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)

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