As we entered the 1990s, the Berlin Wall was gone, the Soviet Union and Yugoslavia were disintegrated, and Europe changed forever.
Club football, especially in Europe, too changed forever in the early 1990s.
A Champion’s League began, a Premier League in England took away much of the glamour the Italian Serie A enjoyed before and the clubs earlier behind the Iron Curtain began losing their grip on European football.

Parallelly, a data revolution began in many countries in the world. It was in its nascent stage but the seeds were sown for a huge upheaval.
As we entered the 21^st century, there was a massive development in the data front. Storing data became cheaper and hence the urge to gather and store more of it became of paramount importance.

Sports, in general, understood that there is a chance to be a part of this data revolution. Already the money injected into and generated from sports was beyond any conceivable ceiling that was ever there.
Given the appeal and size of the market, the marriage between sports and data was just a matter of time.

American sports (Baseball, followed by others) took the plunge. And it generated results in no time!
Data has shown the way in team selection, injury management, one to one matchup and even helped understand certain technical aspects.
The teams that had faith in data reaped tangible benefits, and it showed that the influence of data science on sports was here to stay.

Football, being the number one global game, followed suit.
As the ownership of clubs like Manchester City and Paris Saint-Germain changed hands, the money football began enjoying was almost beyond belief.
And it paved the way for cutting-edge technology and the most sophisticated usage of data.
The technical areas of the teams began expanding in no time, with them carrying more staff than players.

International football, too, was not far behind. European nations like Germany and Spain understood the value of investing in data.
And the greatest stage to use it was surely the World Cup.

(to be continued)

The post Parallel World Of Data Science & FIFA World Cup appeared first on Jigsaw Academy.

I interact with aspirants regularly and have found that many among them have somehow got this idea. I have tried to understand the reasons at the basic level and here’s what I could think of:

1.  The predominance of Python as a tool has, rather unfortunately, helped many believe that a minimal knowledge and use of statistics while building the model(s) is fine and there is very little chance of facing any issues.
2. Aspirants have also been keen to follow a certain set of rules and/or instructions, understand the code required for it, and implement it to reach a plausible conclusion. Statistics does not play a major role while one carries out this sequence of operations and hence there probably has been a tendency to overlook it.
3.  Understanding a subject is always harder than getting comfortable with a tool. So, unless it becomes necessary, not many are interested to invest time and energy on knowing the subject.

There can be other reasons as well but these, in my opinion, broadly, takes care of the majority in question.

Let’s try to underline how knowing the subject can make an aspirant confident and help him/her become a ‘data scientist’.

When aspirants learn the basics of data science, they often encounter predictive models at an early stage. The presence of sophisticated tools have made life easier for us and, with knowledge of the underlying assumptions and how to check them, it is usually straightforward to build these models.
And thus, it is not necessary to even know the null hypothesis one tests while building a simple regression model.

Until things work well.

And unless issues crop up.

Understanding of the theory and framework becomes useful when things don’t go as per plan.
A bad multicollinearity or heteroscedasticity problem can be dealt with most effectively if it is known why it happened. To know this ‘why’, it is important to understand the statistics involved.

The other advantage is that it can help separate the excellent data scientists from merely the good ones. The knowledge of the subject can be used to understand if things are going fine or not. Else, it might well happen that after spending hours, in the end, one realises that the effort has proven to be futile.

Machine Learning and Deep Learning are attractive terms but there is a hierarchy which must be followed. If the basics of statistics and predictive modelling is not done properly, it is usually difficult to comprehend the advanced topics.

Building a second and third floor on a fragile ground floor is never a great idea…..

The post Data Science Myth buster : Knowledge of statistics is not mandatory appeared first on Jigsaw Academy.