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Introduction to Data Science and Basic Statistics for Business

In this course you will acquire statistical methods for decision making in business, as well as technological tools to develop quantitative skills.

Areas such as " big data" require very clear knowledge of statistics and business, technology provides us various applications that require solid training in statistics for proper use and interpretation .

Introduction to Data Science and Basic Statistics for Business

There is one session available:

After a course session ends, it will be archived.
Starts Dec 3
Estimated 4 weeks
5–8 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

About this course

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This course allows you to develop skills of a decision maker leader based on the following competencies:

  • analysis of statistical elements of information
  • concepts and statistical foundations for the application of the area of ​​data science

Through descriptive statistics, the study and analysis of discrete and continuous probability distributions, as well as the estimation by intervals for the mean and the proportion, it is how you will be able to develop both skills.

At a glance

What you'll learn

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Through this course, participants will be able to:

● Apply basic statistical methods to the business environment

● Use technological tools to develop quantitative skills

● Interpret the results of statistical methods applied to businesses

Topic 1: Descriptive statistics

1.1 Types of data; Measurement scales; Organization of qualitative and quantitative data; Graphic representation;

1.2 Measures of central tendency; Position measurements; Measures of dispersion; Descriptive statistics on your laptop

Topic 2: Discrete Probability Distributions

2.1 Discrete random variable; Density and cumulative probability functions; Mean and variance of a discrete random variable; Fundamental properties;

2.2 Poisson distribution ; Binomial Distribution

Topic 3: Continuous Probability Distributions

3.1 Continuous random variable; Density and cumulative probability functions; Mean and variance of a continuous random variable; Fundamental properties;

3.2 Uniform Distribution; Normal distribution

Topic 4: Interval estimation for the mean and proportion

4.1 Confidence intervals calculation; Calculation of sample size required for a given estimation error;

4.2 Student's t distribution.

About the instructors

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