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Data Science Program (DSAIT02)


Course Content

Program Outline

  • Prerequisite Course A: Digital Business Acumen
  • Prerequisite Course B: 21st Century Business Skills
  • Course 1: Data Analytics
  • Course 2: Data Management
  • Course 3: Data Science Engineering
  • Course 4: Domain specific analytics
  • Course 5: AI and Machine Learning
  • Course 6: Capstone Project & Lab

Each course runs for three months and starts at the beginning of a quarter. January—March, April—June, July—September, and October —December. The capstone runs for four weeks at the beginning of each quarter: January, April, July, October. For exact dates for the current course run, please contact Fast Lane US.

Who should attend

Developers, Software Engineers, Data Analysts, Data Scientists, Solution Architects, Systems Engineers and curious cats.


Experience with an object-oriented programming language, e.g., Python (all code demos during the training will be in Python). There is an optional customization available to convert the course into R.

Course Objectives

This custom Fast Lane Data Science and AI program provides role-based technical training for the following five audiences:

  • Data Manager
  • Data Analyst
  • Data Engineer
  • Data Scientist
  • Machine Learning Developer

Outline: Data Science Program (DSAIT02)

Course Outline: Digital Business Acumen

Module 1: Enterprise Digital Vision and Roadmap

  • Identify the enterprise Digital Transformation vision and roadmap
  • Understand the importance of Business Model Innovation in the context to enterprise’s Digital Transformation journey.
  • Define business outcomes and strategic initiatives
  • Determine your Digital Skills, Talent, Technology and Organizational requirements

Module 2: Research and Plan

  • Assess the Digital Maturity of incumbent’s organization
  • Assess Product and Services Digital readiness
  • Assess the Digital technology readiness
  • Assess the ecosystem and partnership readiness
  • Assess the Digital cultural (‘AI ready culture’) readiness
  • Planning the Digital journey based on Digital Maturity

Module 3: Prepare and Blueprint

  • Best practice and mistakes to avoid
  • Identify the workforce requirements, technologies and capabilities
  • Address security, organizational and change management needs
  • Build your “coalition of the willing

Module 4: Implement & Scale

  • How to measure and validate success in DX projects
  • Impact of Change Management
  • Significance of Knowledge Management
  • Learn to scale to the rest of the enterprise

Course Outline: 21st Century Business skills

  • Design Thinking
  • Critical Thinking
  • Communication & Empathy
  • Collaboration
  • Creativity and Attitude
  • Planning and Organizing
  • Customer-centric Focus
  • Working with Tools & Technology
  • Dynamic self-initiated re-skilling
  • Professional Networking
  • Ethics
  • Mindfulness
  • Adaptive Mindset

Course Outline: Data Analytics

  • Statistical methods for Data Analysis
  • Machine Learning Essentials
  • Data Mining
  • Text Data Mining
  • Predictive Analytics
  • Data Visualization, Modeling & Optimization

Course Outline: Data Management

  • General principles in Data Management & Organization
  • Data Management Systems
  • Data Management & Enterprise Data Infrastructure
  • Data Governance, Privacy & Ethics
  • Large Scale Data storage
  • Digital libraries & Data archiving

Course Outline: Data Science Engineering

  • Big Data infrastructure & technologies
  • Infrastructure & Platforms for Data Science applications
  • Cloud computing technologies for Big Data & Data Analytics
  • Big Data Systems organization & engineering
  • Data Science Applications design

Course Outline: Domain Analytics – Finance, Energy (Oil & Gas), Healthcare or Manufacturing

  • Domain-specific Analytics Foundations
  • Domain-specific organization & enterprise management

Course Outline: Advanced Data Analytics

  • Graph theory
  • Information theory
  • Deep Learning
  • Anomaly detection
  • Time series analysis
  • Risk Simulation & queueing
  • Network Optimization

Course Outline: Advanced Data Management

  • Advanced Big Data storage infrastructure and operations
  • Storage architectures, distributed files systems (HDFS, Ceph, Lustre, Gluster, etc.)
  • Data storage redundancy and backup
  • Data factories, data pipelines
  • Cloud based storage, Data Lakes
  • Digital libraries and archives organization
  • Information Retrieval
  • Data curation and provenance
  • Search Engines technologies

Course Outline: Advanced Data Science Engineering

  • Infrastructure, applications and data security
  • Data encryption and key management
  • Access Control and Identity Management
  • Security services management, including compliance and certification
  • Data anonymization
  • Data privacy
  • Models and languages for complex interlinked data presentation and visualization
  • Agile development methods, platforms and tools
  • DevOps and continuous deployment and improvement paradigm
  • Decision Analysis and Decision Support Systems
  • Predictive analytics and predictive forecasting
  • Data Analysis and statistics
  • Data warehousing and Data Mining
  • Multimedia information systems
  • Enterprise information systems
  • Collaborative and social computing systems and tools
Online Training

Duration 0.1 days

Classroom Training

Duration 0.1 days


Currently there are no training dates scheduled for this course.