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Data Science with Python & R Online Training
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Data Science is considered as one of the hottest jobs that one can have today. Along with elite packages that get eye-balls rolling, it also is one of the most exciting and diverse IT professions in the world. Our All-in-one Data Science Master bootcamp or Data Science with Python & R training course is designed to create a 360 degree impact on your prospects of succeeding as a Data Scientist, Data analyst or Data engineer. Exploring fundamental and advanced Data Science topics like Data Visualization, Deep Learning & Data Mining while exploring programming languages like Python and R and mathematical concepts like Statistics and probability in this exclusive and high-quality data science online course. Explore Data Structures and Algorithms for Machine Learning while picking up fundamental principles and concepts of Artificial Intelligence.

Data Science Course Highlights

  • 40 hours of Instructor Led training
  • Certified & Experienced Trainers
  • Small Size Batch
  • 1 : 1 Mentor Support
  • Access to Pre-recorded Sessions
  • Study Materials

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Data Science Course Description


Data Science is one of the most in-demand domains in the IT industry today. As processes and business get more data-driven, this demand is only going to go higher. Our course is created as an all-in-one resource that can catapult your career to the next level. Explore all the fundamental basics of Data Science while also making sure that you have enough exposure to advanced topics in this data science with Python and R training program.

The course / bootcamp covers crucial topics like making use of Anaconda, Spyder & R-studio for Python, & R, basics of Python & R, Population and Sample, Moments, Skewness & Kurtosis, Correlation analysis, probability, machine learning, Data visualization and interpretation, etc. It can be considered as one of the most in-depth fundamental Data Science online certification programs available that is highly career oriented.

According to a report by the U.S. Bureau of Labor Statistics, Data Science jobs are going to increase by about 28% through 2026. The strong skill sets imparted via this program will help you to take advantage of unique opportunities with excellent pay-grades while solving complex business problems via data-driven decisions. Confidently use top tools and practices to mine, visualize and interpret Data with the help of this Data Science with Python & R course.

Why Data Science with InfoSecTrain?

Our Data Science Master Bootcamp with Python & R is a comprehensive program taught by Industry-experts which covers the fundamentals basics as well as top programming languages in Data Science & Machine Learning.

In this data science online course, You will learn the following:

  • Building basics on Python and R programming language
  • Installing, Configuring & Using Anaconda & R Studio
  • Learn about Population & Data Sampling
  • Explore Probability for Data Science
  • Get an Introduction to Machine Learning
  • Learn about Data Visualization with Interpretation
  • Explore Deep Learning & neural Networks

And much more!

Join the Data Revolution & craft a winning professional career with this A to Z Data Science program from InfoSecTrain.

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Target Audience

  • Anyone who willing to learn Machine Learning from Scratch to Professional Level
  • Students and Working Professional


  • Basic Knowledge of Mathematics and Statistics
  • Beginner in Python & R Programming
  • Ready to Learn Passionately


5 + 66 =

Data Science Course Content

Introduction to Data Science

  • Why to learn Data Science
  • Scope of Data Science
  • Jobs In Data Science

Introduction to Python & R

  • Installation of Anaconda
  • How to launch Jupyter Notebook, Spyder and R-Studio in Anaconda
  • Shortcut keys of Jupyter Notebook and Spyder
  • How to install Packages in python
  • Download and Install R-Studio independently (without Anaconda)
  • Shortcut keys of R-Studio
  • How to install Packages in R-Studio
  • Similarities between Python & R

Basics of Python & R

  • Variables & Data Types
  • Statements and loops
  • Different types of operators
  • Functions and Modules

Population & Sample

Types of Characteristics

  • Attributes
  • Variables

Types of Data

  • Primary Data
  • Secondary Data
  • Cross-sectional Data
  • Time Series Data
  • Directional Data

Methods of Sampling

  • Simple Random Sampling with and without Replacement
  • Stratified Random Sampling
  • Systematic Sampling
  • Cluster Sampling
  • Two Stage Sampling

Summary Statistics

Measure Central Tendency

  • Arithmetic Mean
  • Mode
  • Median
  • Mean
  • Quartiles
  • Deciles
  • Percentiles
  • Geometric Mean
  • Harmonic Mean

Measure of Dispersion

  • Range
  • Semi interquartile Range (Quartile Deviation)
  • Mean Deviation
  • Variance
  • Standard Deviation
  • Mean Squared Deviation
  • Coefficient of Variation(C.V.)

Moments, Skewness & Kurtosis

  • Raw Moments
  • Central moments
  • Relation between Raw and Central Moments
  • Concept of Skewness and Its Types
  • Bowley’s Coefficient of Skewness
  • Concept of kurtosis and Its Types

Correlation Analysis

  • Types of Correlation Coefficients
  • Karl Pearson’s Coefficient of Correlation
  • Spearman’s Rank Correlation Coefficient
  • Types of Correlation
  • Positive Correlation (Poor, Moderate and Strong)
  • Negative Correlation (Poor, Moderate and Strong)
  • No Correlation


  • Conditional Probability
  • Multiplication theorem of Probability
  • Bayes Theorem

Testing of Hypothesis

  • Statistic (Estimator)
  • Parameter
  • Hypothesis
  • Null Hypothesis
  • Alternative Hypothesis
  • Type I Error
  • Type II Error
  • Critical Region
  • Test
  • Test Statistic
  • Level of Significance
  • P-Value (Observed Level of Significance)
  • Confidence Intervals

Test for Population Mean

  • One Sample Z-Test
  • Two sample Z-Test
  • One Sample t-Test
  • Two Sample t-Test

Central Limit Theorem

Test for Population Proportion

  • One Sample population Proportion
  • Two Sample Population Proportion

Paired t-Test

Test for Population Variance

  • Chi-Square Test

Test for Goodness of Fit

Test for Independence of Two Attributes

Test for Equality of Population Variances

  • F-Test

Analysis of Variance (ANOVA)

  • Complete Randomised Design
  • Randomised Block Design

Introduction to Machine Learning

  • What is Machine Learning?
  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Applications of Machine Learning

Data Visualisation with interpretation

  • Bar plot
  • Histogram
  • Pie Chart
  • Boxplot
  • Scatter plot
  • Many More……

Supervised Machine Learning

Regression Analysis

  • Simple Linear Regression
  • Multiple Linear Regression
  • Non-linear Regression (Logarithmic, Exponential, Polynomial, Quadratic, and many more)
  • Stepwise Regression (Forward Selection and backward elimination)
  • Regularization Techniques (Ridge & Lasso Regression)


  • Logistic Regression
  • Naïve Bayes Algorithm
  • K-Nearest Neighbours
  • Support Vector Machine
  • Decision Tree
  • Ensemble Techniques
  • Bagging (Random Forest)
  • Boosting (Gradient Boosting, XGBoost, AdaBoost, etc.)
  • Neural Networks (Multi-layer perceptron)

Unsupervised Machine Learning

Dimensionality Reduction Technique

  • Principal Component Analysis

Clustering Analysis

  • Hierarchical Clustering
  • Single Linkage
  • Complete Linkage
  • Average Linkage
  • Median Linkage
  • Weighted Linkage
  • Ward Linkage
  • Non – Hierarchical Clustering
  • K – Means

Association Rule & Recommendation System

  • Measure of association
  • Support
  • Confidence
  • Lift Ratio
  • Market Basket Analysis (Affinity Analysis)
  • Apriori Algorithm

Deep Learning

Introduction to Neural Network & Deep Learning

  • Deep Learning Importance [Strength & Limitation]
  • SP | MLP
  • Neural Network Overview
  • Neural Network Representation
  • Activation Function
  • Loss Function
  • Importance of Non-linear Activation Function
  • Gradient Descent for Neural Network

Parameter & Hyper parameter

  • Train, Test & Validation Set
  • Vanishing & Exploding Gradient
  • Dropout
  • Regularization
  • Optimization algorithm
  • Learning Rate
  • Tuning
  • Softmax

Convolutional Neural Network (CNN)

  • Deep Convolution Model
  • Detection Algorithm

Recurrent Neural Network (RNN)

  • RNN
  • LSTM
  • Bi Directional LSTM

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Data Science Course Benefits

Data science career benefits

Here's What people are saying about InfosecTrain

Benefits You Will Access Why Infosec Train

Student-infosectrain Certified & Experienced Instructors
24x71-infosectrain Post Training Support
tailor-infosectrain Customized Training
flexible-infosectrain Flexible Schedule
video1-infosectrain Access to Recorded Sessions

Data Science FAQs

1. Why Data Science with Us?
InfoSecTrain is a leading certification and upskilling training provider with alumni across the globe. Our trainers are high-experienced industry veterans with years of experience in the different domains and roles in Data Science. Our state-of-the-art online training materials along with access to downloadable study resources and access to pre-recorded training sessions are a boost to your Data Science Learning journey.
2. Will I get a certificate on successful completion of this course?
All students enrolling in this course are entitled to a certificate of completion that is verifiable on our website and can be displayed on your social profiles.
3. What are my career prospects after completing this course?
The objective of this data science with Python & R training bootcamp is to prepare you for your dream data science career by covering all the fundamental basics of Data Science like Python & R programming, their development environments, Probability and Data Structures for Data Science, Machine Learning, Data Visualization & Interpretation, Deep Learning, etc. Post the course competition, you will be able to apply for entry level positions in Data Science while working on more advanced specializations and tools that will further enhance your prospects.
4. What is special about this course?
Our data science with Python & R certification course covers the two significant languages necessary for a career in Data Science, Python and R programming languages. Most courses available in the industry today cover one of them leaving very little room for choice for the candidates to choose their own career paths. We take special note in covering all the basic fundamentals and programming concepts
5. How will I attend the classes and are they conducted live?
You can easily attend the classes of our data science online course from the comfort of your home or pleasure by having a stable internet connection and a working laptop. Our classes are completely delivered online while bifurcated to hour or more long sessions in LIVE Instructor-led teaching mode. Here you will get to learn and interact with your mentors/ instructors live from a browser based experience and meeting tools. Along with this, you will get secret ingredients that can act as catalysts to proceed in your career.
6. How long is my certificate valid?
The course completion certificates of InfoSecTrain’s Data Science Combo training with Python & R do not expire and can be validated on our website.after they are issued.
7. What is the payscale of Data Scientists around the world?

With the growing demand for data scientists but a scarcity of talent, companies across the world are paying high salaries to skilled professionals. A Data Science certification further increases your earning potential. Here are the data scientist’s average annual salaries across some top countries (Source: Payscale):

  • India – Rs.7,08,012
  • US – $96,106
  • Australia – A$116K
  • Canada – C$80,394
  • Singapore – SGD93,000
  • London – £50,585

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