Data Science for
Executives
Overview
Course Objective
• Gain confidence in the management of data-analytic projects
• Learn the skills necessary to allow their organizations a pain-free migration into the
“datadriven enterprise” world and to increase their organization’s foothold in data
analysis
• Acquire an understanding of the key trends in Data Science and how these are influencing
the future of business
Who Should Attend
C-level executives.
Prerequisites
• Exposure to Business Intelligence
• Exposure to Data Storage Solutions/Databases
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Introduction to Data Science
• Origins of Data Science and a brief history of the Big Data
revolution
• The Big Data landscape
• How much data is there really, and does it matter?
• Un-siloing data: use paradigms for organizational data and public
data
• Descriptive, predictive and prescriptive analysis
• From recommendations to insights: black-box and white-box
analytics
Module 2 - Data as an Asset (30 min)
• The V’s of Big Data: Volume, Velocity, Variability, Veracity.
• Data business strategies
• Data sources, synergies and differentiators
Module 3 - Data Lifecycle (60 min)
• The analytics value chain
• Overview of the data analysis cycle, connecting Data Science to
the business problems
• Work cycle of a data scientist: wrangling, modelling, validation,
and optimization
• Managing research
Module 4 - Data Wrangling and EDA (60 min)
• Determining data quality and data cleansing
• Entity/semantic matching
• Missing data and Imputation
• Exploratory analysis
Module 5 - Fundamentals of Statistics (90 min)
• Types of data: numerical, categorical, ordinal.
• Statistical summaries: mean, standard deviation, quantiles,
correlation
• Simple data visualization: histograms, boxplots, time series and
scatterplots
• Causality vs. correlation and independence vs. dependence
• Randomization and random sampling
• Statistical inference
Module 6 - Model Creation and Validation (90 min)
• Prediction: linear regression, nonparametric regression
• Forecasting: auto.arima and Error-Trend-Seasonal exponential
smoothing algorithms.
• Classification: logistic regression, decision trees, SVM’s.
• Clustering: k-means, hierarchical clustering.
• Supervised vs. unsupervised vs. semi-supervised learning.
• Dimension reduction: principal components
• Languages and environments (e.g. R, Python) and standards
(PMML)
Module 7 - Data Visualization (30 min)
• Practical and effective visualization: beyond bar charts.
• Finding the unexpected: the role of visualization in exploratory
analysis
• Communicating findings: the role of visualization in
communicating Data Science outputs.
• Standard tools: R, Tableau, D3
Module 8 - Data Engineering for Analysis (60 min)
• Data Science engineering and its drivers for change
• Data volumes, data structures, and how they vary
• Data Science architectures: Data Lakes and Big Data
• Batch and Real-time processing: Distributed File Systems, Map
Reduce, Spark
Module 9 - Operationalization and Data Model Lifecycle (90 min)
• Determining the needs: on how much data must decisions be
taken, how often and how quickly must they be made, how often
must models be refreshed?
• Plugging into existing data paths and choosing appropriate
technologies
• Stale models and model refreshing.
• Operationalization from a business perspective: determining
value and making Data Science outputs part of standard business
and decision-making processes
Module 10 - Deep Learning
• Artificial neural networks (ANN’s)
• Applications and implications of ANN’s
• Prescriptive analytics and future business automation
Module 11 - Hands-on Exercise – Panel Discussion on Building a Data-Driven Enterprise
• Data Science as a process, rather than as a point event.
• The role of high-level management in enabling data-driven
decisions.
• The role of direct management: on the un-Gantt-ability of
Research.
Module 12 - Case Studies
• Operational efficiency by predictive analytics
• Architectural choices for integration and efficacy
FAQs
General Questions:
Q: What is the Data Science for Executives course about?
This course helps executives understand how to leverage data science for strategic decision-making. It covers practical concepts like data wrangling, analytics, modeling, and operationalization, empowering leaders to build a data-driven culture in their organizations.
Q: Who should attend this course?
C-level executives and senior decision-makers looking to integrate data-driven practices and lead data science initiatives within their organizations.
Q: What are the prerequisites for this course?
Participants should have exposure to business intelligence and data storage solutions or databases. No prior experience in data science is required.
Q: How long is the course?
The course runs for 2 days.
Q: What key topics are covered in this course?
Introduction to data science and analytics
The data lifecycle and analytics value chain
Data wrangling and exploratory data analysis
Basic statistics and predictive modeling
Classification, clustering, and forecasting techniques
Data visualization and communication
Big data engineering and architecture
Operationalizing data models in business
Deep learning concepts and business implications
Panel discussions and case studies on building a data-driven enterprise
Q: Will I receive a certification after completing the course?
While the course does not offer a formal certification, participants will gain essential knowledge and strategic insights to drive data initiatives in their organizations.
Program Content & Skills:
Q: What foundational data concepts will I learn in this course?
You’ll explore core topics such as the data lifecycle, data wrangling, modeling, and analysis techniques. The course also covers descriptive, predictive, and prescriptive analytics to support strategic business decisions.
Q: How does the course prepare me to align data with business goals?
You’ll learn how to connect analytics to business problems, manage data science projects effectively, and embed data-driven thinking into organizational processes for improved outcomes and innovation.
Q: What skills will I develop in analyzing and transforming data?
You’ll gain skills in cleansing and exploring data, identifying trends and patterns, performing statistical analysis, and applying machine learning models for forecasting and classification.
Q: Will I learn how to work with data across different systems and sources?
Yes. The course covers structured, semi-structured, and unstructured data, as well as integrating public data and using big data tools like Spark for real-time and batch processing.
Q: How does the course address real-world decision-making needs?
You’ll learn how to operationalize data science outputs, refresh and maintain models, visualize data effectively, and use insights to guide decisions in areas such as customer behavior, risk management, and operational efficiency.
Submit your interest today !
