Data Science Certification Course Using R
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Syllabus
Introduction to Data Science
• 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
Statistical Inference
• What is Statistical Inference?
• Terminologies of Statistics
• Measures of Centers
• Measures of Spread
• Probability
• Normal Distribution
• Binary Distribution
Data Extraction, Wrangling and Exploration
• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Visualization of Data
Introduction to Machine Learning
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning algorithm: Linear Regression and Logistic Regression
Classification Techniques
• 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
Unsupervised Learning
• What is Clustering & its use cases
• What is K-means Clustering?
• What is C-means Clustering?
• What is Canopy Clustering?
• What is Hierarchical Clustering?
Recommender Engines
• 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
Text Mining
• The concepts of text-mining
• Use cases
• Text Mining Algorithms
• Quantifying text
• TF-IDF
• Beyond TF-IDF
Time Series
• 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
Complimentary sessions on communication presentation and leadership skills.
Benefits from the course
Mode of Teaching
Live Interactive
Data Science is greatly in demand. Prospective job seekers have numerous opportunities with high salary package.
Software professionals who already have Big Data background and aspire to delve into developing automated learning features of a system
There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries.
Data Science training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naïve Bayes using R.
Learners have the opportunity to implement real-life use-cases on Media, Healthcare, Social media, Aviation, and HR.
Prerequisite:
Basic understanding of R
Prerequisites
- Basic understanding of R
Course Duration:
30 Hours
Class Hours:
2 Hours Daytime slots or 3 Hours week end Slots (May change)