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Data Science / Machine Learning using R

Overview

Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.
Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.

Duration : 40-50 Hours

Prerequisites : Basic knowledge of Programming Language. Basic knowledge of Database and files.

Training Highlights : Course covers from Beginner to Expert Proficiency Levels. All our trainers have extensive experience in IT Industry and have more than 3 years of experience in teaching.

Course Syllabus

Data Science / Machine Learning using R
  1. Fundamentals of Statistics
  2. Introduction to Statistics
    • Types of data
    • Measures of central tendency and dispersion
    • Statistical Graphics
  3. Probability and Probability Distributions
    • Binomial Distribution
    • Poisson Distribution
    • Normal Distribution
  4. R Programming Basics
    • Introduction to R
    • Data Types
    • Reading data, Subsetting Data
    • Visualizing the Data
    • Input Output Sub setting
    • Control structure
    • Functions
    • Data Exploration
    • Data Harmonization
  5. Descriptive & Inferential Statistics
    • Estimation Theory
      • Sampling Distribution
      • Point Estimation
      • Interval Estimation
    • Sampling Distribution
    • Test of Hypothesis
      • Inference about one population means
      • Inference about two populations means
      • Analysis of Variance Concept
    • Inference about one & two population (Means & Proportion)
    • Analysis of Variance ( 1 Way & 2 Way)


  6. Machine Learning: Supervised Algorithms
    • Introduction to Machine Learning
    • Naïve Bays Algorithm
    • K-Nearest Neighbor Algorithm
    • Decision Tress (SingleTree)
    • Regression
      • Correlation coefficient
      • Simple Linear Regression
      • Multiple Linear Regression
      • Logistic Regression
    • Time Series Analysis
      • Moving Average
      • Simple Exponential Smoothening
      • Holt-Winter's Method
      • ARIMA Models
    • Support Vector Machines
    • Random Forest
    • Support Vector Machines
    • Model Ensembling
      • Bagging
      • Boosting
      • Stacking
  7. Unsupervised Learning Algorithms
    • Cluster Analysis
      • Hierarchical Clustering
    • K-means Clustering
    • Association Rules Mining
    • Principal Components Analysis
  8. Natural Language Processing
    • Term Document Matrix
    • TF-IDF
    • Word Cloud
    • Recommendations Systems
  9. Neural Network

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