Check out our Blog
Nothing but quality content. Be sure to sign-up to our newsletter for regular updates.
Building feature engineering pipelines
Data scientists are always looking for ways to improve model performance. Of course, getting your hands on more data, trying different model types, and tweaking model parameters are all good options to get that better model fit. But what about feature engineering? And better yet, what about building a solid feature engineering pipeline?
Data science using Excel
It's not surprising to see a number of businesses using Excel for data analytics. With many citing the transparency and ease of sharing workflows, the broad level of acceptance and adoption, as well as the flexibility and power Excel's built-in functions and formula provide. But there is also a dark side to Excel. Particularly for those who have too heavily relied on the tool to serve their more advanced data needs.
A summarized list of data science concepts
As a fun exercise, our team of Datakick Collaborators spent time discussing and collating a list of data science concepts. This is only a start, but should provide a taste of the breadth of knowledge many expect from a data scientist. Feel free to take an early look before we go for round two on this.
Data science initiatives for your organization
Many industry leaders have moved beyond initial adoption and are now demonstrating and promoting real value from their data science efforts. But at the same time, many are still struggling to take the first step. So towards that, we have outlined a set of early-stage data science initiatives. With each being built around the people, data, and analytical processes of an organization.
Skills for deploying data science solutions
The role of a data scientist is evolving. And it's now more important than ever, that data scientists know how to deploy and scale their solutions for use across the wider organization. So, as a data scientist, how should you prepare yourself to confront these challenges? Which skills should you embrace and develop, versus which should you suggest are better suited for a dedicated data engineer or software developer?
The data science workflow
The data science workflow involves more than just fitting models. The problem needs to be appropriately framed, a good amount of effort needs to be spent on obtaining and preparing the data, and even after the modelling solution has been developed, there is still a piece of work around deploying and making the solution ready for use.