## Course content

- Octave Neural Network for Beginners
- Octave Neural Network – Advanced

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# Tag: Data Analysis

## Octave for Data Scientists

## Course content

## Mining and Analyzing LinkedIn Data

## Course content

## Prompt Engineering for Analytics

## About this course

## Skills you’ll gain

## Accelerating Python Pandas Workflows with Modin Training Course

## Scaling Data Analysis with Python and Dask Training Course

## Scientific Computing with Python SciPy Training Course

## Python Programming for Finance Training Course

## Fluentd for Log Data Unification Training Course

## Advanced Data Analysis with TIBCO Spotfire Training Course

## TIBCO Statistica Training Course

- Octave Neural Network for Beginners
- Octave Neural Network – Advanced

- Introduction
- LinkedIn datasets
- Connections between users and invitations
- Messages between users
- Final remarks

Whether you are a professional data analyst or just have data to analyze, AI models like OpenAI’s GPT can help you generate everything from analytics strategies to Python code. In this course, you will learn how to use OpenAI’s models as an AI assistant directly within your own Jupyter Notebook environment. Get ready to power up your analytics workflow with AI.

- Write effective analytics prompts
- Connect to OpenAI using an API key
- Improve performance with API parameters
- Understand and avoid common AI pitfalls

Introduction

- Modin vs Dask vs Ray
- Overview of Modin features and architecture
- Pandas fundamentals

Getting Started

- Installing Modin
- Importing Pandas from Modin
- Defaulting to Pandas in Modin
- Supported APIs

Managing Pandas workflows using Modin

- Using Modin on a single node
- Using Modin on a cluster
- Connecting to a database (read_sql)
- Optimizing resources for Modin

Interacting with Datasets

- Reading data, dropping columns, and finding values
- Executing advanced Pandas operations
- Common issues and examples

Troubleshooting

Summary and Next Steps

Introduction

- Overview of Dask features and advantages
- Parallel computing in Python

Getting Started

- Installing Dask
- Dask libraries, components, and APIs
- Best practices and tips

Scaling NumPy, SciPy, and Pandas

- Dask arrays examples and use cases
- Chunks and blocked algorithms
- Overlapping computations
- SciPy stats and LinearOperator
- Numpy slicing and assignment
- DataFrames and Pandas

Dask Internals and Graphical UI

- Supported interfaces
- Scheduler and diagnostics
- Analyzing performance
- Graph computation

Optimizing and Deploying Dask

- Setting up adaptive deployments
- Connecting to remote data
- Debugging parallel programs
- Deploying Dask clusters
- Working with GPUs
- Deploying Dask on cloud environments

Troubleshooting

Summary and Next Steps

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

Setting up the Development Environment

- Programming locally vs online: Anaconda and Jupyter

Python Programming Fundamentals

- Control structures, data types, functions, data structures and operators

Extending Python’s Capabilities

- Modules and Packages

Your first Python Application

- Estimating beginning and ending dates and times

Accessing External Data with Python

- Importing and exporting, reading and writing CSV data
- Accessing data in an SQL database

Organizing Data Using Arrays and Vectors in Python

- NumPy and vectorized functions

Visualizing Data with Python

- Matplotlib for 2D and 3D plotting, pyplot, and SciPy

Analyzing Data with Python

- Data analysis with scipy.stats and pandas
- Importing and exporting financial data (Excel, website data, etc.)

Simulating Asset Price Trajectories

- Monte Carlo simulation

Asset Allocation and Portfolio Optimization

- Performing capital allocation, asset allocation, and risk assessment

Risk Analysis and Investment Performance

- Defining and solving portfolio optimization problems

Fixed-Income Analysis and Option Pricing

- Performing fixed-income analysis and option pricing

Financial Time Series Analysis

- Analyzing time series data in financial markets

Taking Your Python Application into Production

- Integrating your application with Excel and other web applications

Application Performance

- Optimizing your application
- Parallel Computing and Multiprocessing

Troubleshooting

Closing Remarks

Introduction

- The need for distributed systems logging
- The inadequacy of conventional logging solutions

Setting up Fluentd

Overview of Fluentd Features and Architecture

Configuration File Syntax

Overview of Event Workflow

Working with Fluentd Plugins

Overview of Fluentd Use Cases

Searching Data

Analyzing Data

Collecting Data

Archiving Data

Deploying Fluentd to Production

Logging and Monitoring

Managing Performance

Managing Plugins

Configuring Fluentd for High Availability

Troubleshooting

Summary and Conclusion

- Introduction
- Overview of Spotfire Server and Spotfire Analyst
- Navigating the Web Client
- Managing Libraries
- Collaborating with Spotfire
- Creating Information Links to Access Data
- Performing Advanced Visualizations
- Beyond the GUI – Advanced Analysis through Expressions
- Creating Property Controls
- Integrating Statistical Engines
- Data Relationships and Predictive Modeling
- Multivariate Data Analysis
- Troubleshooting
- Summary and Conclusion

Introduction

Overview of TIBCO Statistica Features

- Interactive UI and workflow UI
- Analytic facilities and unique features
- Statistica Help

Getting Started with TIBCO Statistica

- Installation
- MS Office integration
- Selecting a data file
- Templates

Exploring Data Analysis Capabilities

- Commonly used statistical tests
- Elementary concepts (variables, measurement scales, relations, etc.)
- Statistical Advisor feature

Exploring Statistics Basics

- Correlations and ANOVA
- Variable bundles
- By-group analyses
- Summary results panels

Managing Data Using TIBCO Statistica

- Spreadsheet formulas
- Workspace nodes
- Managing output
- Printing, importing, and exporting

Managing Statistica Settings and Projects

- Project actions
- Customizing settings (toolbar, menus, commands)

Working with Spreadsheets, Reports, and Macros

- Using spreadsheets and workbooks
- Creating graphs and reports
- Working with macros

Exploring Advanced Analytics Topics

- Statistica Data Miner (SDM)
- Machine learning
- Engineering applications
- Design of Experiments (DOE)

Summary and Conclusion