Currently, there are 2 prominent project management methodologies/theories. Waterfall and agile. Most common is the Waterfall based upon PMI (but also Prince2), and agile based upon Scrum (but also Extreme Programming, Crystal Methods, Kanban etc).
Please note that very few organisations employ a pure methodology as described by the corresponding theories. There are 2 main reasons for this:
Corporations are seeking for a competitive advantage. If all were using the same methodology then they wouldn’t have an advantage. Therefore they always explore further, and as a side-effect theories are further improved.
There is always a gap between theory and praxis. Simply speaking usually there is either a lack of education or resources (time, money, manpower …) in order to implement the corresponding theory. Or simple enough the theory does not fit our case.
Personally, I do not find anything more practical than a good theory! The familiarity, and ability to implement various theories is a great asset to tackle problems.
“Come and join us, and we will show you how to predict the future”.
I tend to get invitations like this all time. Friendly people from marketing want to meet me and eventually sell tools or services to predict the future. So, it makes me wonder, if they actually have a machine to predict the future, why on earth are they spending their energy selling software and organizing events? Perhaps, you could pause for a second and consider what would you do if you had such a machine?
Although artificial intelligence has led to remarkable achievements in recent years, expectations for what the field is able and will be able to achieve in the next decade tend to run much higher than what will actually turn out to be possible. While some world-changing applications like autonomous cars are already within reach, many more are likely to remain elusive for a long time.
We mentioned in the previous section that in the last few years, there have been many attempts at a fresh approach to statistical time-series forecasting using methods like neural networks and Deep Learning.
Machine Learning and Deep Learning are subsets of Artificial intelligence, as discussed here. Deep learning excels at identifying patterns in unstructured data, which most people know as media such as images, sound, video and text.
We already have seen in the previous section how Time Series Modeling with the help of ARIMA can be realized.
In the last few years, there have been more attempts at a fresh approach to statistical time-series forecasting using the increasingly accessible tools of machine learning. This means methods like neural networks and extreme gradient boosting, as supplements or even replacements of the more traditional tools like auto-regressive integrated moving average (ARIMA) models.
‘Time’ is the most important factor which ensures success in a business. It’s difficult to keep up with the pace of time. But, technology has developed some powerful methods to enable us to ‘see things’ ahead of time. One such method, which deals with time-based data is Time Series Modeling. As the name suggests, it involves working on time (years, days, hours, minutes) based data, to derive hidden insights to make informed decision making.
A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data.
Time Series Data obviously has to do with time and the first thing that comes to mind is finance. The world of finance is the world of time series, so stocks, and currency exchange rates and interest rates, they’re all Time series.
One of the questions often addressed to me is whether an organization needs a data science team or not.
The way in which an organization will interact with data science depends a little bit on what kind of organization it is.
To some extent, it depends a lot on the size of the organization. So, when it is just a start up, when it is an early stage company, or just one person with a very small team, then we may not need to worry so much yet about how to do experimentation, how to do machine learning, how to do sort of prediction and downstream calculations. The first order of business is just making sure we keep our data in order. And the way to do that is to make sure we focus on infrastructure.