Data Science with R | Data Science R Course | Coding Elements
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Data Science with R and Python - Online

Find hidden patterns in data using statistical models in R and Python. Great for students of economics, finance, biology, and humanities. An excellent gateway into Machine Learning.


R
Python
Statistics
Data Analysis
Data Science




Online

4-year access. Live chat support with TA.



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4

Year Access!

Study at your own pace. Pause for exams. Revise for interviews.



2

Finished Projects with Code

Plus Homework Projects for College and Resume



Yes

Verified Certificate

As soon as your homework is accepted.


20,000

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Scholarship Form

Scholarship available



  • 4

    Year Access!

    Study at your own pace. Pause for exams. Revise for interviews.



  • Yes

    Verified Certificate

    As soon as your homework is accepted.



  • 4

    Year Access!

    Study at your own pace. Pause for exams. Revise for interviews.



  • Yes

    Verified Certificate

    As soon as your homework is accepted.



  • 4

    Year Access!

    Study at your own pace. Pause for exams. Revise for interviews.



  • Yes

    Verified Certificate

    As soon as your homework is accepted.



  • 4

    Year Access!

    Study at your own pace. Pause for exams. Revise for interviews.



  • Yes

    Verified Certificate

    As soon as your homework is accepted.



  • 4

    Year Access!

    Study at your own pace. Pause for exams. Revise for interviews.



  • Yes

    Verified Certificate

    As soon as your homework is accepted.



  • 4

    Year Access!

    Study at your own pace. Pause for exams. Revise for interviews.



  • Yes

    Verified Certificate

    As soon as your homework is accepted.



  • 4

    Year Access!

    Study at your own pace. Pause for exams. Revise for interviews.



  • Yes

    Verified Certificate

    As soon as your homework is accepted.

  • 1
  • 2
  • 3

20,000

Original23,000

Scholarship Form

Scholarship available


How to Enroll

Fill out this scholarship form.

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Because of high number of enquiries the waiting time may be upto 24 hours.





Data Science with R and Python - Online - Recent Placements

Best off-campus placements and career services


Coding Elements programming institute off-campus placement at Fidelity
Python & Data Science course student Arushi Arora
Coding Elements coding institute off-campus placement at Fidelity

Python & Data Science

Congratulations Arushi Arora for receiving offers from Fidelity and other companies.

Coding Elements programming institute off-campus placement at UrbanClap
Python & Data Science course student Devam Khurana
Coding Elements coding institute off-campus placement at UrbanClap

Python & Data Science

Congratulations Devam Khurana for receiving offers from UrbanClap and other companies.

Coding Elements programming institute off-campus placement at IBM
Python & Data Science course student Jitin Bahri
Coding Elements coding institute off-campus placement at IBM

Python & Data Science

Congratulations Jitin Bahri for receiving offers from IBM and other companies.



Data Science with R and Python - Online Course - Curriculum

We will assess the student and adjust the curriculum for optimal learning.



  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA
  • laptop_chromebook

    Programming Basics and Logic

    • Basic syntax & Control flow
    • If-else Conditions
    • Loops
    • Functions
    • Lambda functions
  • laptop_chromebook

    Data Structures and Algorithms

    • Arrays
    • List Comprehension
    • Strings
    • Tuple
    • Set
    • Dictionary
  • laptop_chromebook

    Data Science

    • Files (csv, json)
    • Essential modules (time, random)
    • Data science (numpy, pandas)
    • Graphs and Plots
    • Kaggle
  • laptop_chromebook

    Machine Learning

    • Linear Regression
    • Logistic Regression
    • Clustering
    • Classification
    • Anomaly Detection
    • Ensembling: Bagging, Boosting
  • laptop_chromebook

    Web and API

    • Flask
    • Web APIs
    • Web scraping
    • Web automation
    • Web sockets
    • Git Essentials
    • Cloud server deployment
  • laptop_chromebook

    R Basics and Logic

    • Basic syntax & Control flow
    • Conditions
    • Loops
    • Functions
  • laptop_chromebook

    Data Types & Data Structures

    • R Objects
    • Vector and subsetting
    • Vector operations
    • Matrix and subsetting
    • Matrix operations
    • Lists
    • Data Frames and subsetting
    • Mixin, Coercion
  • laptop_chromebook

    Tools & Utility

    • Missing values
    • Date & Time
    • Apply family
    • File Input/Output
    • Text vs Binary
    • Large datasets
    • Downloading from web
  • laptop_chromebook

    Data Science & Data Analysis

    • Random Sampling
    • Replacement
    • Simulation
    • Vectorization
    • EDA
    • Outlier Detection
    • Normality Test
    • Correlation Analysis
    • Parametric Tests
    • Non-Parametric Tests
    • Data Visualization
    • Dplyr Package
  • laptop_chromebook

    Algorithms and ML

    • Theory
    • Linear Regression
    • Validation Analysis
    • Residual Analysis
    • Case Studies
    • Sentiment Analysis
    • Clustering KMeans
    • Clustering Hierarchical
    • PCA



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Data Science with R and Python - Online - Course Overview


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    Solid Foundation in the field of Data Science.</b></Data>

    Designed to help you build a solid foundation in the lucrative field of Data Science.
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    Uncover hidden trends and behavior patterns in data using R

    Uncover hidden trends and behavior patterns in data using R.
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    Learn methods, algorithms and processes

    Learn methods, algorithms and processes to extract knowledge and insights from structured and unstructured data.
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    Projects

    2 projects with code where you can apply the knowledge you have learnt.
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    No Prior knowledge Required

    No prior knowledge of coding needed

What students will learn in Data Science with R and Python - Online Course?


Students will learn the core concepts of Data Science and gain an in-depth understanding of the field by working on the projects. The concepts covered are:

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    R Basics and Logic

    R is a programming language and free software environment for statistical computing. You will learn the basic syntax and Control Flow, Conditions, Loops and Functions in R.
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    Data Types & Data Structures

    You will cover topics like R Objects, Vector and subsetting, Vector operations, etc.
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    Tools & Utilities

    You will learn various important tools and utilities like Missing Values, Date and Time, Apply family, etc.
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    Data Science & Data Analysis

    You will cover Random Sampling, Replacement, Simulation, Vectorization, EDA, Outlier Detection, Normality Test, etc.

What are the future prospects after taking Data Science with R and Python - Online Course?


Data Science is a very upcoming and lucrative field and brings with it a large number of career options and possibilities. There is a chance for very high paying jobs in this field. Some of the career options available are:

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    Data Scientist

    Data Scientists possess the capability to analyse large data and come up with valuable insights that help businesses. Thus they are a highly skilled set of professionals who not just need to know how to work with data, but should have the knowledge of statistical methods and algorithms.
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    Data Analyst

    Data analysts analyse various kinds of data and come up with data-driven insights. They help businesses make decisions based on their study and findings. They are much needed in the industry due to the impact of Big Data.
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    Data Engineer

    They use many techniques and toolsets to find insights in large amounts of data. They are the ones who create the big data applications. They have to work along with Data Analysts and Data Scientists to find business solutions.
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    Other Opportunities

    Apart from these, there are many other options like Data Architect, BI Analyst, Researcher, etc.

How Coding Elements is different:


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    Industry Level Course with Faculty Support

    The course is built by experienced professionals who have created this course to give you a complete learning experience in Data Science. You can ask your queries to dedicated Teaching Assistants who will help solve them.
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    Course Certificate is Provided After Completion

    You can avail a verified certificate after successfully clearing the course which you can showcase on your resume.
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    Students get Real World Project Experience

    As part of the course, you will get a chance to work on projects which will help you gain confidence in the field and will also help you during your interviews.
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    The Course Offers High Flexibility

    You can learn whenever you get time since the courses are very flexible. This helps you take up this course along with your college education.





Student Reviews - Data Science with R and Python - Online


Which is the best site to learn python online with certification?


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Yash Mehra, Online Student

Python Online for Data and Web

The python online course provided by coding elements is the best among all. They’ve well designed and curated course that helps in better understanding. And more importantly, their courses cover Data Structure, Data Science and Web Designing too!


Which is the best training institute of Python in Delhi NCR?


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Anmol Khurana, Student at Delhi Public School

Python for Data Science

I have to commute a lot to attend classes at Coding Elements. I don't mind. Because every single class has been worth the tiresome journey. I think, you are one of the best teachers I've had. Probably the best. I hold you in quite high an Esteem. I think you are really good at teaching the things you teach, but you are also good at listening to the doubts and getting to the core of issues, and understanding what is really bothersome, and also at being patient, as the process of 'learning' has its fits and starts and patience is ultimately the way to go, no matter what other virtues you may have. Also, the non-judgmental approach you take towards us, that's quite a relief in itself. It's a lot of pressure off the back of any student who is trying to master a new skill. I am hyperactive and it is really hard for me to keep my interest in one place for months on end. But, in this case, I think Im hooked to programming for a long long time, thanks to you, for the way you helped structure and create my paradigm towards programming and the field of computing in general. And all the conversations we have had apart from 'Python', they were very meaningful in itself. And you shared freely, everything that you could, about computing and other topics, and I think, this too, will help me cast my career in a different light. In recent days, you have become a friend, a teacher and a mentor. And writing this was my way of saying 'Thank You'.


Which is the best institute for coding in Delhi?


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Pushkar Mittal, Computer Science student

Machine Learning

Well, When it comes to coding, and such stuff like machine learning, the key is individual attention, and experienced faculty. because Students lag behind in large batches which makes it difficult to cope with the course piled up. I suggest Coding Elements for this. It is accessible, Faculty is experienced, have small batches and most importantly are dedicated to teach.






Frequently Asked Questions - Data Science with R and Python - Online



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    What are the prerequisites to Data Science with R and Python - Online?

    None. There are no prerequisites for this course. It is a good option for students of Economics, Finance, Biology, and Humanities.

    If you have had no prior coding experience, you are eligible for this course.

    If you have had mediocre coding experience, you are also eligible for this course.

    You may skip this course if you know most of the concepts listed in the curriculum.
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    How will my doubts get resolved?

    We have dedicated Teaching Assistants who will help you during this course.

    Every Teaching Assistant is assigned 5-8 students.

    You can approach them via WhatsApp and Email.

    You can also schedule Live Doubt Solving Sessions.
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    How many hours of content does this course have?

    This course has 30 hours of content.
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    Can I download the recordings of this course?

    No. We do not allow downloading of the course videos.

    On the other hand, you will have access to the course for 4 Years.

    We have allocated a generous Watch Time Quota (Total no. of hours you can watch the content), enough to watch the entire content three to four times over.

    Online TA chat support will be available for 1 year, but you will still retain access to Hints, Doubt forum, Practice questions, Test cases, and Videos.
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    How long will it take to complete Data Science with R and Python - Online?

    If you dedicate 5hr/week to this course, you will be able to complete it in 1-2 months.
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    How many students are there in a batch?

    Every Teaching Assistant is assigned 5 - 8 students.

    The TAs work closely with the students, helping them understand the content, and carving out the right learning path for them.
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    Are there any Projects and Assignments in Data Science with R and Python - Online?

    We have 3 Projects in this course.
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    For how long do I have access to the course?

    You will have access to the course for 4 Years.

    Also, a reasonable limit has been applied to your Total Watch Time.

    Total Watch Time is the total number of hours for which you can watch the content.

    We have allocated a generous Watch Time Quota (Total no. of hours you can watch the content), enough to watch the entire content three to four times over.

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    Does Data Science with R and Python - Online offer a certificate?

    Yes.

    If you complete all the Projects and 70% of the Assignment Questions, you will be awarded a certificate.
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    What career prospects can I look forward to after completing this course?

    You can look forward to becoming a Data Analyst, Data Scientist, Data Engineer, and tons of other exiciting fields of study.
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    Is there a Free Trial available before enrollment?

    Yes. A Free Trial is available.

    You can apply for a Free Trial here.
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    Will there by any Placement Assistance at the end of this course?

    Yes. We do provide Placement Assistance at the end of this course.

    You can contact our Placement Managers for any help.

    We are also keen on building a longterm relationship with our students.

    We will provide help with choosing the appropriate profiles and positions that the tech industry has to offer.





Online

4-year access. Live chat support with TA.


LIVE
Augmented Classroom Courses



Lifetime access to Practice | Four Year Video Access | Live Doubt Sessions Daily



Lifetime access to Practice | Live Teaching Sessions | Live Doubt Sessions Daily






Coding is for everyone. This course will be an investment in your future.