Tag: Data Manipulation

Introduction

• SciPy vs NumPy
• Overview of SciPy features and components

Getting Started

• Installing SciPy
• Understanding basic functions

Implementing Scientific Computing

• Using SciPy constants
• Calculating integrals
• Solving linear equations
• Creating matrices with sparse and graphs
• Optimizing or minimizing functions
• Performing significance tests
• Working with different file formats (Matlab, IDL, Matrix Market, etc.)

Visualizing and Manipulating Data

• Implementing K-means clustering
• Using spatial data structures
• Processing multidimensional images
• Calculating Fourier transformations
• Using interpolation for fixed data points

Troubleshooting

Summary and Next Steps

• Introduction to Python
• Controlling the flow of your program
• Working with lists
• Working with the dictionary data type
• Manipulating strings
• Pattern matching with regular expressions
• Reading, writing and managing files
• Debugging your code
• Pulling information from the internet (web scraping)
• Working with Excel, Word, and PDF Documents
• Working with CSV and JSON
• Keeping time
• Scheduling tasks
• Launching programs
• Sending emails and other messages
• Manipulating images
• GUI Automation
• Closing remarks

Course Outline

Introduction

• What’s new in TIBCO Spotfire X?
• Overview of TIBCO Spotfire X features and architecture
• Understanding augmented and predictive analytics

Getting Started

• Installing TIBCO Spotfire X
• Upgrading Spotfire
• Navigating the UI

Loading Data

• Connecting to a database
• Configuring an on-demand data source
• Importing data from a file
• Transforming data

Processing Data

• Working with large data sets
• Connecting to streaming data
• Handling data in analysis flyout
• Managing data in data canvas
• Manipulating data tables
• Working with multiple data tables

Visualizing Data

• Types of charts, tables, maps, and other visualizations
• Interacting with visualized data
• Enhancing visualizations
• Collaborating using conversations, annotations, etc.
• Using tags, filters, lists, and other tools

Troubleshooting

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