My Data Analytics Understanding:

Maimuna Onakoya Written by Maimuna Onakoya · 2 min read >

Data Analytics, also known as DA in the EMBA class is something u have always been interested in. To be, Data Analytics knowledge enables you to crunch data and use data to tell a story while also forecasting what will happen in the near future.

According to jake Frankenfild, hr defined d Data analytics is the science of analyzing raw data to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity.

Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

I took a lot of interest in the Data Analysis and was happy that this course is to be taken. Though I have struggled a little through the class, this is an important course for me to understand (not to say that all other courses are not important). This course is to be understood for various reasons, from predicting the future of a transaction,


Probability is the likelihood that an event will occur. Probability Distribution

Understanding Probability Distribution

What is Probability Distribution? What are different types of Probability distributions? How will it help in framing Data Science Solutions?.

Let me try to explain it in very simple words

Definition:-A probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment.

Rolling a Dice gives me a set of outcomes distributed in a particular way , where as the marks of a particular subject of a class gives me another distribution and occurrence of car accidents in a particular year follows an entirely different distribution and so on. Different Distributions help us to know more about the data and its characteristics. It helps to understand what could be the possible outcome if it follows a particular distribution.

Binomial Distribution

The binomial distribution is used when there are exactly two mutually exclusive outcomes of a trial, like Heads or Tails while flipping a coin, raining or not raining tomorrow ,winning or losing a match. These outcomes are appropriately labeled as “success” and “failure”. The binomial distribution is used to obtain the probability of observing x successes in N trials, with the probability of success on a single trial denoted by p. The binomial distribution assumes that p is fixed for all trials.


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