Description
SUMMARY OF TECHNICAL SKILLS * Data Science / Statistics - Statistics I: Machine Learning/Data Mining/Predictive Modeling, Time Series, Regressions, - Statistics II / Statistical programming languages: SAS, R, Spark, Python (Numpy, Pandas, IPython, ), Scala, MatLab - Database querying language: SQL - Statistics III: Null hypothesis, P-Value, Maximum Likelihood Estimators, Confidence Intervals, . - Statistics IV: K-Nearest Neighbors, K-Means Clustering, Naive Bayes, Random Forests, Neural Network, Decision Tree, Ensemble Methods, SMA, Exponential Smothing, ARIMA, Correlation/Variance Analysis - Data Wrangling: cleaning up data by identifying corrupt (or impure) data and correcting/deleting them. - Data Visualization: ggplot2, plot, scatterplot 3D persp, 3D scatterplot3d EgoogleMaps, . - Software Engineering: data structures like Queues, Arrays, Lists, Tuples, Dict, Trees, * NOSQL Databases MongoDB, Cassandra, HBase * Hadoop technologies - HDFS, MapReduce, Yarn Pig, Hive, Sqoop, ZooKeeper, Flume, Parquet, Avro, Crunch - Hadoop Administration * Relational Databases SAS, Sybase (ASE vers. 16.x, 15.x, 12.x, 11.9.2), Oracle 12c /11g, MS SQL Server /Access * Amazon Web Services (AWS) S3, EC2, EBS, VPC, ElastiCache, CloudWatch, DynamoDB, RDS, Redshift
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