Data Science Certification Course Using R

Data Science Certification Course Using R

OL Tech Edu'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.

The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings by 2020.

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Data Science Course UpComing Batches

Nov-17 - Dec-29

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Dec-14 - Jan-25

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Course Curriculum

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Data Science Certification Course using R

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OL Tech Edu'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.

  • WEEK 5-6
  • 10 Modules
  • 6 Hours
Data Science Training Lets You Gain Expertise
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 :b> 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.

Program Syllabus

Curriculum

You can also view the program syllabus by downloading this program Curriculum.

Curriculum

Projects

What are the system requirements for this Data Science Training?

If you have a Windows system.:

1.5 GB minimum free disk space.

If you have a MAC system, you should have: 5 GB minimum free disk space.

How will I execute the practicals in Data Science Training course?

Download RStudio Desktop Open Source License from the Rstudio Official Website for free. Or purchase the licensed Full- version of RStudio Desktop Commercial License, The detailed step by step installation guides will be present in your LMS which will help you to install and set-up the required environment.

Course Description

About The Course
About Data Science Certification Course.
  • Data Science is a "concept to unify statistics, data analysis and their related methods" to "understand and analyse actual phenomena" with data.
  • Data Science Training employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science.
  • Computer Science from the sub-domains of machine learning, classification, cluster analysis, data mining, databases and visualization.
  • The Data Science Certification Course enables you to gain knowledge of the entire life cycle of Data Science, analyse and visualise different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest and Naive Bayes.
Objectives Of Our Online PySpark Training Course
What are the objectives of our Data Science Online Course?Data Science Certification Training is designed by industry experts to make you a Certified Data Scientist. The Data Science course offers:
  • In-depth knowledge of Data Science Life Cycle and Machine Learning Algorithms.
  • Comprehensive knowledge of various tools and techniques for Data Transformation.
  • The capability to perform Text Mining and Sentimental analyses on text data and gain an insight into Data Visualization and Optimization techniques.
  • The exposure to many real-life industry-based projects which will be executed in RStudio.
  • Projects which are diverse in nature covering media, healthcare, social media aviation and HR.
  • Rigorous involvement of an SME throughout the Data Science Training to learn industry standards and best practices.
Go For Online Spark Training
Why should you go for Data Science Training?
  • Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics.
  • To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
  • Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set and interpret it for decision-making purposes.
  • Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges.
Learning With Our PySpark Certification Training
What are the skills that you will be learning with our Data Science Training?Data Science Training will help you become a Data Science Expert. It will hone your skills by helping you to understand and analyze actual phenomena with data and provide the required hands-on experience for solving real-time industry-based projects. During this Data Science course, you will be trained by our expert instructors to:
  • Gain insight into the 'Roles' played by a Data Scientist.
  • Analyze several types of data using R.
  • Describe the Data Science Life Cycle.
  • Work with different data formats like XML, CSV, etc.
  • Learn tools and techniques for Data Transformation.
  • Discuss Data Mining techniques and their implementation.
  • Analyze data using Machine Learning algorithms in R.
  • Explain Time Series and it’s related concepts.
  • Perform Text Mining and Sentimental analyses on text data.
  • Gain insight into Data Visualization and Optimization techniques.
  • Understand the concepts of Deep Learning.
Who Should Go For Our PySpark Training Course
Who should go for this Data Science Course?
  • The market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals.
  • Hiring managers are looking for certified Big Data Hadoop professionals.
  • Our Big Data & Hadoop Certification Training helps you to grab this opportunity and accelerate your career.
  • Our Big Data Hadoop Course can be pursued by professional as well as freshers. It is best suited for.
  • The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals.
  • Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data.
  • Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts.
  • Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics.
  • 'R' Professionals who wish to work Big Data Analysts wanting to understand Data Science methodologies.
Who Should Go For Our PySpark Training Course
What are the pre-requisites for OL Tech Edu's Hadoop Training Course?
  • There is no specific pre-requisite for Data Science Training. However, a basic understanding of R can be beneficial.
  • OL Tech Edu offers you a complimentary self-paced course, i.e. "R Essentials" when you enroll in Data Science Training.

Course Certification

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Features

Explore step by step paths to get started on your journey to Jobs of Today and Tomorrow.

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes.
Weekend Class : 10 sessions of 3 hours each.

Real Life Case Studies

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various real life solutions / services.

Assignments

Assignments

Each class will be followed by practical assignments.

24 x 7 Expert Support

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Certification

Certification

Towards the end of the course, OL Tech Edu certifies you for the course you had enrolled for based on the project you submit.

Course FAQ's

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