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Data science Certification Training Using R

2.45K+ Learners

StepLeaf's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR.

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Data Science With R Certification

Aug 01 Sat,Sun (7.5 Weeks) Weekend Batch 01:30 AM  03:30 AM
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Course Price at

$ 638.00

About Course

 StepLeaf’s Data Science Certification Training using R gives an good exposure and hands-on in installing R/R Studio and R package, computations in R, Graphs, continuous and categorical variables, treating missing values, feature engineering, Label encoding / One Hot encoding, Linear Regression, Decision Tree and Random Forest. 

Course Objective

This course is a benchmark for you to build a complex model in ease. It helps you to learn uncanny things and a good threshold to work with.

• Understand the lifecycle of Data Science

• Ability to work with Machine Language Techniques

• Tackle real-world data analysis project in ease

• Ability to do Text mining and Sentimental analysis with data

• Handle all domain’s data with much accuracy

Why should you go for a Data Science Course?

For every organization, a Data Scientist is the one who organizes large data sets, identifies the business clients and is helpful for marketing. They leverage the data and information which helps in building good growth strategies. Data Scientists bloom in every field in which they work and they have the unbeaten salaries around the world.

Who should take up this Certification Course? 

StepLeaf’s Data ScienceTraining Course is mainly preferred for Analytics Manager, Software Developers, Business Analytics, Integration specialists, Information Architects and ‘R’ professionals.

What are the prerequisites for this course?

Little knowledge in the basics of R programming will explode your learning into a masterpiece.


Key Skills

associationrules, datascience, dataextraction, k-meansclustering, deeplearning, r, timeseries, forecasting, textmining, c-meansclustering, canopyclustering, hierarchicalclustering, ewranglingandexploration, statisticalinference

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Course Contents

Download Syllabus

Data science certification course using R Content

Learning Objectives - Get an introduction to Data Science in this module and see how Data Science helps to analyze large and unstructured data with different tools.  

Topics:

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Big Data and Hadoop
  • Introduction to R
  • Introduction to Spark
  • Introduction to Machine Learning


Learning Objectives - In this module, you will learn about different statistical techniques and terminologies used in data analysis.
Topics:
  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution

Learning Objectives - Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.
Topics:
  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data
Hands-On/Demo:
  • Loading different types of dataset in R
  • Arranging the data
  • Plotting the graphs

Learning Objectives - Get an introduction to Machine Learning as part of this module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.
Topics:
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning algorithm: Linear Regression and Logistic Regression

Hands-On/Demo:

  • Implementing Linear Regression model in R
  • Implementing Logistic Regression model in R
Learning Objectives - In this module, you should learn the Supervised Learning Techniques and the implementation of various techniques, such as Decision Trees, Random Forest Classifier, etc.
Topics:
  • What are classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • What is Naive Bayes?
  • Support Vector Machine: Classification
Hands-On/Demo:
  • Implementing Decision Tree model in R
  • Implementing Linear Random Forest in R
  • Implementing Naive Bayes model in R
  • Implementing Support Vector Machine in R

Learning Objectives - Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
Topics:
  • What is Clustering & its use cases
  • What is K-means Clustering?
  • What is C-means Clustering?
  • What is Canopy Clustering?
  • What is Hierarchical Clustering?
Hands-On/Demo:
  • Implementing K-means Clustering in R
  • Implementing C-means Clustering in R
  • Implementing Hierarchical Clustering in R
Learning Objectives - In this module, you should learn about association rules and different types of Recommender Engines.
Topics:
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendations
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation use cases
Hands-On/Demo:
  • Implementing Association Rules in R
  • Building a Recommendation Engine in R
Learning Objectives - Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module.
Topics:
  • The concepts of text-mining
  • Use cases
  • Text Mining Algorithms
  • Quantifying text
  • TF-IDF
  • Beyond TF-IDF
Hands-On/Demo:
  • Implementing Bag of Words approach in R
  • Implementing Sentiment Analysis on Twitter Data using R
Learning Objectives - In this module, you should learn about Time Series data, different component of Time Series data, Time Series modeling - Exponential Smoothing models and ARIMA model for Time Series Forecasting.
Topics:
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting
Hands-On/Demo:
  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series Forecasting
  • Forecasting for given Time period

Learning Objectives - Get introduced to the concepts of Reinforcement learning and Deep learning in this module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for Artificial Neural Networks, and few Artificial Neural Network terminologies.
Topics:
  • Reinforced Learning
  • Reinforcement learning Process Flow
  • Reinforced Learning Use cases
  • Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN’s

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Projects

What are the system requirements for our Data Science Certification Training using R? 

1. Microsoft Windows 7 or later versions (32 bit and 64 bit)

2. 2GB memory

3. Intel Pentium 4 or later versions

4. Microsoft Server 2008 R2 or later versions

How will I execute my practicals? 

The Case Studies are executed using RStudio. The necessary instruction will be given by our StepLeaf instructor to execute all the assignments. 

What are the Case studies for this course? 

Project:#1 

Domain: Entertainment Industry 

Description 

In the bookmyshow application, collect the dataset using the parameters like movie name, duration, collection, budget, rating etc., Analyse the following ideas 

1. Find the top rated films based on IMDB rating 

2. Find the top rated films based on collections

3. Find the top rated films based on social media likes

4. Group the films based on directors, actors, genre etc.,

Project:#2 

Domain: Business Intelligence 

Description 

In a real estate business, collect the dataset and analyse the population in an area, area value, income of the people living, age etc.,

Project:#3 

Domain: HealthCare 

Description 

In a Nursing Home, collect the dataset and analyse various parameter of each patient and give analysis of the baby and the patient about their health 

Project:#4 

Domain: Food Industry

Description

In a supermarket, collect the dataset and analyse each items price, manufacturing data, expiration date, number of available products, number of moving products etc., Analyse the data and give a proper business strategy for its growth.



StepLeaf’s Data Scientist with proficiency in R Certificate Holders work at 1000s of MNC Companies All Over the World

FAQ

StepLeaf uses a blended learning technique which consists of auditory, visual, hands-on and much more technique at the same time. We assess both students and instructors to make sure that no one falls short of the course goal. 


Yes, we offer crash courses. You could get the overview of the whole course and can drive it within a short period of time.


Currently we don't offer demo class as the number of students who attend the live sessions are limited. You could see our recorded video of the class in each course description page to get the insight of the class and the quality of our instructors

StepLeaf has a study repository where you can find the recorded video of each class and all other essential resources for the course. 

Each student who joins StepLeaf will be allocated with a learning manager to whom you can contact anytime to clarify your queries

Yes we have a centralized study repository, where students can jump in and explore all the latest materials of latest technologies.

Assessment is a continuous process in StepLeaf where a student's goal is clearly defined and identifies the learning outcome. We conduct weekly mock tests, so that students can find their shortfalls and improve them before the final certification exam.

StepLeaf offers a discussion board where students can react to content, share challenges, teach each other and experiment their new skills.

You can pay your course fee online quickly through secure Razorpay gateway. You will be able to track the payment details on the way.

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