Bootcamp to Predictive Modelling with R - Statistical Track

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1 Anova Analytics, LLC BUSINESS ANALYTICS Bootcamp to Predictive Modelling with R - Statistical Track Introduction: Apart from the profession with the highest demand on years to come, it is beyond doubt the seismic impact Data Science will have on business, education, society and government. Building your skills to understand, use and build predictive models will equip your career and business for the coming R as an open-source language that is rapidly becoming the leading programming language in statistics and data science. It is used and supported by the largest community of students, academics and professionals for both research and business applications. This Boot Camp will teach you how to build insight from data and help you unleash the power of R programming in your data science work. In the Statistical Modeling Track, you will focus on the statistical techniques used to analyze and gain insights from your data. This is where the magic happens! These sessions will take you from getting your data all the way to producing insightful predictions to support decision making. You ll learn how to build your own prediction models and models that learn all from a tab in your browser.

2 Key Learning Objectives Learn directly from leading industry practitioners with extensive Data Mining and Business Analytics experience to: 1. Understand and learn how to assemble and create a business case and relevant data worthy of building a Data Product to solve that problem. 2. Gain the skills to understand, describe and appreciate the data from the perspective of quality, diversity and accuracy. 3. Develop a deeper knowledge and establish patterns of information gathered from data thru visual representations. 4. Learn to build means and methods of massive data manipulation and data wrangling to produce a ready to model dataset. 5. Learn about new processes to train, test and measure the performance of your model using scientific methods. 6. Use your predictive modeling analysis to support and improve your company s productivity and strategic decision-making. 7. Master the know-how to turn your Predictive Models into Stand Alone operational solutions over and over again. What to expect: During the 8 whole day sessions, you will get the following: 1. Access to a virtual machine ready to use with R, R-Studio, all the dataset files and all the packages you will need for the boot camp. 2. A workbook including all the code snippets and exercises for all the lessons. 3. A complete set of cheat sheets for topics to be covered. 4. Each day is broken into 3 hours of topical concepts and presentations, a healthy lunch, followed by 3 hours of Lab sessions. 5. All Lab sessions are supported by an excellent instructor-to-student ration. 6. A healthy list of resources and instructions to setup your own laptop/desktop to replicate the same at your home or office.

3 Benefits 1. The course will equip you to apply Data Science in Business Analytics and improved Decision Support. 2. You can empower your career to Data Science and transition to a new job, role, project or a start-up idea. 3. You are not alone: Toolkits and community support to start working on real Data Science problems for Data Driven Insights will be provided and demonstrated. 4. You will master specific skills: R, Multiple source data acquisition, Data Analysis, Visualization, Descriptive and Predictive Analytics among many other skills. 5. The Data Science principles can apply to many other domains i.e. Marketing, Operational Excellence, Media, Logistics, Healthcare, Finance, etc.

4 Frank Hasbani, Presedent, CEO at Anova Analytics, LLC Co-Founder of Anova Analytics, leading a team of data scientists, with significant experience at delivering Data Driven Decision Support to Fortune 100 companies. Graduated with distinction from the Johns Hopkins University Data Science Specialization Certificate Program Frank Hasbani, CSM Anova Analytics (404) "Monetization Beyond Big Data" Specialization Certificate Program. A resultsdriven, highly skilled visionary in data mining technology, a leader with a strong reputation for integrity, innovation, empowerment, and collaboration with teams and clients. Proven capabilities in predictive and prescriptive analytics, web analytics, program execution and delivery, financial, compliance, banking, telco, and digital media, industrial and logistical sectors. Mark Jack is a theoretical physicist with expertise in computational modeling of quantum systems and high-performance computing. Mark is a faculty member at Florida A&M University s Physics Department in Tallahassee, FL and serves as senior advisor to Anova Analytics. Mark Jack PhD Adviser, HPC Pattern Recognition, Linear & Parallel Computing As research associate at the University of California Los Angeles Neourobiology Department, he later worked on analyzing electrical spike train statistics in retinal ganglia cells of mice using Bayes information theory and pattern recognition techniques (k-means clustering). Mark earned his PhD in physics (dr. rer. nat.) at the Humboldt University Berlin in Germany and is currently completing the Johns Hopkins University certificate in Data Science Specialization (April 2016).

5 Daniel T. Lago Engineer, Six Sigma Black Belt, PMP, PMI-ACP "Link Vision to Value Realization" Daniel Lago is an expert in process improvement, trained and worked with the Institute of Japanese Union of Scientist and Engineers (JUSE) on operational improvement methods. JUSE is the developer of the bestselling software for process analysis JUSE- StatWorks in Japan and accreditor of the Deming Prize, a globally renowned award for process excellence. He has implemented this tool and improved processes on fortune 500 companies. Most recently, he has been working with the largest enterprise software companies leading teams to implement technical solution and new processes transforming solution vision into value realization. Optional Tracks Predictive Modeling Track: We cover unique aspects of Data Science for Business Application including Data Acquisition, Data Wrangling, Model Building and Testing & Model Evaluation. Coming Soon: Machine Learning Track: We cover supervised and unsupervised learning tools that make the most accurate predictions at the expense of explaining why predictions are made. Advanced Analytics On Steroid: This track focuses on turning data into insight in days, arming you with the knowhow, tools and processes to assemble Data Products with scale and speed. Text Mining & Social Media Track: Be it a sentiment analysis, voice, video or translation, recognition of data and producing knowledge and insight are at the cutting edge of Prescriptive and Cognitive analytics.

6 Outline: LESSON 1: LESSON 3: Getting Started with R and R Studio (Frank 2 hrs) Understand Your Data Using Data Resources List Visualization RStudio Session: Check with Revolution Understand Your Data To Get The - re commercial use Best Results R Scripts Visualization Packages Univariate Visualization Crash Course in R For Developers (Frank 2 Hrs) Multivariate Visualization R Syntax Tips for Data Visualization Assignment Data Structures Flow Control LESSON 4: Functions 2 Examples Linear Regression: (Mark to prepare Packages outline) Prepare Your Data for Re- gression Modeling With Pre-Processing Need For Data Pre-Processing LESSON 2: Data Pre-Processing in R Standard Machine Learning Datasets Scale Data Practice On Small, Well-Understood Datasets Center Data Package: datasets Standardize Data Package: mlbench Normalize Data Package: AppliedPredictiveModeling Box-Cox Transform Yeo-Johnson Transform Getting & Loading Your Data Mining Datasets Principal Component Analysis - Access To Your Data (4 examples csv, Transform xlsx, url and sql) Independent Component Analysis - Load Data From File Transform Load Data From URL Tips for Data Transforms Understand Your Data Using Descriptive Statistics You Must Understand Your Data LESSON 5: Peek At Your Data Statistical Model Evaluation Metrics Dimensions of Your Data Data Types Class Distribution Model Evaluation Metrics in R Accuracy and Kappa RMSE and R Data Area Under ROC Curve Standard Deviations Skewness Correlations Tips To Remember Logarithmic Loss

7 LESSON 6: LESSON 8: Testing Predictive Algorithms Using Your Statistical Model Optional Best Algorithm for a Problem Module Algorithms to Test in R Finalize Your Model Make Predictions On New Data Comparing Performance of Predictive Algorithms Create A Standalone Model Prepare Dataset Save and Load Your Model Train Models Compare Models Choose the Best Model Tuning Predictive Algorithms Get Better Accuracy from Top Algorithms Tune Predictive Algorithms Test Setup Tune Using Caret Tune Using Algorithm Tools Craft Your Own Parameter Search LESSON 7: Resampling Methods To Estimate Model Accuracy Estimating Model Accuracy Data Split Bootstrap k-fold Cross Validation Repeated k-fold Cross Validation Leave One Out Cross Validation Tips for Evaluating Algorithms Combine Predictions from Multiple Models Increase the Accuracy Of Your Models Test Dataset Boosting Algorithms Bagging Algorithms Stacking Algorithms Outline:

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