Statistics for Supply Chain Analytics

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Why Statistics is essentials when you venture into Supply Chain data analytics?

Data plays a huge role in today’s tech world. All technologies are data-driven, and humongous amounts of data are produced on a daily basis. A data scientist is a professional who is able to analyse data sources, clean and process the data, understand why and how such data has been generated, take insights from it, and make changes such that they profit the organization. These days, everything revolves around data.

  • Data Cleaning: It deals with gathering the data and structuring it so that it becomes easy to pass this data as input to any machine learning algorithm. This way, redundant, irrelevant data and noise can also be eliminated.
  • Data Analysis: This deals with understanding more about the data, why the data has yielded certain results, and what can be done to improve it. It also helps calculate certain numerical values like mean, variance, the distributions, and the probability of a certain prediction.

How the basics of statistics will serve as a foundation to manipulate data in data science

The basics of statistics include terminologies, and methods of applying statistics in data science. In order to analyze the data, the important tool is statistics. The concepts involved in statistics help provide insights into the data to perform quantitative analysis on it. In addition to this, as a foundation, the basics and working of linear regression and classification algorithms must also be known to a data science aspirant.

How does analyzing data using statistics help gain deep insights into data? 

Statistics serve as a foundation while dealing with data and its analysis in data science. There are certain core concepts and basics which need to be thoroughly understood before jumping into advanced algorithms.

Not everyone understand the performance metrics of machine learning algorithms like f-score, recall, precision, accuracy, root mean squared error, and so on. Instead, visual representation of the data and the performance of the algorithm on the data serves as a good metric for the layperson to understand the same.

Also, visual representation helps identify outliers, specific trivial patterns, and certain metric summary such as mean, median, variance, that helps in understanding the middlemost value, and how the outlier affects the rest of the data.

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What Will You Learn?

  • Essential for understanding data science

Course Content

Over 40 Sessions. Reach to our staff for detailed catalogue

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