I will send the link to teacher .so please use the new google account to creat all the workflow .I suugest to use the name of "Amandayh....." you can add what you want.Thank you
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 0 = 2 664 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 3 775 = 2 664 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 3 775 Slicing a b a b = = 2 664 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 3 775 = 2 664 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 3 775 = 2 664 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 3 775 = 2 664 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 3 775 �6� ��� Appendix C. NumPy Visual Guide Stacking = 2 4 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 3 5 = 2 4 ⇤ ⇤ ⇤ ⇤ ⇤ ⇤ ⇤ ⇤ ⇤ 3 5 = 2 4 ⇥ ⇥ ⇥ ⇤ ⇤ ⇤ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇤ ⇤ ⇤ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇤ ⇤ ⇤ ⇥ ⇥ ⇥ 3 5 = 2 66666666666664 ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇤ ⇤ ⇤ ⇤ ⇤ ⇤ ⇤ ⇤ ⇤ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ ⇥ 3 77777777777775 = ⇥ ⇥ ⇥ ⇥ ⇥ ⇤ = ⇥ ⇤ ⇤ ⇤ ⇤ ⇤ = ⇥ ⇥ ⇥ ⇥ ⇥ ⇤ ⇤ ⇤ ⇤ ⇥ ⇥ ⇥ ⇥ ⇤ = 2 4 ⇥ ⇥ ⇥ ⇥ ⇤ ⇤ ⇤ ⇤ ⇥ ⇥ ⇥ ⇥ 3 5 = 2 664 ⇥ ⇤ ⇥ ⇥ ⇤ ⇥ ⇥ ⇤ ⇥ ⇥ ⇤ ⇥ 3 775 Broadcasting ��� = 2 4 1 2 3 1 2 3 1 2 3 3 5 = ⇥ 10 20 30 ⇤ = 2 4 3 5 1 2 3 1 2 3 1 2 3 +⇥ ⇤ 10 20 30 = 2 4 11 22 33 11 22 33 11 22 33 3 5 = 2 4 1 2 3 1 2 3 1 2 3 3 5+ 2 4 10 20 30 3 5 = 2 4 11 12 13 21 22 23 31 32 33 3 5 Operations along an Axis A = 2 664 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 3 775 = 2 664 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 3 775 = ⇥ 4 8 12 16 ⇤ = 2 664 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 3 775 = ⇥ 10 10 10 10 ⇤ __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 insightsderived from data OVERVIEW Why Data-driven? OVERVIEW Most companies have information systems but 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, or 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 biased . OVERVIEW . OVERVIEW A data-driven organization puts data at the core of their business processes using facts, insightsderived 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 business 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, correlation 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 summarizingdata 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 current 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 range covariance correlation distribution r, R-squared . DATA ANALYST – KEY MEASURES YOU SHOULD KNOW gross change = net change = - 1 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 cancer Develop a decision boundary DATA ANALYTICS DATA ANALYTICS DATA ANALYTICS Sampling variation Sampling error Standard error 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 results ANALYTICS What happened? What may happen? Past performance Historical data Today observe & predict Future performance results ANALYTICS What happened? What may happen? Describe/summarize data scenarios Past performance Historical data Today observe & predict Future performance results ANALYTICS What happened? What may happen? Describe/summarize data scenarios Descriptive Stats Barplots, scatterplots, boxplots Prediction Models Line charts, Histograms prediction models Averages, std. deviations classification models correlations