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  • R Programming
    • DplyrR tidyverse frame, and Dplyr plackege. Using pipe structure to chain fucntions together. Filter commands of Dplyr in R Arange functions of Dplyr in R, Rename the column name in R using Dplyr, Mutate, Select to Choose Variables/Columns, Joins, Slice, Summarise, Gather, Spread , Separate , n(),Nth,n_distinct() , na_if, coalesce , Ranking functions , Sampling, count , case_when , Group By .
    • ggplot2R graphic plackage ggplot2. Introduction of ggplot2, Start building a graph and Add Geoms in ggplot() , Using Grouping, Using Scales , Using Facets , Formulate Labels, Formulate Themes , Graphs as objects , Saving graphs , Bar charts, Pie charts , Histograms , Box plots, Kernel density plots , Violin Plots , Scatter plots , Dot plots , Stem and Leaf Plots , Tree maps , Lollipop , Diverging Bars , Colorful Display of Categorical/Character Frequencies , Nested Pie Chart using plotly , Bubble Plot , step chart , Heatmap.
    • R Advanced Data ManagementThis section and category includes mainly intermediate and more advanced techniques for R data analysis. It specially involve R mathematical functions, R Statistical functions, Probability functions in R, and R Character functions. Apply function as well as in this family, descriptive statistics, and table function family are talked about in this section too. We will also focus on how to write your own functions in R, and how to use control flow. In addition, some frequently used techniques such as reshaping, aggregating dataset are introduced.
    • R Basic Data ManagementR basic data management contains recode and rename variables, sorting , sorting data, handling missing values, using data values. There are also reshaping data, merging data , and subset of data frame in this category.
    • R Data Structure DatasetThis section introduces R data structures: One-dimensional data structure Vector; 2-dimensional data structure Matrix; n-dimensional data structure Array; tabular data structure Data Frame; and data structure List which can store any other type data objects. And this section also introduce how to create R data structure: using read.table() function to read text or csv files to create a data frame; using read.csv() function to read csv file to create a data frame. This section also introduces several functions: with().
  • Python
    • NumpyNumpy module, ndarray, data type.Numpy array include creation of ndarray, and indexing , slicing of the array, and broadcasting array, reading data to return array, etc.
    • PandasPython Pandas module. Data structure Series, Data Frame.
    • Python ClassObject oriented programming in Python uses class. Class definition contains attributes, methods creation. Particular objects(instance) belonging to a class can be created by call a class, or instantiation. The attributes of an object can be directly assigned a new value, by using methods defined in class, etc.
    • Python dictionaryDictionary is a data object type in Python. It stores key-value pairs information. The creation of a dictionary uses braces.
    • Python FunctionFunctions in Python a block of code doing some specific job. Python uses def keyword to define a function. A function can contain argument, default value for the arguments, etc. Functions can be called when it is doing real tasks. And parameters are passed to the function when it is called.
    • Python ListIntroducing lists Changing, Appending,Removing items of Lists . Sorting lists. Looping through a list. Making Numerical Lists . List comprehension and Working with Part of a List. Tuples. Building sets. Removing items from a set – remove(), pop(),and difference. Using a while Loop with Lists. Set operation.
    • Python loopingfor and while loop in Python
    • Python Machine LearningMachine learning algorithms in Python programming. The topics include document sentiment analysis, logistic regression, linear regression, and computer vision, CNN, RNN with PyTorch, Tensorflow.
    • Python StringPython denotes information stored in quotes are string, no matter it is number or words inside the quotes. Both double quotes and single quotes can be used to create a string.
  • Statistics
    • Statistics distributionStatistical distributions contains both continuous and discrete random distributions. Discrete distributions include binomial, Poisson,  Hypergeometric, Negative Binomial, Geometric. Continuous distributions contain Normal, Exponential, Gamma, Beta, Chi-square, Lognormal, t, F, and Weibull distribution.
    • Statistics Using PythonDoing 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.
    • Statistics Using RDoing and discovering statistics using R programming. R 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, etc. Linear regression and Generalized linear models using R programming. Discrete choice modeling using R programming. ANOVA, factor analysis using R programming. Clustering model using R programming.
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    • R Basic CourseR fundamental programming course.
    • Python Basic CoursePython Basic Course

wilsonzhang746

Numpy

Numpy array shape manipulation, using reshape(),ravel() and transpose() in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. Python Numpy module provides a series functions that can reshape Numpy arrays. When an array is created via Numpy, it has a shape and dimension. reshape() is used to Read more…

By wilsonzhang746, 1 yearJuly 31, 2024 ago
Numpy

Using conditional statements to select parts of NumPy arrays in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. In addition to simply using indexing to select elements from a Numpy array, Python lets you easily use conditional tests to systematically select parts of a Numpy array. In Read more…

By wilsonzhang746, 1 yearJuly 30, 2024 ago
Numpy

Using apply_along_axis() to apply an aggregate function upon an Numpy array in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. As an alternative to for looping, Numpy provides an aggregate function, apply_along_axis(), that applies various summary functions upon an array’s column or rows. The function has an option paraemter Read more…

By wilsonzhang746, 1 yearJuly 29, 2024 ago
R Programming

How to iterate over an Numpy array in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. If we want to get the contents of an Numpy array element by element, a for loop can be performed. The usage of a for loop upon an Numpy Read more…

By wilsonzhang746, 1 yearJuly 28, 2024 ago
Numpy

Indexing and slicing Numpy arrays in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. When an Numpy array is created, its elements can be returned by indexing. Indexing of array elements is implemented by using brackets. If there are several non contiguous elements Read more…

By wilsonzhang746, 1 yearJuly 27, 2024 ago
Numpy

Matrix operations of Numpy arrays in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. Matrix addition and subtraction of Numpy arrays of same size are element-wise, i.e. elements in the resulting matrix are the results of the corresponding elements of the inputting matrix Read more…

By wilsonzhang746, 1 yearJuly 26, 2024 ago
R Programming

Arithmetic operations of Numpy arrays in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. Arithmetic operations of Numpy ndarrays are element-wise. If an array has addition, subtraction, multiplication or division with a scalar, each element will have the same operation and the result Read more…

By wilsonzhang746, 1 yearJuly 25, 2024 ago
Numpy

Creating Numpy arrays using ones(),zeros(),arange(), reshape(), linspace() and random() in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. In addition to using array() function that passes a list or tuple to generate an Numpy array in Python, there are lots of ready-made functions that make it possible Read more…

By wilsonzhang746, 1 yearJuly 24, 2024 ago
Numpy

Introducing Numpy array in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. Numpy is a large module in Python programming. It handles mainly numeric data analysis, and is also the basis of another large module, Pandas in Python. The heart of Read more…

By wilsonzhang746, 1 yearJuly 23, 2024 ago
R Programming

How to calculate probability from Negative Binomial distribution in R

We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Negative binomial distribution is used to model the particular probability of k-th success occurring at x-th Bernoulli trial, which is equivalent to say that it has experienced x-1 Read more…

By wilsonzhang746, 1 yearJuly 20, 2024 ago

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