R Data Science

Course Syllabus

  • R Programming, R Studio IDE setup and overview, capabilities of R
  • R Basics
    • Variables, Loops & Functions, Data Structures
    • Data Import & Export (CSV, excel), Database connectivity(SQL: MySQL, NoSQL: MongoDB)
    • JSON, HTML, XML file handling
  • Statistical Processing
  • Extract, Transformations, Loading(ETL), Data Preparation & Cleaning, Data Distribution & Analysis, Data Preparation for Machine Learning
  • Web Scrapping using R
  • Web Log processing using R
  • Machine Learning:
    • Regression: Linear, Multi Linear, Polynomial, Decision Trees, Random Forrest, SVR
    • Classification: Logistic Regression, SVM, Kernel SVM, Decision Trees, Random Forrest, Na誰ve Bayes, K Nearest Neighbor,
    • Clustering:
      • KMeans Clustering
      • Dimensionality Reduction: PCA, LDA
      • Association Rule Mining: Apriori, ECLAT
    • Neural Networks: Using H2O Deep Learning
  • Data Visualization
    • Shiny
    • ggplot
    • Bar Plot, Histograms, Pie Charts, Contour Plot, Scatter plot, Line charts
  • Sentiment Analysis
  • Exploratory Data Analysis

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