Data Science Introduction and Toolbox: Getting Started with Github, Introduction to Git, Introduction to Github, Creating a Github Repository, Basic Git Commands, Basic Markdown, Getting Started with R, Overview of R, R data types, and Objects, Getting Data In and Out of R, Subsetting R Objects, Dates, and Times, Control structures, Functions, Scoping rules of R, Coding Standards for R, Dates, and times, Loop Functions, Vectorizing a Function, Debugging, Profiling R Code, Simulation
Data Extraction, Preparation, and Manipulation ( R, MYSQL, HDFS, HIVE and SQOOP): Data Extraction, Downloading Files, Reading Local Files, Reading Excel Files, Reading JSON, Reading XML, Reading From WEB, Reading From API, Reading From HDFS, Reading From MYSQL, SQOOP, Reading FROM HIVE, Saving and Transporting Object, Reading Complex Structure, Data Preparation, Subsetting and Sorting, Summarizing Data, Creating New Variable, Regular Expression, Working With Dates, Data Manipulation, Managing DataFrame with dplyr package, Reshaping Data, Merging Data, Descriptive Statistics, Univariate Data and Bivariate Data, Categorical and Numerical Data, Frequency Histogram and Bar Charts, Summarizing Statistical Data, Box Plot, Scatter Plot, Bar Plot, Pie Chart, Probability, Conditional Probability, Bayes Rule, Probability Distribution, Correlation vs Causation, Average, Variance, Outliers, Statistical Distribution, Binomial Distribution, Central Limit Theorem, Normal Distribution, 68-95-99.7 % Rule, Relationship Between Binomial and Normal Distribution, Hypothesis Testing, Hypothesis Testing, Case Studies
Inferential Statistics: Testing of Hypothesis, Level of Significance, Comparison Between Sample Mean and Population Mean, z- Test, t- Test, ANOVA (f- Test), ANCOVA, MANOVA, MANCOVA, Regression and Correlation, Regression, Correlation, CHI-SQUARE
Principal Of Analytic Graph: Introduction to ggvis, Exploratory and Explanatory, Design Principle, Load ggvis and start to explore, Plotting System in R, ggvis – graphics grammar, Lines and Syntax, Properties for Lines, Properties for Points, Display Model Fits, Transformations, ggvis and dplyr, HTMLWIDGET, Geo-Spatial Map, Time Series Chart, Network Node
Predictive Models and Machine Learning Algorithm – Supervised Regression: Regression Analysis, Linear Regression, Non- Linear Regression, Polynomial Regression, Curvilinear Regression, Multiple Linear Regression, Collect Data, Explore and Prepare the data, Train a model on the data, Evaluate Model Performance, Improve Model Performance, Logistic Regression, Collect Data, Explore and Prepare the data, Train a model on the data, Evaluate Model Performance, Improve Model Performance, Time Series Forecast, Collect Data, Explore and Prepare the data, Train a model on the data, Evaluate Model Performance, Improve Model Performance
Predictive Models and Machine Learning Algorithm – Supervised Classification:Naive Bayes; Support Vector Machine; Random Forest; K- Nearest Neighbors; Classification and Regression Tree (CART) - Collect Data, Explore and Prepare the data, Train a model on the data, Evaluate Model Performance, Improve Model Performance
Predictive Models and Machine Learning Algorithm – Unsupervised:K-Mean Cluster; Apriori Algorithm; Case Study : Customer Analytic – Customer Lifetime Value - Collect Data, Explore and Prepare the data, Train a model on the data, Evaluate Model Performance, Improve Model Performance
Text Mining, Natural Language Processing, and Social Network Analysis:Natural Language Processing; Social Network Analysis; Capstone Project - Collect Data, Explore and Prepare the data, Train a model on the data, Evaluate Model Performance, Improve Model Performance, Saving R Script, Scheduling R Script
Regression Models in R [URL] - These workshop notes by Harvard University are licensed Creative Commons License. Presented by Data Science Services at IQSS.
Useful Resources:
Data Science Live Book: A book to learn data science, data analysis and machine learning, suitable for all ages! [URL]
Probability and Statistics with Examples using R. URL