Tag: R Programming

Introduction

Stata and Big Data

• What is Stata?
• Stata syntax and commands

R Programming

• What is R?
• R syntax and structure

Preparing the Development Environment

• Installing and configuring Stata
• Installing and configuring R libraries and frameworks

R and Stata

• Reading and writing to Stata with R

Databases and Data in Stata

• Opening and clearing databases
• Compressing databases
• Importing and exporting databases
• Viewing, describing, and summarizing raw data
• Using tabulations and tables
• Implementing variables for data manipulation

Descriptive Analysis and Predictive Analysis

• Working with distributional analysis
• Working with Monte Carlo simulations
• Working with count data analysis
• Working with survival analysis

Hypothesis Testing

• Testing and comparing means

Graphing in Stata

• Using plots, charts, and graphs
• Working with statistical analysis in graphing
• Styling and combining graphs

Regression Models with R

• Using bivariate correlation and regression
• Working with OLS regression, logits, and probits
• Using interactive effects in regression models

Summary and Conclusion

Introduction

Algorithmic Trading Core Concepts

• What is algorithmic trading?
• Markets and trading
• Textual data and analysis

Python, R, and Stata

• Stock trading
• Bond trading
• Investment analysis

Preparing the Development Environment

• Installing Quandl
• Installing quantmod
• Installing and configuring Stata

Algorithmic Trading and Python

• Importing data
• Using Quandl
• Working with financial data
• Creating databases for financial data

Algorithmic Trading and R

• Importing data
• Using quantmod
• Working with regressions

Algorithmic Trading and Stata

• Importing and cleaning data
• Testing strategies
• Working with regressions

Summary and Conclusion

Introduction

Core Programming and Syntax in R

• Variables
• Loops
• Conditional statements

Fundamentals of R

• What are vectors?
• Functions and packages in R

Preparing the Development Environment

• Installing and configuring R and RStudio
• Setting up Rserve

Classifying Data

• Moving data between R and Tableau
• Preparing and cleaning data
• Modeling and scripting in R

Regressions in R and Tableau

• Creating a regression model
• Visualizing regressions
• Predicting and comparing values

Clustering and Models

• Working with clustering algorithms
• Creating clusters
• Visualizing clustered data

Advanced Analytics with R and Tableau

• Using CRISP-DM
• Working with TDSP models
• Summarizing data

• Connecting to various databases
• Data connection types
• Working with Single Data Sources Multiple data sources & data blending
• Tableau geocoding
• Advanced mapping + using Background Images
• Overview of additional visualizations
• Dashboards: quick filters, actions, and parameters
• Advanced calculations
• Parameters, calculations, sorting, filtering etc.
• Best practices when using Tableau R programming

Introduction

Setting up the IDE (Integrated Development Environment)

• RStudio

R Programming Fundamentals

• R objects: vectors, matrices, arrays, data frames and lists
• Flow control: branching, looping and truth testing

Accessing Data with R

• Reading and writing CSV data
• Accessing data in an SQL database

Visualizing Data with R

• Plotting with R

Analyzing Data with R

• Manipulating data frames
• Descriptive statistics

Inference and Time Series Analysis

• Analyzing time series data in financial markets
• Volatility modeling for high frequency financial data

Simulating Asset Price Trajectories

• Monte Carlo simulation

Asset Allocation and Portfolio Optimization

• Performing capital allocation, asset allocation, and risk assessment
• Regression analysis

Risk Analysis and Investment Performance

• Defining and solving portfolio optimization problems
• VaR and ES

Fixed-Income Analysis and Option Pricing

• Performing fixed-income analysis and option pricing

Taking Your R Application into Production

• Integrating your application with Excel and other web applications

Application Performance

• Optimizing your application
• R multiprocessing

Troubleshooting

Day One: Language Basics

• Course Introduction
• About Data Science
  • Data Science Definition
  • Process of Doing Data Science.
• Introducing R Language
• Variables and Types
• Control Structures (Loops / Conditionals)
• R Scalars, Vectors, and Matrices
  • Defining R Vectors
  • Matricies
• String and Text Manipulation
  • Character data type
  • File IO
• Lists
• Functions
  • Introducing Functions
  • Closures
  • lapply/sapply functions
• DataFrames
• Labs for all sections

Day Two: Intermediate R Programming

• DataFrames and File I/O
• Reading data from files
• Data Preparation
• Built-in Datasets
• Visualization
  • Graphics Package
  • plot() / barplot() / hist() / boxplot() / scatter plot
  • Heat Map
  • ggplot2 package (qplot(), ggplot())
• Exploration With Dplyr
• Labs for all sections

Day Three: Advanced Programming With R

• Statistical Modeling With R
  • Statistical Functions
  • Dealing With NA
  • Distributions (Binomial, Poisson, Normal)
• Regression
  • Introducing Linear Regressions
• Recommendations
• Text Processing (tm package / Wordclouds)
• Clustering
  • Introduction to Clustering
  • KMeans
• Classification
  • Introduction to Classification
  • Naive Bayes
  • Decision Trees
  • Training using caret package
  • Evaluating Algorithms
• R and Big Data
  • Connecting R to databases
  • Big Data Ecosystem
• Labs for all sections

Introduction

• Overview of Tableau
• Fundamentals of Python, R, and SQL

Getting Started

• Setting up the development environment
• Understanding software integration

Data Analysis with Python

• Python fundamentals and programming
• Importing libraries and datasets
• Wrangling data
• Data normalization and formatting
• Exploratory data analysis
• Performing regression analysis
• Model development and evaluation
• Visualizing Data

Data Analysis with R

• R fundamentals and programming
• Preparing data
• Classifying and working with data in R
• Using functions
• Visualizing Data

Data Analysis with SQL

• Setting up the database
• Connecting Python and SQL
• Connecting R and SQL
• SQL aggregations and joins
• Querying the database
• Manipulating data

Data Visualization Using Tableau

• Tableau design principles for visualization
• Creating dashboards, charts, and tables
• Mapping techniques
• Regressions in R and Tableau
• Advanced analytics with R and Tableau
• Practical examples and use cases

Troubleshooting