Actian Vector Jupyter Notebook using Python Tutorial Challenge

Welcome to the Actian Vector Jupyter Notebook using Python Tutorial Challenge.

Developers, data scientists, and researchers spend a lot of time working with data, and many are using Python and Jupyter Notebooks to get the job done. We need help from the TopCoder community to make it easy for Python users to connect to Actian Vector from a Jupyter Notebook, and to demonstrate how to visualize, manipulate and share the data interactively.

                    

What is Actian Vector?

                    

Actian Vector (formerly known as Vectorwise) is a high-performance vectorized columnar analytic database with a relational database engine designed for high performance analytics. Actian Vector was designed from the ground up to exploit performance features in today’s x86 CPUs such as vectorization and larger chip caches enabling in-chip analytics. Actian Vector’s record-breaking speed delivers results faster than any of its competitors. Learn more about Actian Vector here.

                    

Examples of Actian Vector use cases include:

                    

• Customer profile analytics: Granular, multi-channel, near real-time customer profile analytics can tell you about your customers, the best means to connect, the targeted offers that will resonate, their predilection to churn, and the best ways to personalize the entire customer experience to win more business and drive up loyalty levels.

• Micro-segmentation: Uncover relationships between customers and key purchase drivers and predicting the value of each customer along thousands of customer attributes, you can uncover new segments that your competition isn’t thinking about yet, increasing conversions and gaining higher returns on your marketing investment.

• Customer lifetime value: Connect to all of your data, from account histories and demographics to mobile and social media interactions, and blend these disparate sources with speed and accuracy. Uncover key purchase drivers to understand why someone purchases or rejects your products. Assign customer value scores by correlating which characteristics and behaviors lead to value at various points of time in the future.

• Next best action: Use micro-segmentation models to find and classify small clusters of similar customers. Customer value models predict the value of each customer to the business at various intervals. Combining the output of these two models into a personalized recommendation engine gives you the information you need to take action that gives you a distinct competitive advantage. You can optimize your supply chain, customize campaigns with confidence, and ultimately drive meaningful, personalized engagements.

• Campaign optimization: Traditional campaign optimization models use limited samples of transactional data, which can lead to incomplete customer views. Actian Vector allows you to connect to social media and competitor web sites in real time to learn which competitive offerings are gaining traction in the marketplace.

• Churn analysis: Churn prediction models have been limited to account information and transactional history, a tiny fraction of available data. With Actian Vector, increase the accuracy of churn predictions by combining and analyzing traditional transactional and account datasets with call center text logs, past marketing and campaign response data, competitive offers, social media, and a host of other data sources.

• Market basket analysis: Actian Vector enables data science models and advanced analytics to go deeper into detailed associations on all product relationships, and segment customers and spending habits into similar groups to learn more about shoppers.

 

Actian Vector product overview and datasheets are available here.

                    

Requirements:
 

1. Setup Actian Vector Community Edition.
   
   a) Register to request your free copy of Actian Vector Community Edition
   b) You will receive an email with instructions and important information from the following Actian email account, Marketing@bigdata.actian.com, so please check that it is not blocked by any of your email filters.
   c)  Follow the instructions in the email to

                     1. Download and install your free copy of Actian Vector Community Edition
                     2. Register for an Actian ID on the Actian community (please note your Actian ID) - If you already have an Actian ID, you can skip this step
                     3. Find technical documentation and community support resources for Actian Vector

2. Load your data into Actian Vector. The Actian Vector Community Edition will allow you to load up to 1TB of uncompressed data. You will witness the best performance the more data you add, so don't be afraid of maxing out to use the full 1TB. Here is an example of Actian Vector in action.

3. Setup a Jupyter Notebook and connect it to Actian Vector using the Python ODBC Bridge.

4. Create a tutorial that covers the installation and setup, and that demonstrates how to use the Jupyter Notebook via a proof of concept covering a real-world use case of your choice. The tutorial will be in the form of a blogpost and must include how to:
   a)  Install Actian Vector Community Edition
   b)  Create a database and load your data into Actian Vector (see the Vector documentation for more information)
   c)  Install all necessary dependencies including pyodbc, etc.
   d)  Connect to data in Actian Vector from Jupyter using pyodbc
   e)  Visualize and manipulate the data interactively via a Jupyter Notebook (Jupyter was formerly iPython Notebook) and
   f)  share your Jupyter Notebook via GitHub.

More information and examples on working with Jupyter Notebooks is available here.

                    

Need help with something?     
                

• Please use the TopCoder forums for any questions relating to completing this challenge e.g. to clarify a requirement.

• Please use the Actian Vector community forum for questions relating to how to use Actian Vector e.g. to ask a question about SQL data types supported by Actian Vector.
   

Scorecard

                    

NOTE - This challenge will be reviewed by client and there will be no appeals or appeals response phases.


We’ll use the Scorecard (1-10) for grading submissions. The submissions will be reviewed by client and there will be no appeals or appeals response phases.

 

Weightage distribution is as follows

 

Focus of assessment for Actian 

• 25% Actian Vector setup and dataset

• 25% Actian Vector connected to Jupyter Notebook via pyodbc    

• 50% Tutorial and proof of concept demonstration

                            

The blog post must be comprehensive, covering the setup fully, in clear, unambiguous terms. Your instructions must:                                                     

• be easy to read, and

• include some tests that will help make it easy for developers to validate and troubleshoot using the Jupyter Notebook with Actian Vector and pyodbc.

                            

 

Additional terms and conditions for all participants

 

By participating in this Competition, You acknowledge and agree that                                                     

• You must comply with all applicable laws in submitting a Competition Submission, and you represent that you are authorized to submit the Competition Submission.

• Actian Corporation (“Actian”) is free to use, disclose, distribute or otherwise exploit Residual Knowledge. Residual Knowledge means information that is retained in the unaided memories of Actian’s employees and contractors who have had access to any Competition Submissions submitted by You. An employee’s or contractor’s memory will be considered unaided if the employee or contractor has not intentionally memorized the information for the purpose of retaining and subsequently using or disclosing it; and

• You are not entitled to any compensation from Actian or any of the benefits which Actian may make available to its employees, and You are not authorized to make any representation, contract or commitment on behalf of Actian.

• Employees and direct and indirect subcontractors of Actian Corporation and its subsidiaries and other affiliates, and employees and direct and indirect subcontractors of Actian’s partners (including TopCoder and its affiliates) are not eligible to participate in the challenge.

• You may only use data that is open and that can be shared with anyone in the world and which is freely available and to which you have rights to use the data in submitting such data.                                 

            

Final Submission Guidelines

Submission Guidelines

                            

• A tutorial blog post featuring your proof of concept.

• A short screencast walkthrough that supports the demonstration (make the video available as an .mp4 file or share privately on YouTube or Vimeo).

• IMPORTANT: Include a file called "Submission details" with your entry. Include the following information:            

1. Actian ID – you created this when you registered on the Actian Community
2. Links to any websites, source code repositories, videos, and blog posts related to this challenge that you have online
3. Links to any and all data sources used in your submission
4. If you are one of the winners and you would like Actian to contact you about opportunities to have your entry featured on the Actian blog, please confirm your interest and provide your full name and email address
5. If you would like someone from Actian to contact you, please provide your full name, email address and a brief description of what you would like to discuss so that we can connect you with the appropriate person from Actian