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March Edition: Making sense of so much data

11 Must-Read Articles

12 Useful Things to Know about Machine Learning

by James Le – 16 min read

Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled.


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The Art of Effective Visualization of Multi-dimensional Data

by Dipanjan Sarkar – 16 min read

Descriptive Analytics is one of the core components of any analysis life-cycle pertaining to a data science project or even specific research. Data aggregation, summarization and visualization are some of the main pillars supporting this area of data analysis.


Big Data will be biased, if we let it

by Federica Pelzel – 7 min read

For those of us who have the ever exciting and growing task of working with Big Data to help solve some of organization’s biggest inefficiencies, questions, or problems, perpetuating bias is a way too easy-to-make mistake, and we should all be familiarized with it by now.


Interpreting machine learning models

by Lars Hulstaert – 8 min read

Regardless of the end goal of your data science solutions, an end-user will always prefer solutions that are interpretable and understandable. Moreover, as a data scientist you will always benefit from the interpretability of your model to validate and improve your work.


How to build a data science pipeline

by Balázs Kégl – 5 min read

Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.


How To Create Data Products That Are Magical Using Sequence-to-Sequence Models

by Hamel Husain – 17 min read

I never imagined I would ever use the word "magical" to describe the output of a machine learning technique. This changed when I was introduced to deep learning, where you can accomplish things like identify objects in pictures or sort two tons of legos.


How to Datalab: Running notebooks against large datasets

by Yufeng G – 5 min read

Streaming your big data down to your local compute environment is slow and costly. In this episode of AI Adventures, we’ll see how to bring your notebook environment to your data!


10 Common Software Architectural Patterns in a nutshell

by Vijini Mallawaarachchi – 5 min read

Ever wondered how large enterprise scale systems are designed? Before major software development starts, we have to choose a suitable architecture that will provide us with the desired functionality and quality attributes.


Databases – SQL and NoSQL

by Anuradha Wickramarachchi – 4 min read

SQL came in to play with the research paper "A Relational Model of Data for Large Shared Data Banks" in 1970 by Dr. E. F. Codd. Yes!! that’s the Codd in Boyce-Codd normalization.


Architecture for Large Scale App Development

by Chamin Nalinda – 17 min read

Now it takes only a glimpse of time to analyze the petabytes of data in real-time which sent by satellites from space to earth. Remember Elon Musk highlighting a probable threat from AI which is not that far if we fail to regularize ‘AI’ which is quite crucial according to him?


A Data Science Workflow

by Aakash Tandel – 13 min read

There is no template for solving a data science problem. The roadmap changes with every new dataset and new problem. But we do see similar steps in many different projects.


We also thank all the great new writers who joined us recently, Uzma Barlaskar, Chris Rawles, Kemal Tugrul, Nathan Hubens, Janne Kool, Ike Okonkwo, Vidyasagar Machupalli, Derrick Mwiti, Niklas Donges, Tricia Aanderud, Abhinav Ralhan, John Cothran, Srinandaka Yashaswi, Zack Akil, Greg Rafferty, Ahsan Anis, Jeremie Harris, Ilyas Habeeb, Venkatesh Pappakrishnan, Ph.D., Noora Husseini, Aditya Ananthram, Francesco Zuppichini, Sukant Khurana, Hamza Bendemra, Elvan Aydemir, Jae Duk Seo and many others. We invite you to take a look at their profiles and check out their work.


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