Tuesday, December 26, 2017

Deep Learning Skills / Data Science

Check out my Data Science Bootcamp on: http://www.qcfinance.in/python-for-data-science-machine-learning/

PDF of Pricing and Outline: http://qcfinance.in/wp-content/uploads/2018/06/Data-Science-Course-Curriculum-v3.pdf

Programming languages (Python, R, Lua, Scala …) and multiple frameworks and technologies (Tensorflow, Torch, Hadoop, Spark, RDBMS…) to support the modeling requirements

Deep learning, other AI, natural language processing, data mining, information theory, and optimization

Python, R, Lua, Scala, C++

Major deep learning libraries:. TensorFlow, Torch, DeepLearning4J


Distributed system (e.g. Spark, Hadoop, Ignite …)

Big data visualization

Substantial programming experience with almost all of the following: SAS (STAT, macros, EM), R, H2O, Python, SPARK, SQL, other Hadoop. Exposure to GitHub.
Modeling techniques such as linear regression, logistic regression, survival analysis, GLM, tree models (Random Forests and GBM), cluster analysis, principal components, feature creation, and validation. Strong expertise in regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation (transformation, binning, high level categorical reduction, etc.) and validation (hold-outs, CV, bootstrap).
Database systems (Oracle, Hadoop, etc.), ETL/data lineage software (Informatica, Talend, AbInitio)

Data visualization (e.g. R Shiny, Spotfire, Tableau)

AWS ecosystem: experience with S3, EC2, EMR, Lambda, Redshift

Data pipelines  Airflow, Luigi, Talend, or AWS Data Pipeline

APIs:  Google, YouTube, Facebook, Twitter, or Oauth

version control (Github, Stash etc.)


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