Emerging Technologies Certifications
Certified Data Science Practitioner
Certified Data Science Practitioner (CDSP) is a vendor-neutral, high-stakes certification designed for programmers, data professionals and analysts seeking to validate and showcase their knowledge and skills in the area of Data Science.
The Certified Data Science Practitioner exam is designed for professionals across different industries seeking to demonstrate the ability to gain insights and build predictive models from data
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Data Science Practitioner Certification
The exam will certify that the successful candidate has the knowledge, skills, and abilities required to answer questions by collecting, wrangling, and exploring datasets, applying statistical models and artificial-intelligence algorithms, to extract and communicate knowledge and insights.
Data Science Practitioner Course Overview
The Certified Data Science Practitioner™ (CDSP) is an industry-validated certification which helps professionals differentiate themselves from other job candidates by demonstrating their ability to put data science concepts into practice. Data can reveal insights and inform—by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework.
This certification validates candidates’ ability to use data science principles to address business issues, use multiple techniques to prepare and analyze data, evaluate datasets to extract valuable insights, and design a machine learning approach.
In addition, it will validate skills to design, finalize, present, implement and monitor a model to address issues regardless of business sector.
Course Overview
- Course accredited by CertNexus
- 4 days live virtual sessions with accredited trainers for online live learning + Lifetime access to E-learning
- Lab access included
- Certification exam included
- High-quality E-learning material for self-paced learning
- E-learning access includes quizzes and practice exams
- Doubt clearance sessions included
- Defining the question to be addressed through the application of data science
- Extracting, Transforming, and Loading Data
- Performing exploratory data analysis
- Building models
- Testing Models
- Communicating Findings
Course Curriculum
Target Audience
The Certified Data Science PractitionerTM (CDSP) exam is designed for professionals across different industries seeking to demonstrate the ability to gain insights and build predictive models from data.
Pre-requisites
For attending the course, no pre-requisites are required. There are no formal prerequisites to register for and schedule an exam. Successful candidates will possess the knowledge, skills, and abilities as identified in the domain objectives in this blueprint. It is also strongly recommended that candidates possess the following knowledge, skills, and abilities:
- A working level knowledge of programming languages such as Python® and R
- Proficiency with a querying language
- Strong communication skills
- Proficiency with statistics and linear algebra
- Demonstrate responsibility based upon ethical implications when sharing data sources
- Familiarity with data visualization
Certification Examination Details
- No. of items: 100, of which 75 counts toward your score
- Pass mark: 70% or 73% depending on exam form. Forms have been statistically equated
- Exam duration: 120 minutes (Note: exam time includes 5 minutes for reading and signing the Candidate Agreement and 5 minutes for the Pearson VUE testing system tutorial.)
- Exam Options: In person at Pearson VUE test centers or online via Pearson OnVUE
- Item Formats: Multiple Choice/Multiple Response
Course Content
Objective 1.1 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)
Objective 1.2 Understand stakeholder challenges
- Understand stakeholder terminology
- Become aware of data privacy, security, and governance policies
- Obtain permission/access to data
Objective 1.3 Classify a question into a known data science problem
- Access references
- Identify data sources and type
- Select modeling type
Objective 2.1 Gather relevant datasets
- Read data
- Research third-party data availability
- Collect open-source data
Objective 2.2 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
Objective 2.3 Merge datasets
- Join data from different sources
Objective 2.4 Apply problem-specific transformations to datasets
- Apply word embeddings
- Generate latent representations for image data
Objective 2.5 Load data
- Load into DB
- Load into DataFrame
- Export to CSV files
- Load into visualization tool
- Make an endpoint
Objective 3.1 Examine data
- Generate summary statistics
- Examine feature types
- Visualize distributions
- Identify outliers
- Find correlations
- Identify target feature(s)
Objective 3.2 Preprocess data
- Identify missing values
- Make decisions about missing values (e.g., imputing method, record removal)
- Normalize, standardize, or scale data
Objective 3.3 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
Objective 4.1 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
Objective 4.2 Build training models
- Define algorithms to try
- Train model
- Tune hyperparameters, if applicable
Objective 4.3 Evaluate models
- Define evaluation metric
- Compare model outputs
- Select best performing model
- Store model for operational use
Objective 5.1 Test hypotheses
- Design A/B tests
- Define success criteria for test
- Evaluate test results
Objective 5.2 Test pipelines
- Put model into production
- Ensure model works operationally
- Monitor pipeline for performance of model over time
Objective 6.1 Report findings
- Implement model in a basic web application for demonstration (POC implementation)
- Derive insights from findings
- Identify features that drive outcomes (e.g., explainability, variable importance plot)
- Show model results
- Generate lift or gain chart
Learning Options
- Lifetime access to high-quality self-paced eLearning content curated by industry experts
- 40 Hours of Self-Paced Videos, Quizzes and Practice Exams
- Certification exam voucher included
- 24x7 learner assistance and support
- Lifetime access to high-quality self-paced eLearning content curated by industry experts
- Four Days of Online Live Public Training Sessions
- Certification exam voucher included
- 24x7 learner assistance and support
- Lifetime access to high-quality self-paced eLearning content curated by industry experts
- Four Days of Online Live OR Classroom Private Training Sessions
- Certification exam voucher included
- 24x7 learner assistance and support
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