There are several ways you can use Python to connect to SQL Server and one of such ways is to use pyodbc and sqlalchemy
This is a sample code to use Python to connect to SQL Server Native Client. import sqlalchemy import pyodbc try: import urllib params = urllib.quote_plus(“DRIVER={SQL Server Native Client 11.0};SERVER=.\MSSQLSERVER_ENT;DATABASE=MagicDirect;Trusted_Connection=yes;”) engine = sqlalchemy.create_engine(‘mssql+pyodbc:///?odbc_connect=%s”‘ % params) results.to_sql(“clusterSegments”, engine, if_exists = ‘replace’)# except (RuntimeError, TypeError, NameError): print(‘Error in Conneccting’) print(RuntimeError, TypeError, NameError) finally: print(“connected”)
Presenting your report in an interactive and user-friendly way is a very effective communicative tool which can help you convey message to your audience easily. R is a very powerful analytic tool which helps you to do that with the “shiny” package. Here, I simply look at an app about the Mining Fields Evaluation which was
Sometimes, you would want to easily access and run your Python and R codes wherever you find yourself in the world once you have internet access. This is really handy when you have a mini-project you are working on , or you are going through an online tutorial, or you are trying to teach or
In Python, the scope ( Public, Protected, Private) characteristic of an attribute or member of the class is indicated by “the naming conventions of the member”. These are the naming conventions Public: This means the member can be accessed outside of the class by other instances. The naming convention denotes that it has no underscores
Here is a collection of several DataSet repositories, I have come across the web. ( I will adding more along the way) You can add to this by commenting on the post and I will update it in the this list Google Dataset Search : This is a great tool released by Google to help
It is really ideal that you define the structure of your Data Science project before beginning the project. The Data Science Project can take a couple of structures, however this is a high level guide which can help you structure and remain focused with your Data Science project Structure Credit: JHU @Coursera. Also this
This is a script to consume Azure Machine Learning Model which you build using Azure ML Studio. So basically this is a Request-Response Service (RRS) according to Microsoft. This was a Boosted Decision Tree Algorithm build with Census Adult Data which is available as one of the Sample DataSet in Azure ML. You can adapt the
These are some common basic bash commands for your quick reference Credit: Big Data For Data Engineers – Coursera Command Description awk “Aho, Weinberger and Kernigan”, Bell Labs, 1970s. Interpreted programming language for text processing. awk -F (see above) + Set the field separator. cat Display the contents of a file at the command line, is
This is a quick download of the Microsoft Azure Machine Learning Cheat Sheet. Having them handy can help save you a lot of time and it will help newbies and even experience Data Scientist and Analysts to choose the best algorithm for their dataset First, is Microsoftml Package Algorithm Cheat Sheet This cheat sheet
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