Data Science for
Business Professionals
DSZ - 210
Overview
Course Objective
Who Should Attend
Prerequisites
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Define Data Science Terms and Concepts
• Data science
• Data analytics
• Descriptive analytics
• Predictive analytics
• Prescriptive analytics
• Diagnostic analytics
• Statistical analysis
• Artificial intelligence
• Machine Learning
• Programming tools
• APIs
• Web scraping
• Velocity of data
• Data types
• Big data
Module 2 - Describe The Data Science Lifecycle
• Discovery
i.Data acquisition
ii.Sourcing
• Data preparation
i.Exploration
ii.Profiling
iii.Pre-processing
iv.Cleansing
• Model planning
• Model building
i.Running
ii.Testing
iii.Revising
• Model implementation
• Communication of results
Module 3 - Improve Customer Experience (CX)
• Personalized customer experience
• Sentiment analysis
• Recommender systems
• Self-service support
• Chatbots
• Virtual assistants
Module 4 - Improve Marketing Efforts
• Audience segmentation
• Targeted advertising
• Campaign optimization
• Measurement analysis
Module 5 - Optimize Organizational and Transactional Security
• Fraud detection
• Minimize loan defaults
• Reduce intellectual property theft
• identify and mitigate risks
• Cybersecurity
Module 6 - Enhance Operational Practices
• Predict system or component failure
• Optimize sales forecasting
• Implement dynamic pricing
• Identify reasons for customer churn
• Optimize talent acquisition
• Optimize transportation and logistics
Module 7 - Develop A Data-centric Organization
- Preparing Organizations for Data Science Implementation
i. Define Potential Contribution of Data Science to the Organization
ii. Align Data Science with Corporate Strategy
iii. Align Data Science with Organizational Resources
iv. Align Data Science with Organizational Culture
Design Thinking
Critical Thinking & Objectivity
Accurately Communicating Results
Business Acumen & Data-Driven Decisions
Learning Organization, Professional Development & Upskilling
- Preparing Teams for Data Science Implementation
i. Talent Acquisition
ii. Training
iii. Combination of Roles
Module 8 - Develop An Implementation Strategy
- Identify business case for using data science
- Identify organizational investments to be made to implement data science projects
- Identify factors related to data discovery
i. Collection of relevant data
ii. Methods of collecting relevant data
iii. Tools for generating data
iv. Methods for storing data
v. Tools for storing data
vi. Appending data - Identify factors related to data preparation
i. Data wrangling/munging
ii. Cleaning data
iii. Tools for preparing data
iv. Analyzing data
v. Tools for data analysis - Identify factors related to modelling
i. ETL process
ii. Model types
iii. Model testing
iv. Model revision
v. Evaluation of model results
vi. Model implementation - Identify factors related to communicating model results
i. Document key findings
ii. Create visual representation of insights
iii. Develop reports and other assets for stakeholders - Identify factors related to data visualization
i. Audience
ii. Charts
iii. Tables
iv. Tools for data visualization
Module 9 - Describe The Impact of Data Science on Business
- Impact on Overall Business Operations
i. Reputational Impact
ii. Talent Gaps (expertise, training, and experience)
iii. Legal and Regulatory Violations
- Impact on Business Processes
i. Cost
ii. Infrastructure Requirements
iii. Employee Impact
iv. Organizational Changes
- Data Issues
i. Data Security (breach and theft)
ii. Low-Quality Data
- AI-Related Risks
i. Data Privacy Violations
ii. Lack of Data Controls
iii. Lack of Formal Governance for AI
iv. Acquisition of Third-Party Data and AI Products
v. Lack of Domain Expertise in Pre-Production Reviews
vi. Use of Black-Box Models and Technologies
Module 10 - Identify Governance Measures
Ethical considerations
Privacy
Accountability
Transparency and explainability
Fairness and non-discrimination (bias)
Safety and security
Legal and regulatory considerations, frameworks, and guidelines
i. EU GDPR
ii. PCI DSS
iii. OECD Privacy Guidelines
iv. APEC Cross-Border Privacy Rules (CBPR)
v. Jurisdictional issues
vi. US federal and state-level privacy legislation
vii. EU legislation, frameworks, and regulations
viii. Data privacy legislation in other countries
ix. AI legislation
FAQs
General Questions:
Q: What is this course about?
A: This course provides business professionals with foundational knowledge of data science, enabling them to make informed decisions and drive organizational data strategies. It covers data fundamentals, implementation tactics, and the potential impact of data-driven solutions on business functions. Participants will also learn about the challenges and milestones of adopting data science within an organization.
Q: Who should attend this course?
A: This course is designed for managers, business leaders, and decision-makers interested in leveraging data science to improve business performance. It is also suitable for professionals who want to understand how data science can enhance customer experience, marketing, security, and operational efficiency.
Q: What are the prerequisites for this course?
A: There are no specific technical prerequisites. The course is designed for business professionals without a data science background but who want to understand how data science can drive business value.
Q: How is the course structured?
A: The course is divided into four key domains:
Understanding Data Science Fundamentals – Covers key data science concepts, data types, analytics, AI, and machine learning.
Identifying Business Uses for Data Science – Explores applications in customer experience, marketing, security, and operations.
Implementing Business Requirements for Data Science – Focuses on building a data-driven organization and developing an implementation strategy.
Identifying Data Science Risks – Addresses governance, ethical considerations, and regulatory compliance.
Q: How long is the course?
A: The course duration is 1 day.
Q: Will I receive a certificate upon completion?
A: This course provides valuable insights into data science for business professionals, but it does not mention a certification upon completion. Please check with the course provider for certification details.
Program Content & Skills:
Q: What specific topics are covered in the course?
A: The course covers fundamental data science concepts and their business applications, including:
Key data science terms and analytics types (descriptive, predictive, prescriptive, diagnostic).
The data science lifecycle: data acquisition, preparation, modeling, and implementation.
Business applications such as customer experience improvement, targeted marketing, fraud detection, and operational optimization.
Developing a data-driven organization and implementation strategies.
Identifying risks, governance measures, and ethical considerations in data science.
Q: Will I learn about advanced data science technologies?
A: The course provides an overview of essential data science concepts, including AI, machine learning, statistical analysis, and data visualization. However, it focuses on business applications and strategic decision-making rather than in-depth algorithm development or advanced machine learning techniques.
Q: Will I learn how to integrate data science into business operations?
A: Yes, the course explores how data science can be applied across various business functions, such as marketing, finance, security, and operations. It also covers best practices for aligning data-driven initiatives with corporate strategy, stakeholder needs, and regulatory compliance.
Q: Will I work on real-world examples and exercises?
A: While the course primarily focuses on theoretical concepts and business applications, it includes case studies and examples of how data science is used in different industries. Participants will gain insights into practical implementation strategies rather than hands-on technical exercises.
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