As large banks embrace FinTech start-ups and their advanced technology rather than compete against them, both parties aim to eliminate the “middle man” in financial services transactions. This “middle man” is a metaphor for the different steps of customer interaction that technology has removed to make banking more efficient. However, in the hyper-speed environment from which bank - start-up integrations emerge, a new middle man - between these parties has become necessary.

Empire Startups, a thriving startup FinTech community, held it’s annual FinTech Conference on Tuesday, April 26 at concert venue Webster Hall.  The sold-out audience of 560 attendees consisted of early stage startups and larger companies in roughly equal proportion.

Data Science is the coveted new career around the block but not many can define the exact role of a data scientist. Being a relatively new field of work with people signing up for the role from different backgrounds, data science as a discipline requires a very broad skill set. Data mining, data analysis, machine learning, business analysis, data visualization, A/B testing are some of the skills a data scientist should have.

At Byte Academy, we teach a variety of courses within the field of Data Science. From our full-time immersive to our mini-courses in topics like Machine Learning and Data Visualization, we're consistently asked about what a career transition into Data Science looks like. To provide some guidance to our prospective students and blog readers, we've asked our own Data Scientist, Lesley Cordero, for her own perspective on frequently asked questions.

The strength and robustness of a machine learning algorithm often lies in the quality of the dataset used to train it. Therefore, it would suffice to say that to gain true mastery within these fields, it is imperative that a person gains experience over a variety of machine learning problems that deal with a variety of datasets - ranging from image processing to speech recognition.

Most publicly available datasets or datasets at the workplace are complete. However, from time to time we encounter datasets where some or many entries are missing. The problem of missing data exists on a spectrum; only a few entries missing among millions is virtually negligible, however, upwards of 10% of missing data can be crippling.

The exact problem of missing data contains multiple layers, so let us proceed to peel it like the onion it is.  At its most basic, enough missing data may skew the distribution(s) the data follows.

The Logistic Regression is an important classification model to understand in all its complexity. There are a few reasons to consider it:

Will this breakdown remain, or will the market get too heavy in those sectors and even out?