Course registration link:
https://rdatacode.com/contact-us/
Python fundamental tutorials:
Data structure, function creation, control flow, Numpy, Pandas, visualization using matplotlib.
Python statistics:
Doing and discovering statistics using Python programming.
Python functions handling calculating the density, cumulative probability, quantile and random number generation for different statistical distributions: Normal distribution, t distribution, gamma distribution, chi-square distribution, F distribution, beta distribution, Hypothesis testing, etc.
Linear regression and Generalized linear models using Python programming.
ANOVA, factor analysis using Python programming.
Clustering model using Python programming.
Python machine learning:
tensorflow, keras, scikit-learn.
List is the simplest type of data structure in Python programming. A list is used to store a collection of elements of same type (numeric, string, etc.). In Python, a pair of brackets [] indicates the data object is a list type. For example, the following two statements create two lists, in which one is numeric and the other is of string type.
Python is among the most popular programming language for data science nowadays, and getting started with Python is quite easy. You can just install e.g. a free platform like Anaconda, then you can get direct access to Python as well as most of its preinstalled modules (Numpy, pandas, matplotlib, etc.), its IDE (Spyder, etc) and its easygoing package management tools.
When we perform data analysis using Python, it is often useful to pass a list in calling the function as we want each element of the list to be accessed by the function.
A function in Python is a group of code statements wrapped together to perform specific tasks. After a function is defined, then it can be called by passing real values to its arguments and get the returning results. A while loop in Python is a group of conditional statements bundled in a statement beginning with keyword ‘while’, and the codes will run forever until the condition returns false. By including a user-defined function inside a while loop in Python, many iterative tasks can be fulfilled.
Poisson distribution is a discrete distribution. It is frequently used to model the counts of event occurrence during a specified time interval, such as telephone calls coming in to a call center in a given day. There is one parameter in the Poisson probability function, λ, which denotes the constant occurring rate in a Poisson process.
In hypothesis testing, the possibility of the other side than the conclusion usually exists, and the analysis commits so-called Type I and Type II errors, with respect to the truth and the decision made upon the random sample and hypotheses. In particular, a Type I error measures the probability that a true Null hypothesis (H0) is incorrectly rejected, and a Type II error says the probability that a false H0 not being rejected, respectively.