Home    Tutorials    Teaching & Academics    Data science tools: Basic of Statistics

Data science tools: Basic of Statistics

 Admin0

Introduction to statisrics and probability

· Students will gain knowledge about the basics of statistics

What you’ll learn

  • Students will be able to analyze, explain and interpret the data.
  • They will understand the relationship and dependency between the data and how to make the prediction.
  • Students will understand different method of data analyses such as measure of central tendency (mean, median, mode), measure of dispersion (variance, standar.
  • Students will have basic understanding of probability and Bayes theorem.
  • They will come to know about rates, ratio, odd ration and screening test.

Course Content

  • Introduction :Data and Statistics –> 4 lectures • 25min.
  • Summary measures: Central Tendency –> 3 lectures • 38min.
  • Summary measures:Measures of Dispersion –> 2 lectures • 28min.
  • Shape of data: Measures of Skewness –> 4 lectures • 50min.
  • Correlation and Regresson analysis –> 4 lectures • 1hr 11min.
  • Probability-Bayes-Theorem –> 2 lectures • 50min.
  • Discrete probability distribution –> 2 lectures • 24min.
  • Continious probability distributio:Normal distribution –> 2 lectures • 35min.
  • Rates-Ratio-odd ratio-OR –> 2 lectures • 42min.
  • Screening_test_confusion_matrix –> 4 lectures • 58min.

Data science tools: Basic of Statistics

Requirements

· Students will gain knowledge about the basics of statistics

· They will have clear understanding about different types of data with examples which is very important to understand data analysis

· Students will be able to analyze, explain and interpret the data

· They will understand the relationship and dependency by learning Pearson’s correlation coefficient, scatter diagram and linear regression analysis between the variables and will be able to know make the prediction

· Students will understand different method of data analyses such as measure of central tendency (mean, median, mode), measure of dispersion (variance, standard deviation, coefficient of variation), how to calculate quartiles, skewness and box plot

· They will have clear understanding about the shape of data after learning skewness and box plot, which is an important part of data analysis

· Students will have basic understanding of probability and how to explain and understand Bayes theorem with the simplest example

· Students will have basic understanding of discrete probability distribution such as Binomial, Poisson and continuous probability distribution such as normal distribution with details example

· They will come to know about rates, ratio, odd ratio and screening test

· They will have clear knowledge about screening test and confusion matrix with details example

· They will gain a clear idea about fundamental of statistics

· Specially, who are interested to advance their carriers in data science and machine learning should complete the course

Get Tutorial