Data Scientist Machine Learning - Statistics: Supervised learning: Mixed linear, GLM, ARIMA, Logistic – Logit (Propensity score match), Non-linear, Neural Networks: RNN (BI-LSTM, GRU, WTTE-RNN,AutoEncoders) Unsupervised: Clustering, Association Rules, Recommenders: Collaborative Filtering (SVD, ALS, Wals, CoClustering), Content-based filtering (TF-IDF) Statistical Packages: R (data.tables, dplyr, devtools, paraller, SparkR, Keras, Shiny, …), Python (Conda, TensorFlow, Keras, Dask, Cython, Pandas, SciPy, NumPy, matplotlib, scikit-learn, rpy2, PySpark, …), SAS (Base, Macros, SAS/STAT, SAS/ETS, SAS/SQL, SAS/ACCESS, SAS/ODS, DS2, DDE, SAS/GRAPH, %sysfunc, sasmacr, fcmp, javaobj, …), SPSS, MATLAB, Spark (Yarn, RDD, SQL, MLlib, ALS, Kmeans …) Languages / Programming: JAVA (EE, Maven, Sockets, ...), Python, VBA, C++, Scala, UML, ICONIX Design patterns: DAO, MVC, Factory, Abstract Factory Databases: MySQL (Procedures, Cursors, Triggers), H2, sqlite3, T-SQL, PL/SQL, MongoDB, Hadoop (HDFS, Zookeeper, YARN, HBase, Phoenix), Cassandra Web Development: Back end: Python (Flask + mod_wsgi, Jinja, SQLAlchemy), Java (Servlet, WebSocket, HikariCP), Front end: jQuery, AJAX, CSS, Bootstrap, nvd3.js Web Servers: Apache, Tomcat BI Dashboards: SiSense, Pentaho, Tableau Operating Systems / Scripts: Linux (CentOS, Fedora, Ubuntu, FTP, SSH, Shell), Windows Server (PowerShell, Windows Shell, PSTools, VB Script), VMware, VirtualBox, Docker Tools / IDEs: VS code, Eclipse, R-studio, Spyder, Git, MS Office ©