DATA ANALYTICS    
Data Analyst
Business Analyst
DATA ANALYTICS    
Who is the Business Analyst?
What are the Business questions?
DATA ANALYTICS    

             Business Analyst

Decision Maker              Data Analyst
(questions)                     (solutions)
DATA ANALYTICS    
Business questions
What happened?
What will happened?
DATA ANALYTICS    
What happened?      -business case-
Which products underperformed?
Which were more profitable?
Did our market share change?
What is our retention rate?
Who are our most valuable customers?
DATA ANALYTICS    
What will happen? -business case-
What is the expected growth?
Who are potential customers?
Most promising product lines?
What market share can we expect?
What new competitors may arise?
DATA ANALYTICS    
What will happen? -new product-
What is the probability of success?
What is the risk of failure?
What is the market acceptance rate?
Will it outperform cu
ent best product?
DATA ANALYTICS    
What will happen? -investment-
What is the expected return?
What is the probability of a loss?
If there is a loss, how large can it be?
What scenarios are possible?
Major external risk in our sector?
DATA ANALYTICS    
How does the
Data Analyst
answer these questions?
DATA ANALYTICS    
DATA ANALYST – KEY MEASURES YOU SHOULD KNOW    
percentages
weighted average
percentile/quantile
absolute, relative change
net, gross change
growth rate
mean, median, variance
ange
covariance
co
elation
distribution
, R-squared
.
DATA ANALYST – KEY MEASURES YOU SHOULD KNOW    
gross change =
net change = XXXXXXXXXX
net change also called relative change
DATA ANALYST – KEY MEASURES YOU SHOULD KNOW    
Example:      If there is a loss,
         how large can it be?
Collect past data
Find distribution of daily losses
Find 95% quantile of daily losses
Find expected loss beyond that quantile (VaR)
DATA ANALYTICS    
.
DATA ANALYTICS    
|
95%
Example:     Medicine
Business Question
    Predict tumor outcome (benign or malign)                 based on tissue measurements
Collect lab data about variables related to cancer tumors
Build classification model
DATA ANALYTICS    
DATA ANALYTICS    
DATA ANALYTICS    
DATA ANALYTICS    
DATA ANALYTICS    
DATA ANALYTICS    
DATA ANALYTICS    
DATA ANALYTICS    
Data Analyst
Searches subsets of variables
to identify malign cancer
Use PCA plot to verify if the PCs are able to identifying cance
Develop a decision boundary
DATA ANALYTICS    
DATA ANALYTICS    
DATA ANALYTICS    
Sampling variation
Sampling e
o
Standard e
o
Significant Statistical difference vs True difference
DATA ANALYST – KEY CONCEPTS YOU SHOULD KNOW    
Data Analytics focus is on answering business questions
What happened?
What will happen?
DATA ANALYTICS    
What happened?    
Descriptive Stats
Summary Tables (crosstabs, pivot tables)
Data visualization
Dashboards
DATA ANALYTICS    
What happened?            Descriptive Analytics
Descriptive Stats
Summary Tables (crosstabs, pivot tables)
Data visualization
Dashboards
DATA ANALYTICS    
What happened?            Descriptive Analytics
Descriptive Stats
Summary Tables (crosstabs, pivot tables)
Data visualization
Dashboards
What may happen?    
Prediction Models
Classification Models
Clustering methods
DATA ANALYTICS    
What happened?            Descriptive Analytics
Descriptive Stats
Summary Tables (crosstabs, pivot tables)
Data visualization
Dashboards
What may happen?        Predictive Analytics
Prediction Models
Classification Models
Clustering methods
DATA ANALYTICS    
What happened?            Descriptive Analytics
Descriptive Stats
Summary Tables (crosstabs, pivot tables)
Data visualization
Dashboards
Why did it happen?
What may happen?        Predictive Analytics
Prediction Models
Classification Models
Clustering methods
DATA ANALYTICS    
What happened?            Descriptive Analytics
Descriptive Stats
Summary Tables (crosstabs, pivot tables)
Data visualization
Dashboards
Why did it happen?        Diagnostic Analytics
What may happen?        Predictive Analytics
Prediction Models
Classification Models
Clustering methods
DATA ANALYTICS    
Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
DATA ANALYTICS    
Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
DATA ANALYTICS    

                        
                    
Past performance
Historical data
Today
observe & predict
Future performance
esults
ANALYTICS    
What happened?                      XXXXXXXXXXWhat may happen?
                        
                    
Past performance
Historical data
Today
observe & predict
Future performance
esults
ANALYTICS    
What happened?                      XXXXXXXXXXWhat may happen?
Describe/summarize data                  XXXXXXXXXXscenarios
                        
                    
Past performance
Historical data
Today
observe & predict
Future performance
esults
ANALYTICS    
What happened?                      XXXXXXXXXXWhat may happen?
Describe/summarize data                  XXXXXXXXXXscenarios
Descriptive Stats        
Barplots, scatterplots, boxplots                 XXXXXXXXXXPrediction Models
Line charts, Histograms                      XXXXXXXXXXprediction models
Averages, std. deviations                  XXXXXXXXXXclassification models
co
elations                    
Answered Same Day Jun 03, 2021

Solution

Ishvina answered on Jun 04 2021
147 Votes
Link for the day1 homework submission- It is all as per the instructions given , you can open the link in your
owser and view it .
https:
colab.research.google.com/drive/1g9WmPsAvImmB9cg0hr-VXDFNAKOzFBqR?usp=sharing
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