Statistics for data analytics and science

Almost at the tail end of the class, about 5 sessions left and then it’s off on our own..Ok not really but what it really means is that we will either continue practicing on our own or with others to become better at analyzing and understanding data. Learning never ends and continuous practice will help us retain the knowledge longer and become “clockwork.”

So yesterday’s class lesson was a continuation of statistics, reviewing mean, median, mode, etc. I wonder if the combination of rain, colder temperatures and recovery from what feels like a short weekend due to Daylight Savings contributed to the low energy. I myself was guilty of this too, wondering  where the statistics lesson was going.

Ok back to the statistics which we thought we left behind in college but are reviewing this again. We underestimate the significance of statistics in interpreting and understanding data and this article in the Wall Street Journal echoes that sentiment. For example, we need to know if an article talking about big data is referencing a sample or an entire population because this will validate the assumptions made. In addition, we need to use statistics to chart the data to show trends, patterns or whether there is a relationship between variables. All of this can be used in a business to measure whether the amount spent towards marketing a product is yielding positive revenues and identify any outliers that may throw our results off. And finally, the information can guide decision making to justify in favor of or against a specific direction.

Do you agree with the article about statistics in big data? If the goal of big data is to analyze, predict and make sense of the data then statistics plays an equally important role.


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