Course: Core Statistical Concepts

$89.00
$107.69 incl. vat

duration: 4 hours |

Language: English (US) |

access duration: 90 days |

Details

With the increasing value of data as a key asset, the demand for data science skills is growing rapidly. In this course, you will be taught the fundamentals of data science, including the use of sampling to produce various statistics and descriptive statistics such as measures of central tendency and distribution. You will also learn how to apply these skills in Python to manage data and statistics effectively.

Result

After completing this course, you will be able to generate samples and compute statistics using various tools and methods.

Prerequisites

No prerequisites.

Target audience

Data analist

Content

Core Statistical Concepts

4 hours

Core Statistical Concepts: An Overview of Statistics & Sampling

  • With data now being one of the most valuable assets to tap into,

  • the demand for data science skills increases by the day. Statistics
  • and sampling are at the core of data science. Use this course as a
  • theoretical introduction to using samples to reveal various
  • statistics. Examine what exactly is meant by statistics and
  • samples. Explore descriptive statistics, namely measures of central
  • tendency and of dispersion. Study probability sampling techniques,
  • including simple random sampling and cluster sampling. Investigate
  • how undersampling and oversampling are used to generate more
  • balanced datasets. Upon completion, you'll know the best way to use
  • statistics and samples for your specific goals and needs.

Core Statistical Concepts: Statistics & Sampling with Python

  • Data is one of the most valuable assets a business has, but it's only as valuable as the methods used to interpret it. Data science, which at its core includes statistics and sampling, is the key to data interpretation. In this course, practice using the pandas library in Python to work with statistics and sampling.
  • Practice loading data from a CSV file into a pandas DataFrame. Compute a variety of statistics on data. While doing so, see how to visualize the relationship between data and computed statistics.
  • Moving along, implement several sampling techniques, such as stratified sampling and cluster sampling. Then, explore how a balanced sample can be created from an imbalanced dataset using the imblearn module in Python.
  • Upon completion, you'll be able to generate samples and compute statistics using various tools and methods.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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