1. Understanding the field & setting expectations

2.  Personal  International  UNT Alumni (Mathematics)  Academic  Economics & Mathematics  Professional  Academic Research, Hilton, Ansira, Sabre

3.  Analytics: Discovery and communication of meaningful patterns in Data  Data Science: The novel application of algorithms and statistical techniques to solve business problems.  Reality: Different meanings at different companies  The culture of the company determines the nature of work that you do  A relatively new field  Most Companies are in the process of defining their analytics strategy  Titles common to the field:  Data Scientist, Analytics Consultant, Statistical Modeler, Risk Analyst, Statistician.

4.  Forecasting  “Predictive Analytics”: Classification  Logistic Regression, SVM, Random Forest, Gradient Boosting  Fraud, Customer Acquisition  Customer Retention/ Churn Modeling  Who is likely to leave for a competitor  Recommendation Engines  Netflix Challenge  Customer Choice Modeling  What will people buy  Multinomial Logit Model  Optimization  Market Mix Modeling  Clustering/ Market Basket Analysis

5.  Most Companies house their data in relational databases  Oracle, Teradata, IBM DB2, Microsoft SQL  SQL queries used to retrieve data  SQL: a basic entry level requirement to work in this field  Most of tasks require significant amounts of time and energy combining tables and data  Hadoop -An open source distributed framework for storing and processing large amounts of data  Petabytes  Java based  Map-Reduce  Pig, Hive-SQL syntax-Facebook, Impala-SQL syntax, Spark  Spark – UTD offers a Spark Course  HTML  JSON

6.  Statistical Programming Languages  R- Open Source, easy to learn, unparalleled no. of packages and functionality, Memory Limitations.  SAS – Very Common in Businesses but losing popularity, expensive, losing market share to R, handles large data sets well.  Python – Versatile, reasonable no. of packages, R’s biggest competitor.  Matlab – More common in Engineering field.  General Programming Languages  JAVA – Not knowing java has cost me at least 4 jobs.  C/ C++ - For writing faster R programs  Scala – Spark more common among people on the forefront of development

7.  Search for positions you are overqualified for.  More likely to sponsor you  State your status as soon as possible  Some companies have policies against hiring international students.  myvisajobs.com  See companies that are sponsoring  See salaries for negotiation purposes  Others.

8.  SQL  Fundamental Requirement  Experience with Large Data Sets  10k records is no large  SAS/ R  Take courses  Free courses at UNT  Very Strong in at least one area (Optimization, Forecasting, Classification)  Specialize in something  Linux Experience  Get exposure  JAVA  Learn it.  Multiple Projects (At least 3)- Code Research Paper, Apply a technique to company data, participate in Kaggle, do internship.

9.  Universities  UTD – School of Management/ Operations Research  OSU (Oklahoma) – Analytics and Data Mining Programs  UNT-Economics  SMU- Statistics  Economics, Mathematics, Statistics, Operations Research, Computer Science, Engineering.  Companies  AT&T, Sabre, Epsilon, Amazon,  AnalyticRecruiting.com (lots of Phone Interviews), Kforce.com (Very Promising and takes care of Visa issues)

10.  Kaggle.com  The Home of Data Science  Company recruiting & Pays winners  Many Kaggle winners manage Analytics teams  Compete! Get recognized.  Internships are extremely important  AT&T, Sabre, Epsilon, Amazon, Santander, Capital One in Plano  Companies prefer to hire Mathematicians  Never accept first offer  Jumping around vs. Staying at one company  They always divide by 2  Dallas R user group- Network  Meetup.com – Network  Informs local chapter

11.  The Elements of Statistical Learning: Data Mining, Inference and Prediction.  The Art of R Programming  The Theory and Practice of Revenue Management