Designing Smart Financial Dashboards with AI and Tableau

Turn Live Market Data into Actionable Insights with AI-Powered Tableau Dashboards

Duration
15 Hours
Schedule
11 – 13 November 2025
09.00 – 15.00 WIB
Habitare
Jl. H. R. Rasuna Said No.Kav.20, Karet Kuningan, Kecamatan Setiabudi, Jakarta Selatan 12940

WORKSHOP STARTS IN

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Overview

This 15-hour hands-on workshop empowers professionals in finance and investment sectors to transform real market data into intelligent, predictive visualizations. Using live datasets from Sectors financial data suite, participants will master Tableau’s full visualization workflow while learning how to integrate Python-based AI models via TabPy. The course blends Tableau best practices like advanced calculations, LODs, and interactivity with machine learning capabilities, enabling teams to go beyond dashboards to not only describe but also explain and predict. No prior Python or AI experience is required.

Course Syllabus

Introduction to Tableau and Tableau Workflow

Connecting Tableau to Live Financial Data from Sectors APIlow

Tableau File Types and Extensions
Creating a Live Data Connection
Connecting Tableau to Sectors API through Spreadsheet
Understanding Physical and Logical Layers
Setting Up Relationships Across Tables

Organizing and Preparing Financial Data

Data Filtering and Date Filters
Sorting by Ticker, Sector, Index Weight
Creating and Using Financial Hierarchies (e.g., Sector > Subsector > Stock)
Grouping Entities (Banks, Commodities, Tech)

Visualizing Market Movements and Financial Trends

Bar and Line Charts for Stock Movement
Highlight Tables for Financial Performance
Scatter Plots for Risk vs Return
Trend Lines, Reference Lines for Benchmarks
Using the Analytics Pane for Quick Insight

Slicing Time-Based Financial Data

Discrete vs. Continuous Dates in Market Context
Comparing Index and Sector Movements Over Time
Shared and Dual Axis for Price vs Volume Views

Defining Segments and Subsets in Finance Data

Using Sets for Financial Clustering
In/Out Sets for Portfolio Inclusion
Nested Sorting for Market Capitalization Ranking

Financial Calculations in Tableau

Using Calculated Fields for KPI Derivation (e.g., YoY Growth, Volatility)
Aggregating Ratios and Index Contributions
Introduction to LOD for Financial Summaries

Table Calculations for Financial Metrics

Scope and Direction: YoY Change, Rolling Averages
Using Quick Table Calculations for Trend Analysis
Custom Table Calculations for Sector Indexing

Distributions and Outlier Detection in Financial Data

Box and Whisker Plots for Sector Variability
Bins and Histograms for Volume Distribution
Pareto Charts for Top Gainers Analysis

Mapping Financial Data by Region

Using Maps to Visualize Regional Performance
Custom Symbol Maps for Branch/Exchange Data
Integrating Geo Fields with Market Indicators

Parameters for Dynamic Financial Views

Creating What-If Scenarios
Using Parameters with Reference Lines
Interactive Metrics Switching

AI Concepts and Clustering in Tableau

What is AI in Business Intelligence
Built-in Tableau Clustering for Grouping Stocks
Introduction to TabPy and its Capabilities
Setting Up a TabPy Environment

Introduction to Python for Machine Learning

What is Python and why it’s used in AI
Key Python libraries: Pandas, NumPy, Scikit-learn
Understanding DataFrames, Arrays, and ML workflows
Writing simple Python scripts for data analysis
Preparing data for modeling tasks (cleaning, splitting, scaling)

Python & TabPy Integration with Tableau

Writing Python Scripts to Analyze Financial Data
Calling Models from Tableau Calculated Fields
Passing and Receiving Data Between Tableau and Python
Debugging and Structuring Finance-Specific Python Scripts

AI Modeling and Predictive Analytics in Tableau

Running Python ML Models (Stock Return Prediction)
Embedding Output into Tableau Visuals
Visualizing Predictions, Confidence Intervals, and Model Metrics

Dashboard Design and Deployment for Financial Storytelling

Dashboard Layouts for Financial Stakeholders
KPI Panels, Sector Tabs, Forecast Modules
Filters and Actions for Drilling Into Stock Movement
Publishing and Sharing Secure Tableau Reports

Course Receivables

Lecturer’s Notes
Including Course Books (PDF), HTML files, course transcripts (if any).
Highly-accelerated Learning
Learn under the assistance of mentorship of our lead instructor and a band of qualified teaching assistants throughout each course.
Certification of Completion
Show current employer hat you’ve completed the course with a signed certificate of completion.
Quality Learning Environment
We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, and small group size.
Refreshments & Coffee Break
In our commitment to delivering a premium experience, we collaborate with leading catering services in Jakarta. Our aim is to ensure that all participants are served delectable lunches and revitalizing coffee breaks.

ABOUT THIS SERIES

Courses in this series cater to a diverse audience: from casual learners and working professionals to those venturing into data science and machine learning without a programming background.

We recognize that many students may not have prior expertise in statistics, mathematics, or algebra. Therefore, our courses are designed with a gentle learning curve, placing a strong emphasis on hands-on experience and individualized instruction. Our dedicated team of instructors and teaching assistants ensure personalized guidance every step of the way.

Teaching Methodology

Students work through tons of real-life examples using sample datasets donated by our mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.

YOUR INSTRUCTOR

Muhammad Dzaky Jayalaksono
Business Intelligence Instructor at Algoritma Data Science School

Dzaky has a strong understanding and proficiency in platforms for business analytics, enabling valuable insights for data-driven decision-making. As a detail-oriented professional, He possesses the technical thinking skills to identify innovative solutions for business problems and is deeply committed to the company’s advancement. Orchestrating the end-to-end business intelligence process from defining the business problem to conducting thorough analysis, visualizing insights, and deploying impactful dashboards. Bring a comprehensive skill set to drive the entire lifecycle of business intelligence. He is motivated to continuously learn and grow, keep collaborating, and provide exceptional abilities to drive success.

Get In Touch

PT. Algoritma Data Indonesia.
RDTX Square, 9th Floor. Setiabudi, DKI Jakarta 12930.
Office: 0816-692-471
Email: community@algorit.ma

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