Data Science and Machine Learning with R and Python

Data Science and Machine Learning with R and Python, available at $19.99, has an average rating of 3.1, with 28 lectures, 5 quizzes, based on 24 reviews, and has 1139 subscribers.

You will learn about Solve any data analytics problem using R and Python Perform Data exploratory functions,importing and exporting the data sets,Data manipulation Perform Logistic Regression using Cancer remission data set. Data manipulation in R-Dply,Filter,multiple filter,mutate,arrange,summarize. Data visualization in R Association Analysis in R using Market Basket analysis Basics of Python Working with tuple continued,list,tuple,dictionaries(key value pair),set,In keyword,define a function,sorting using sorted function. Data mapping and indexing using enumerate,creating dictionaries using enumerate,sorting without order,reversing the list,append the list. Zip function,Iterate the value of tuple,If statement,else if statement,for loop,while,exception handling Mathematical on Array using Numpy,decalring the 2D array,3D array,dtype,numpy operations,boolean array Shape manipulation on Arrays Working with Pandas, Series, lists and dictionary Vector operation with data alignment Grouping by variable,Sorting of Data,standardization of data,apply standardized function to data frame Linear regression in python Quizzes and practice tests included for knowledge test This course is ideal for individuals who are Beginner,Intermediate as well as Advanced It is particularly useful for Beginner,Intermediate as well as Advanced.

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Summary

Title: Data Science and Machine Learning with R and Python

Price: $19.99

Average Rating: 3.1

Number of Lectures: 28

Number of Quizzes: 5

Number of Published Lectures: 28

Number of Published Quizzes: 5

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Solve any data analytics problem using R and Python

Perform Data exploratory functions,importing and exporting the data sets,Data manipulation

Perform Logistic Regression using Cancer remission data set.

Data manipulation in R-Dply,Filter,multiple filter,mutate,arrange,summarize.

Data visualization in R

Association Analysis in R using Market Basket analysis

Basics of Python

Working with tuple continued,list,tuple,dictionaries(key value pair),set,In keyword,define a function,sorting using sorted function.

Data mapping and indexing using enumerate,creating dictionaries using enumerate,sorting without order,reversing the list,append the list.

Zip function,Iterate the value of tuple,If statement,else if statement,for loop,while,exception handling

Mathematical on Array using Numpy,decalring the 2D array,3D array,dtype,numpy operations,boolean array

Shape manipulation on Arrays

Working with Pandas, Series, lists and dictionary

Vector operation with data alignment

Grouping by variable,Sorting of Data,standardization of data,apply standardized function to data frame

Linear regression in python

Quizzes and practice tests included for knowledge test

Who Should Attend

Beginner,Intermediate as well as Advanced

Target Audiences

Beginner,Intermediate as well as Advanced

Data Science with R:

Types of operators used in R,IF statements,IF then else statement,nested IF, ifelse() function,switch statement,FOR loop,While loop,repeat,break,next statement

Data types in R-Vector,Scalar,Matrix,accessing a matrix by dimensions,creating matrices from vectors

Importing the data set. Setting up the working directory,creating a duplicate file,Data Exploratory functions in R such as str, summary for descriptive statistics of data,names,head,tail,sd(standard deviation),var(variance),mean,minimum,level,dim,unique,duplicate,range,tolower,toupper.

Functions in R such as apply,lapply,sapply,tapply,mapply

Data manipulation in R-Dply,Filter,multiple filter,mutate,arrange,summarize.

Functions in R such as apply,lapply,sapply,tapply,mapply

Data manipulation in R-Dply,Filter,multiple filter,mutate,arrange,summarize.

Data visualization in R-Bar graphs,Stacked bar,grouped bar graph

Data Visualization in R

Data Visualization in R-Line chart for time series data,Box plot to calculate mean, median, min ,max ,3rd quartile and 1st quartile values

Logistic Regression using Cancer remission data set.

Clustering using Kmeans?

Association Analysis in R using Market Basket analysis

Machine Learning using R

Data Science with Python:

Basics of Python,importing sys library,platform,checking the platform,run a code,checking data type,performing calculations,concatenating of strings,conversion of values,working on tuples,

Working with tuple continued,list,tuple,dictionaries(key value pair),set,In keyword,define a function,sorting using sorted function.

Data mapping and indexing using enumerate,creating dictionaries using enumerate,sorting without order,reversing the list,append the list.

Zip function,Iterate the value of tuple,If statement,else if statement,for loop,while,exception handling

Mathematical on Array using Numpy,decalring the 2D array,3D array,dtype,numpy operations,boolean array.

Shape manipulation on Arrays,flatten the data set,reshape the data set,resize the array,split array,stacking of arrays,broadcasting,scalar on arrays,transpose function,inverse function on arrays using linalg function,sum of diagonal elements using trace.

Pandas,series containing data and label,list,series from a scalar,creating series from dictionary,access the element in series according to location using iloc()

Vector operation with data alignment,creating data frame out of dictionary of equal length list,create Data frame by combining two series,create Data frame from array.

Vector operation with data alignment,creating data frame out of dictionary of equal length list,create Data frame by combining two series,create Data frame from array

Grouping by variable,Sorting of Data,standardization of data,apply standardized function to data frame

Linear regression in python

Clustering using Kmeans

Data Visualization using matplotlib

Machine learning using Python

Quizzes and practice tests included for knowledge test

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Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Data Science using R

Lecture 1: Part 1:R Programming

Lecture 2: Part 2:R Programming

Lecture 3: Part 3:R Programming

Lecture 4: Part 4:R Programming

Lecture 5: Part 5:R Programming

Lecture 6: Part 6:R Programming

Lecture 7: Part 7:R Programming

Lecture 8: Part 8:R Programming

Lecture 9: Part 9:R Programming

Lecture 10: Part 10:R Programming

Lecture 11: Part 11:R Programming

Lecture 12: Part 12:R Programming

Chapter 3: Data Science with Python

Lecture 1: Part 1-Python Programming

Lecture 2: Part 2-Python Programming

Lecture 3: Part 3-Python Programming

Lecture 4: Part 4-Python Programming

Lecture 5: Part 5-Python Programming

Lecture 6: Part 6-Python Programming

Lecture 7: Part 7-Python Programming

Lecture 8: Part 8-Python Programming

Lecture 9: Part 9-Python Programming

Lecture 10: Part 10-Python Programming

Lecture 11: Part 11-Python Programming

Lecture 12: Part 12-Python Programming

Lecture 13: Part 13-Python Programming

Lecture 14: Part 14-Python Programming

Lecture 15: Part 15-Python Programming

Instructors

Ankita khanna
Sr Business Analyst and Data Science teacher

Rating Distribution

1 stars: 3 votes

2 stars: 3 votes

3 stars: 5 votes

4 stars: 10 votes

5 stars: 3 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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