For some reason, data exploration and cleaning are often seen as the lesser-arts of the data science world. This could not be more wrong. EDA is the only way for data scientists to really get a grasp on the problem. Exploring the data is crucial for understanding what the data really represents; rather than what we might think it represents. Indeed data often includes biases (e.g. are the label's representative of the class they are supposed to define?

Without understanding what the business and technical goals of the project are (and how they relate to each other!) then making sure an ML project is actually valuable, and that value can be measured effectively, is impossible. That is why it is so important to engage the relevant business and technical stakeholders as early as possible and ensure their goals for the project align and that these can be measured effectively. Once these goals are clear it is important to make sure everyone is on the same page and keep these business goals front and center during the entire project. Your ultimate goal is not to create a high-performing model but to reduce the time/pain/cost for real people doing real things! What your technical KPIs are (is 80% accuracy a good or bad outcome?) are totally dependent on the business goals of the project and this should be reinforced often. All too often the theoretical value of an ML project is mortally undermined by data constraints.

Learning from a textbook provides a more refined and in-depth knowledge beyond what you get from online courses. This book provides a great introduction to data science and machine learning, with code included: "Python Machine Learning", by Sebastian Raschka.

Alice decides to do some quick analysis on the trends using Kaggle Data Science survey to see what backgrounds do the current Data Science practitioners have. A majority of data scientists have college degrees, infact a majority of them have a Masters degree. So Alice would do well to go to college. But Alice is also curious of the importance of getting a degree if she wants her dream job in her dream country. Let's look at those patterns.

AutoML tools are the need of the hour for data scientists to reduce their workloads in the world where the generation of data is only increasing exponentially. Readily available AutoML tools make the data science practitioner's work more comfortable and covers necessary foundations needed to create automated machine learning modules. And with the spur in data and the potential that this data holds, data scientists will benefit more by using AutoML capabilities. As we approach the midpoint of 2020, it is slowly being recognised that this year will see an increase in adaptation of AutoML. With the massive potential of AutoML about to burst, non-data science professionals and data science practitioners will look to get a more comprehensive view on the technology.

Economic disruption is a reality which has been a gradual development over the last several decades. Artificial intelligence (AI) has simply accelerated this process. Virtually every industry has been impacted by AI and certainly data science is no exception. Yet, we may also inquire how does machine learning fit within this overall discussion. The explosion of literature on these topics over the last several years is a testament to the popularity of both topics.

As most data science practitioners know, artificial intelligence (AI) is not new and has been explored by academia back as far back as the fifties. The real core of AI is the branch of mathematics related to neural nets which have been explored both by academia as well as data science practitioners. A number of practitioners including myself familiarized ourselves with these techniques which became one more item within the data scientist toolkit. For those of us involved in using predictive analytics to predict consumer behaviour related to marketing and risk, logistic regression and decision trees in many cases performed at about the same level as neural nets. In some cases such as fraud where there were typically a much larger volume of records, neural nets did exceed the more traditional type of modelling techniques.