My data skills

As a data scientist I need to know how to productively approach a problem. This means identifying a situation’s salient features, figuring out how to frame a question that will yield the desired answer, deciding what approximations make sense, and consulting the right co-workers at the appropriate junctures of the analytic process. All of that in addition to knowing which data science methods to apply to the problem at hand.

#data science  #big data  #predictive analytics  #artificial neural networks  #deep learning  #data mining

Tell me and I forget. Teach me and I remember, Involve me and I learn. -Benjamin Franklin


###SQL & noSQL MS SQL, PostgreSQL, mySQL, MariaDb, AWS redshift, AWS RDS, AWS DynamoDB, AWS Glue
Training @ Lynda Date / Certificate
Advanced NoSQL for Data Science September 2017
CFFEC7E7D8594BB8B35BA2F083A871E2
Microsoft SQL Server 2016 Essential Training August 2017
CFFEC7E7D8594BB8B35BA2F083A871E2
Advanced SQL for Data Scientists June 2017
29E1C4AEFD9C4205868E0D923EB02C72
Microsoft SQL Server 2016: Query Data June 2017
6557C139AAC54DCBADCA1C9128473687
Implementing a Data Warehouse with Microsoft SQL Server 2012 May 2017
A8F9E2AA3CF3441AA47B810FCFC1284B

Python

NumPy, Pandas, SciPy, matplotlib, IPython

Training @ Datacamp Date Certificate
Cleaning Data in Python September 2017 #3,989,084
Importing Data in Python (Part 2) September 2017 #3,984,535
Importing Data in Python (Part 1) September 2017 #3,945,383
Python Data Science Toolbox (Part 2) June 2017 #3,241,500
Python Data Science Toolbox (Part 1) June 2017 #3,174,571
Intermediate Python for Data Science June 2017 #3,122,746

Analysis

Tableau

Other cool stuff

Github, PHP, S3, EC2, Ubuntu Linux, Nginx, SSL, AWS