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Summarization

This course provides a comprehensive introduction to Data Science, focusing on its application in economics and business through 49 modules that cover statistics and practical examples, including Excel computations. Understanding statistics and economics is crucial for informed decision-making in various fields, while probability theory aids in risk analysis and modeling complex systems. The document also discusses sampling methods for data analysis and the use of specialized software for advanced statistical applications.

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shekaremrc
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3 views1 page

Summarization

This course provides a comprehensive introduction to Data Science, focusing on its application in economics and business through 49 modules that cover statistics and practical examples, including Excel computations. Understanding statistics and economics is crucial for informed decision-making in various fields, while probability theory aids in risk analysis and modeling complex systems. The document also discusses sampling methods for data analysis and the use of specialized software for advanced statistical applications.

Uploaded by

shekaremrc
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Summarization of the whole concept with an example

This course walks the learners through the basics of Data Science that helps building a strong
foundation in understanding and applying the same in economics and business. The course
is presented in 49 modules starting with introduction and gradually building on the different
concepts of Statistics to give the readers a continuous flow and flawless understanding of the
data science. Each module is explained in simple language and appropriate practical examples.
Two modules are dedicated to computing using Excel. Using an appropriately chosen data set
the computation of various statistics using Excel is explained.
Overall, an understanding of statistics and economics is essential for making informed decisions
in many fields, including business, government, and public policy. By learning how to collect,
analyse, and interpret economic data, we can gain insights into how markets and economies
work and make more informed decisions about how to allocate resources and promote economic
growth.
Probability is used in a wide range of applications, including risk analysis, decision-making,
and statistical inference. It is also used to model and analyse complex systems, such as stock
markets, weather patterns, and biological systems. In order to make accurate predictions and
decisions based on probability, it is important to have a good understanding of probability
theory, as well as the ability to apply statistical tools and techniques to analyse and interpret
data.
Sampling is a method used in statistics to select a subset of data from a population for the
purpose of analysis. The goal of sampling is to obtain a representative sample that accurately
reflects the characteristics of the population from which it was drawn.
Specialized software, on the other hand, is designed to handle specific types of analyses, such
as time series analysis, survival analysis, or network analysis. These programs may offer more
advanced statistical tools and models for their specific applications.

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