Tag: Data Preparation

• Introduction
• Tydyverse vs traditional R plotting
• Setting up your working environment
• Preparing the dataset
• Importing and filtering data
• Wrangling the data
• Visualizing the data (graphs, scatter plots)
• Grouping and summarizing the data
• Visualizing the data (line plots, bar plots, histograms, boxplots)
• Working with non-standard data
• Closing remarks

Introduction

Alteryx Overview

• What is Alteryx?
• Alteryx features

Preparing the Development Environment

• Installing and configuring Alteryx

Input Tools

• Connecting to data sources
• Writing to a data source
• Working with the browse tool

Preparation Tools

• Cleansing, filtering, and sorting data
• Working with fields and columns

Join Tools

• Merging data sets
• Finding and replacing data
• Matching data

Transform and Parse Tools

• Calculating totals and averages
• Transposing data
• Using regular expressions

In-Database Tools

• Connecting in-database tools
• Streaming data

Reporting and Documentation Tools

• Creating tables and charts
• Managing tools

Macros and R Programming

• Creating macros
• Forecasting with predictive models
• Embedding R code

Summary and Conclusion

Introduction

• The Data Science Process
• Roles and responsibilities of a Data Scientist

Preparing the Development Environment

• Libraries, frameworks, languages and tools
• Local development
• Collaborative web-based development

Data Collection

• Different Types of Data
  • Structured
    • Local databases
    • Database connectors
    • Common formats: xlxs, XML, Json, csv, …
  • Un-Structured
    • Clicks, censors, smartphones
    • APIs
    • Internet of Things (IoT)
    • Documents, pictures, videos, sounds
• Case study: Collecting large amounts of unstructured data continuosly

Data Storage

• Relational databases
• Non-relational databases
• Hadoop: Distributed File System (HDFS)
• Spark: Resilient Distributed Dataset (RDD)
• Cloud storage

Data Preparation

• Ingestion, selection, cleansing, and transformation
• Ensuring data quality – correctness, meaningfulness, and security
• Exception reports

Languages used for Preparation, Processing and Analysis

• R language
  • Introduction to R
  • Data manipulation, calculation and graphical display
• Python
  • Introduction to Python
  • Manipulating, processing, cleaning, and crunching data

Data Analytics

• Exploratory analysis
  • Basic statistics
  • Draft visualizations
  • Understand data 
• Causality
• Features and transformations
• Machine Learning
  • Supervised vs unsurpevised
  • When to use what model
• Natural Language Processing (NLP)

Data Visualization

• Best Practices
• Selecting the right chart for the right data
• Color pallets
• Taking it to the next level
  • Dashboards
  • Interactive Visualizations
• Storytelling with data

Introduction

• Overview of AWS QuickSight
• What is AWS and QuickSight

Getting Started with AWS QuickSight

• Creating an AWS and QuickSight account
• Understanding the QuickSight workflow
• Navigating the QuickSight UI

Preparing Data in QuickSight

• Understanding data preparation in QuickSight
• SPICE vs. direct query
• Uploading and importing data to QuickSight
• Working with columns and fields
• Understanding calculated fields, functions, and operators
• Adding calculated fields using strings to our project
• Extracting information out of strings
• Using conditional functions
• Creating calculated fields with numeric values
• Adding different filters to a project

Analyzing and Visualizing Data

• Understanding the difference between preparing and analyzing data
• Creating the data analysis
• Creating visuals
• Understanding dimensions and measures
• Adding additional data sets
• Field formatting, aggregation, and granularity
• Formatting visuals
• Creating a story and treemap
• Using filters and tables
• Adding a KPI visual

Exporting and Sharing Project Data

• Understanding refresh and schedule refresh
• Exporting project data as .csv files
• Adding users to an account
• Sharing data set and analysis
• Creating and sharing dashboards

Using Databases as Data Sources

• Setting up a database
• Preparing dummy data
• Connecting QuickSight to a database
• Importing data into SPICE
• Importing data as a Query
• Importing calculated fields and query
• Using NoSQL databases