- 30 hours of Instructor-led online live sessions
- 10 sessions of 3 hours each (Weekend)
- 15 sessions of 2 hours each (Weekday)
- Complimentary Self-paced course of ?R Essentials?
- Case Studies on Real-life Scenarios
- Lifetime Access to Learning Management System
- Practice Assignments
- 24X7 Expert Support
- Online Forum for Discussions
Available Courses Delivery
This course is available in the following formats:
Access live online training from anywhere taught by expert instructors
Search and study from listed class recordings and materialsView Batches
Filling Fast Delivery: Online
Fri - Sat (5 Weeks)
Timings - 08:30 PM to 11:30 PM (EST)
Weekend Batch (Evening)
Data Science certification course using R is meant for professionals who want to gain expertise in data science using R programming language. The application of R language in data analysis has a role in machine learning algorithm and statistical inference. Learners are equipped with the tools and techniques for examining, cleaning, and converting data to gather useful information.
- Train in R language, R-studio and R packages
- Teach the entire process of examining, cleaning, and converting data with the application of R programming
- Acquaint learners with the Data Life Cycle and Machine Learning Algorithms
- Make learners adept at advanced statistical concepts
- Teach various tools and techniques for data transformation
- Equip learners with real-life projects and cast studies to impart practical knowledge
- Excellent opportunity to build a successful career in Data and Analytics
- Be considered as the maven of business analytics and R programming
- Higher paycheck
- Remain in demand to work on multiple disciplines
- Become the master in dealing with real-life data-related issues
Who should take up?
- IT professionals with a keen interest in Data and Analytics
- Analytics Managers
- Business Analysts
- Information Architects
- R professionals
- Professionals who wish to enter Data Science field
- 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
- What is Statistical Inference?
- Terminologies of Statistics
- Measures of Centers
- Measures of Spread
- Normal Distribution
- Binary Distribution
- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Visualization of Data
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Supervised Learning algorithm: Linear Regression and Logistic Regression
- 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 Navies Bayes?
- Support Vector Machine: Classification
- What is Clustering & its use cases
- What is K-means Clustering?
- What is C-means Clustering?
- What is Canopy Clustering?
- What is Hierarchical Clustering?
- 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
- The concepts of text-mining
- Use cases
- Text Mining Algorithms
- Quantifying text
- Beyond TF-IDF
- 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
- Deep Learning
- 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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