Course Outline

Week 1: Big Data Concepts for Government

  • Understanding the Four V's (Velocity, Volume, Variety, Veracity)
  • Limits of Traditional Data Processing Capacity
  • Distributed Processing Techniques
  • Statistical Analysis Methods
  • Types of Machine Learning Analysis
  • Data Visualization Best Practices
  • Distributed Processing (e.g., MapReduce)
  • Introduction to Programming Languages for Data Analysis
  • R Language Crash Course
  • Python Crash Course

Weeks 2 & 3: Performing Data Analysis for Government

  • Statistical Analysis Techniques
  • Descriptive Statistics in Big Data Sets (e.g., Calculating Mean)
  • Inferential Statistics (Estimation Methods)
  • Forecasting Using Correlation and Regression Models
  • Time Series Analysis for Government Applications
  • Fundamentals of Machine Learning
  • Supervised vs. Unsupervised Learning
  • Classification and Clustering Techniques
  • Evaluating the Cost of Specific Methods
  • Data Filtering Techniques

Week 4: Natural Language Processing for Government

  • Text Processing Techniques
  • Understanding Text Meaning and Context
  • Automatic Text Generation Methods
  • Sentiment and Topic Analysis for Policy Evaluation
  • Computer Vision Applications in Government

Weeks 5 & 6: Tooling Concepts for Government

  • Data Storage Solutions (SQL, NoSQL, Hierarchical, Object-Oriented, Document-Oriented)
  • Examples of Data Storage Systems (MySQL, Cassandra, MongoDB, Elasticsearch, HDFS, etc.)
  • Selecting the Appropriate Solution for Specific Problems
  • Distributed Processing Techniques for Government
  • Introduction to Apache Spark
  • Machine Learning with Spark (MLLib)
  • Spark SQL for Data Manipulation
  • Scalability Considerations for Government Systems
  • Public Cloud Providers (AWS, Google, etc.)
  • Private Cloud Solutions (OpenStack, Cloud Foundry)
  • Autoscalability in Government IT Environments

Week 7: Soft Skills for Government

  • Advisory and Leadership Skills for Data Professionals
  • Data-Driven Storytelling to Influence Policy
  • Understanding and Engaging Your Audience
  • Effective Data Presentation Techniques
  • Influence Effectiveness and Change Leadership in Government
  • Handling Difficult Situations and Stakeholder Engagement

Exam for Government

  • End of Program Graduation Exam

Requirements

Participants should have a solid foundation in mathematics, equivalent to at least a high school level. While programming skills are not mandatory, they can be beneficial. Prior to participating in this training program for government, candidates will undergo an assessment and interview process.
 245 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Testimonials (3)

Helpful and good listener .. interactive

Ahmed El Kholy - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)

It is great to have the course custom made to the key areas that I have highlighted in the pre-course questionnaire. This really helps to address the questions that I have with the subject matter and to align with my learning goals.

Winnie Chan - Statistics Canada
Course - Jupyter for Data Science Teams

The example and training material were sufficient and made it easy to understand what you are doing.

Teboho Makenete
Course - Data Science for Big Data Analytics

Upcoming Courses

Data Science Programme

2026-04-21 09:30
245 hours
Jefferson City, MO - Jefferson and High
$ 47297 (Online)
$ 68297 (Classroom)

Data Science Programme

2026-05-05 09:30
245 hours
Knoxville, TN – Regus First Horizon Plaza
$ 47297 (Online)
$ 68297 (Classroom)

Data Science Programme

2026-05-19 09:30
245 hours
Lansing, MI - Regus - One Michigan Avenue
$ 47297 (Online)
$ 68297 (Classroom)

Data Science Programme

2026-06-02 09:30
245 hours
Reno, NV - Regus - Mountain View Corporate
$ 47297 (Online)
$ 68297 (Classroom)

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