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StatisticalForecasting InternshipVaisala Inc.AnnaSiniloSeptember12,2016
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Agenda
§ Howtofindaninternship:tips§ Howtopassaninterview:tips§ AboutVaisala andteamIworkedfor§ Myproject§ StatisticaltechniquesIused§ ToolsandtechnologiesIused§ Mycoursework
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Howtofindaninternship:tips
§Whatworkedforme:§ Different[datascience]meet-ups(broadensmind)§ CareerCenteratSJSU(resumereview,spartan jobs,on-campusinterviews,talks)
§ Godirectlytocorporatewebsites§ Tailoryourresumetoeveryapplication(qualitymattersalot!)§ Haveaspreadsheetwithapplicationinfotocontrolyoursearch
§Whatdidn’twork:§ LinkedInandotherjobboards(toocompetitive)§ Staffingagencies
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Howtopassaninterview:tips
§Whatworkedforme:§ Cheatsheetwithcommonquestionsandmyownanswers§ Knowgeneralinfo(aboutcompany,interviewer)§ Beconfidentand“keeptalking”§ Showmotivationandhowyoufittothisrole
§Whatdidn’twork:§ Askingtorepeat/orexplainthequestion§ Givinglonganswers
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AboutVaisala Inc.
§ HeadquartersinFinland,Helsinki§ Develops,manufacturesandmarketsproductsandservicesforenvironmentalandindustrialmeasurement(sensors,radiosondes,radars,etc.)
§ Operatesin140countries§ Celebratesits80th yearofoperation§ Over1500employees§ GiantLeapInternshipProgram(~25internseverysummer)§ Requirementsformyproject:studentinstatistics,knowledgeofforecastingmodels,python/matlab
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Myofficeandteam
§ LocatedinSeattle,WA§ Officeincludesforecastingandassessmentteams§ IworkedinForecasting:§ Peopleinmyteam:– 5people(notincludingme)– 2 withbackgroundrelatedtoMeteorology,2– AtmosphericScience,1– Statistics
§ Produceforecastsandotherconsultingservicestoclients(ownersofwind/solarsites,airports,etc.)
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Myproject
Myproject:§ seasonalforecastingofwindandsolarenergygenerationonmulti-monthaheadhorizons
WindAnomalies
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StatisticaltechniquesIused
§ Exploratorydataanalysis:§ Basicstatisticalmeasures:mean,std/var,correlations,etc.§ Plots
§ Featureengineering§ Dimensionalityreduction(PCA)§ Missingvaluesandoutlieranalysis
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PlotsSubsetofpredictors
(overallnearly300predictors)4typesofresponse
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StatisticalmodelsIused§ Regression§ Modelevaluation:
§ MAE,MSE,RMSE,L-norms
§ Classification(2-class)§ Modelevaluation:
§ accuracy,precision,recall,areaundertheROCcurve
§ Timeseries§ Modelevaluation:
§ MAE,MSE,RMSE,L-norms
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ToolsandtechnologiesIused§ Python(Ipython/Jupyter Notebook,PyCharm IDE)§ Packages:numpy,scipy,scikit-learn,pandas,statsmodels,seaborn,etc.
§ R(RStudio IDE)§ Packages:quantmod,ggplot2,etc.
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Mycoursework
§ ClassesIrecommend:§ Math163ProbabilityTheory§ Math261aRegressionAnalysis§ Math265TimeSeriesAnalysis§ Anymachinelearning/dataminingclass(possiblyonline)§ Kaggle competitions
§ Finding:§ Learningaparticulartechnology– manageable§ Learninghowtointerpretoutput,implementnewconcepts– muchharder
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Thankyouforyourattention!
9/12/16 AnnaSinilo13