Certified Data Science
Practitioner
Overview
Course Objective
By the end of this course, participants will be able to:
- Identify project scope, objectives, and stakeholder requirements for a data science initiative.
- Understand stakeholder challenges, including data privacy, security, and governance policies.
- Classify business problems into data science problems and determine suitable data modeling techniques.
- Gather, clean, and preprocess datasets to ensure data integrity and usability.
- Apply feature engineering and problem-specific transformations to datasets for improved model performance.
- Develop and evaluate machine learning models using appropriate metrics and techniques.
- Test hypotheses, implement A/B testing, and validate model outcomes.
- Deploy models in production environments and monitor their performance over time.
- Communicate findings through reports, visualizations, and proof-of-concept (POC) implementations.
Who Should Attend
Prerequisites
Training Calendar
Intake
Duration
Program Fees
Module
Module 1 - Identify The Project Scope
• Identify project specifications, including objectives (metrics/KPIs) and
stakeholder requirements
• Identify mandatory deliverables, optional deliverables
• Identify project limitations (time, technical, resource, data, risks)
Module 2 - Understand stakeholder challenges
• Understand stakeholder terminology
• Become aware of data privacy, security, and governance policies
• Obtain permission/access to data
Module 3 - Classify a question into a known data science problem
• Access references
• Identify data sources and type
• Select modeling type
Module 4 - Gather relevant datasets
• Read data
• Research third-party data availability
• Collect open-source data
Module 5 - Clean datasets
• Identify and eliminate irregularities in data
• Parse the data
• Check for corrupted data
• Correct the data format for storing/querying purposes
• Deduplicate data
Module 6 - Merge datasets
• Join data from different sources
Module 7 - Apply problem-specific transformations to datasets
• Apply word embeddings
• Generate latent representations for image data
Module 8 - Load Data
• Load into DB
• Load into DataFrame
• Export to CSV files
• Load into visualization tool
• Make an endpoint
Module 9 - Examine Data
• Generate summary statistics
• Examine feature types
• Visualize distributions
• Identify outliers
• Find correlations
• Identify target feature(s)
Module 10 - Preprocess Data
• Identify missing values
• Make decisions about missing values (e.g., imputing method, record
removal)
• Normalize, standardize, or scale data
Module 11 - Carry Out Feature Engineering
• Apply encoding to categorical data
• Assign feature values to bins or groups
• Split features
• Convert dates to useful features
• Apply feature reduction methods
Module 12 - Prepare Datasets for Modeling
• Decide proportion of dataset to use for training, testing, and (if
applicable) validation
• Split data to train, test, and (if applicable) validation sets
Module 13 -Build Training Models
• Define algorithms to try
• Train model
• Tune hyperparameters, if applicable
Module 14 - Evaluate Models
• Define evaluation metric
• Compare model outputs
• Select best performing model
• Store model for operational use
Module 15 - Test Hypotheses
• Design A/B tests
• Define success criteria for test
• Evaluate test results
Module 16 - Test Pipelines
• Put model into production
• Ensure model works operationally
• Monitor pipeline for performance of model over time
Module 17 - Report Findings
• Implement model in a basic web application for demonstration (POC
implementation)
• Derive insights from findings
• Show model results
• Identify features that drive outcomes (e.g., explainability, variable
importance plot)
• Generate lift or gain chart
FAQs
General Questions:
Q: What is this course about?
A: This course provides hands-on training in Data Science, covering data collection, preprocessing, machine learning model development, and deployment. Participants will learn how to analyze datasets, build predictive models, and implement data-driven solutions while ensuring data privacy, security, and governance.
Q: Who should attend this course?
A: This course is ideal for professionals across industries who want to enhance their data science skills. It is suitable for:
Programmers looking to apply machine learning techniques.
Data analysts aiming to develop predictive modeling and AI skills.
Business professionals interested in data-driven decision-making.
Individuals preparing for the Certified Data Science Practitioner™ (CDSP) certification.
Q: What are the prerequisites for this course?
A: Participants should have basic knowledge of statistics, probability, linear algebra, and programming (preferably Python), along with familiarity with data handling, databases, and analytical problem-solving.
Q: How is the course structured?
A: The course is divided into modules covering:
Identifying project scope and business objectives.
Understanding stakeholder challenges and data privacy requirements.
Gathering, cleaning, and preprocessing datasets.
Applying feature engineering and transformations.
Training, evaluating, and optimizing machine learning models.
Testing hypotheses and implementing A/B testing.
Deploying models and monitoring performance.
Communicating insights through reports and visualizations.
Q: How long is the course?
A: The course duration is 5 days.
Q: Will I receive a certificate upon completion?
A: Yes, participants will receive the Certified Data Science Practitioner™ (CDSP) certification upon successfully completing the course.
Program Content & Skills:
Q: What specific topics are covered in the course?
A: The course covers core data science principles, including project scoping, data collection, data cleaning, feature engineering, and machine learning model development. It also explores business applications of data science, such as predictive analytics, data visualization, and AI-driven decision-making. Additionally, it addresses model deployment, performance monitoring, ethical considerations, and data governance.
Q: Will I learn about advanced data science technologies?
A: The course provides a practical and structured approach to data science, covering machine learning, model evaluation, and feature engineering. While it introduces concepts like deep learning and AI, it is focused on hands-on data analysis and business applications rather than advanced algorithm development.
Q: Will I learn how to integrate data science into business operations?
A: Yes, the course covers how data science can be applied across various business functions, including marketing, finance, operations, and customer insights. It also provides guidance on aligning data-driven initiatives with business strategy, stakeholder needs, and regulatory compliance.
Q: Will I work on real-world examples and exercises?
A: Yes, the course includes case studies, hands-on exercises, and real-world applications of data science across different industries. Participants will work with real datasets, build models, analyze insights, and explore best practices for implementing data science solutions in business contexts.
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