Why Pace 2.O for Visual Analytics With Tableau

1. Introduction to Big Data

• Introduction to Big Data, State of the practice in analytics
• Current Analytical Architecture
• Drivers of Big Data, Emerging Big Data Ecosystem
• Big Data Analytics Project Life Cycle: Overview, Phase 1- Discovery, Phase 2- Data preparation, Phase 3-Model Planning, Phase 4- Model Building, Phase 5- Communicate Results, Phase 6- Operationalize.
• Introduction to Machine Learning

2. CORE PYTHON PROGRAMMING LANGUAGE

• Programming Language: Python
• Tools Usage: REPL Online, Anaconda (Jupyter Notebook / Spyder), PyCharm, Tableau, SubLime Text
• Library/Package Usage: Datetime, Statsmodels, NumPy, Pandas, Seaborn, Matplotlib

• Module 1: Introduction to Python, What is Python and history of Python?, Unique features of Python, Python-2 and Python-3 differences, Install Python and Environment Setup, First Python Program, Python Identifiers, Keywords and Indentation, Comments and document interlude in Python, Command line arguments, Getting User Input, Python Data Types, What are variables?, Python Core objects and Functions, Number and Maths.
• Module 2: List, Ranges & Tuples in Python, Introduction, Lists in Python, More About Lists, Understanding Iterators, Generators , Comprehensions and Lambda Expressions, Introduction, Generators and Yield, Next and Ranges, Understanding and using Ranges, More About Ranges, Ordered Sets with tuples
• Module 3: Python Dictionaries and Sets, Introduction to the section, Dictionaries, More on Dictionaries, thon Sets, Python Sets Examples text files, writing
• Module 4: Input and Output in Python, Reading and writing Challenge, Writing Binary Files Manually, Text Files, Appending to Files and Using Pickle to Write Binary Files
• Module 5: Python built in function, Python user defined functions, Python packages functions, Defining and calling Function, The anonymous Functions Loops and statement in Python, Python Modules & Packages
• Module 6: Python For Data Analysis Pandas : What is pandas?, Where it is used?, Series in pandas, Index objects Reindex, Drop Entry, Selecting Entries, Data Alignment , Rank and Sort , Summary Statics, Missing Data, index Hierarchy, Matplotlib: Python For Data Visualization.

3.DATA VISUALIZATION TOOLS & TECHNIQUES

• Relational Plots
• Relplot | Scatterplot | Lineplot
• Categorical Plots
• Catplot | Stripplot | Swarmplot
• Boxplot | Violinplot | Barplot
• Countplot
• Distribution Plots
• Jointplot | Pairplot| Distplot
• Regression Plots
• Lmplot | Regplot | Residplot
• Matrix Plots
• Heatmap | Clustermap
• Tableau Products and Usage
• Basic Charts on Tableau
• Connecting with Multiple Sheets and Data Sources
• Tableau Filters and Visualization Interactivity
• Interaction and Grouping Data
• Time Series Chart
• Maps and Images in Tableau
• Advanced Charts in Tableau and Analytical Techniques/li>
• Calculations on Tableau
• Tableau Integration with Other Tools

3.GETTING STARTED WITH TABLEAU

• The Tableau application suite
• Installing the Tableau Desktop
• Data preparation
• The sample dataset
• The Tableau worksheet
• Working with measures and dimensions
• Working with marks
• Saving, opening and sharing workbooks

5. ADDING DATA SOURCES IN TABLEAU

• Selecting data tables
• Joins
• Unions
• Data extracts and live connections
• Editing the model metadata
• Data types
• Adding hierarchies, calculated fields and table calculations
• Data collections

6.CREATING DATA VISUALIZATIONS

• Chart Types
• Ready, set, show me
• Bar charts, legends, filters and hierarchies
• Line charts
• Highlight tables
• Heat maps
• Bullet charts
• Cumulative sums with waterfall charts
• Anatomy of a Tableau visualization

7.AGRREGATE FUNCTIONS AND CALCULATED FIELDS

• Setting up a data connector
• Aggregate Functions
• Calculated fields
• Aggregation in calculated fields
• Test operators
• Date fields
• Logical functions in calculated fields
• Parameters
• Searching text field
• Different type of calculations
• Level of detail expressions

8.MAPS IN TABLEAU

• Symbol maps
• Filled maps
• Density maps
• Map layers
• Maps with pie charts
• Anatomy of Tableau maps
• Alternative map services

9.TRENDS, FORCASTS AND CLUSTERS

• Tableau analytics pane
• Constant, average and reference lines
• Trend lines
• Forecasts
• Cluster analysis
• Python integration

10. INTERACTIVE DASHBOARD DEVELOPMENT IN TABLEAU

• Creating a dashboard
• The dashboard pane
• Placing charts on the dashboard
• Dashboard titles
• Navigation buttons
• Dashboard actions
• Best practices