I have really enjoyed and valued the Data Analytics course and how my class has been exposed to different skills as well as knowledge to analyse given arrays of data. Data analytics is the process of turning raw data into meaningful, actionable insights. This can be seen as a form of business intelligence used to solve specific problems and challenges within an organisation. It’s all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business.
The course started off with Probability Theory and its application to data occurrences and possible outcomes. Probability provides a way of summarizing the uncertainties that come from data and the likelihood of occurrence. Based on the existence of previous event or outcome, one can estimate conditional probability while calculating the different relationships of the data sets as union or complement. The probability of the complement of an event is all the other outcomes of an event. It Is sometimes easier to work out the complement first before the actual probability. The probability of a mutually exclusive event (that is events that can occur at the same time) is zero but when the events are not mutually exclusive, then the probability of the union of the two events is probability of both events added together. The conditional probability is where an event can only happen if another event has happened. The Bayes Theorem has further demonstrated how the probability can be estimated as joint and posterior probabilities when additional qualifying details are provided. The theorem which was created by Thomas Bayes allows us to estimate the probability of events given prior knowledge about events. It is more of an observation than a theorem, as it correctly works all the time. We got opportune to be exposed to probability distribution for discrete and continuous datasets. The probability distribution is considered uniform if every outcome is equally likely. Probability of any data set must always be between 0 and 1, or often represented as 0% and 100%.
Considering that we have finite resources and time in our different organizations, we were exposed to the tool to maximize and optimize resources to achieve the objectives of the business. Linear Programming was a very interesting topic, it is a simple technique to perform optimization. Linear programming is used for obtaining the most optimal solution for a problem with given constraints. The terms to note in linear programming include – the objective function, the constraints and non-negativity restrictions. The class was also exposed to Correlation and Regression of data. A correlation analysis provides information on the strength and direction of the linear relationship between a pair of variables while regression analysis estimates parameters in a linear equation that can be used to predict values of one variable based on the other. Linear Programming and Regression are simple tools used to minimize cost while maximizing profit and regression for predicting outcomes over a period Indeed, data analytics has helped to make sense of the past and to predict future trends and behaviors.
With a data-driven approach, businesses and organizations can develop a much deeper understanding of their industry, and are much better equipped to make decisions, plan ahead, and compete in their chosen market. So, rather than basing decisions and strategies on guesswork, data analytics has shown that one will be making informed decisions based on the outcome of the analysed data. #EMBA28
