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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.



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.

Data Science Practitioner Jobs


Data science is consistently featured as a key field in LinkedIn’s Emerging Jobs reports, reflecting its significant growth. According to the U.S. Bureau of Labor Statistics, data science roles are expected to grow by 35% from 2022 to 2032. Harvard Business Review underscores data science’s evolving nature and critical importance, emphasizing the need for thorough discussions to validate data and its relevance to specific contexts. The article “Is Data Scientist Still the Sexiest Job?” notes that while the role remains highly popular and lucrative, it has also evolved. There is now a greater emphasis on building diverse data science teams and prioritizing non-technical skills such as ethics and change management.

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Data Architect
  • Database Administrator
  • Business Analyst
  • Data and Analytics Manager
  • Business Intelligence Analyst
  • Marketing Analyst
  • BI Developer
  • Product Owner
  • Machine Learning Engineer
  • Software Engineer
  • Analytics Consultant





CDSP Exam Details

The exam will certify that the successful candidate has the knowledge, skills, and abilities required to answer questions by collecting, wrangling, and exploring data sets, applying statistical models and artificial-intelligence algorithms, to extract and communicate knowledge and insights.



TARGET CANDIDATE

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.

EXAM CODES

DSP-210


LAUNCH DATE

Sept 2024


SUNSET DATE

DSP-110 sunset date is February 2025!
DSP-210 TBA


EXAM DURATION

120 minutes (including 5 minutes for Candidate Agreement and 5 minutes for Pearson VUE tutorial)

PASSING SCORE

72% or 69% depending on the exam form. Forms have been statistically equated.


NUMBER OF ITEMS

90 (of which 75 count towards final score)


ITEM FORMATS

Multiple Choice/Multiple Response


EXAM OPTIONS

In person at Pearson VUE test centers or online via Pearson OnVUE online proctoring







Why Get Certified as a Data Science Practitioner?

Organizations have access to large and ever-increasing quantities of data. A Certified Data Science Practitioner can make sense of this information, capturing and applying it to improve organizational practices and performance.

PROVE YOUR SKILLS

Earning your CDSP certification sets you apart from other candidates when you seek a new job or want to advance in your existing position. It validates your ability to extract key insights from large amounts of data and use this information to drive your organization forward.

LEAD KNOWLEDGEABLE DATA SCIENCE PROFESSIONALS

Data can be empowering, but only if properly collected, maintained, manipulated, analyzed, and translated into actionable information. CDSP confirms the capabilities and expertise of existing team members and those whom you are considering onboarding.






Data Science Training


Data can be very powerful, but only if handled and applied properly. CertNexus CDSP training empowers you to use this data to understand where your organization is and where it is going, helping you to meet and exceed critical goals with informed decisions.







Course Details

1. Addressed through the application of data science

  • 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)
  • Understand stakeholder challenges
    • Understand stakeholder terminology
    • Become aware of data privacy, security, and governance policies
    • Obtain permission/access to data
  • Classify a question into a known data science problem
    • Access references
    • Identify data sources and type
    • Select modeling type

  • Gather relevant datasets
    • Read data
    • Research third-party data availability
    • Collect open-source data
  • 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
  • Merge datasets
    • Join data from different sources
  • Apply problem-specific transformations to datasets
    • Apply word embeddings
    • Generate latent representations for image data
  • Load data
    • Load into DB
    • Load into DataFrame
    • Export to CSV files
    • Load into visualization tool
    • Make an endpoint

  • Examine data
    • Generate summary statistics
    • Examine feature types
    • Visualize distributions
    • Identify outliers
    • Find correlations
    • Identify target feature(s)
  • Preprocess data
    • Identify missing values
    • Make decisions about missing values (e.g., imputing method, record removal)
    • Normalize, standardize, or scale data
  • 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

  • Prepare datasets for modelling
    • Decide proportion of dataset to use for training, testing, and (if applicable) validation
    • Split data to train, test, and (if applicable) validation sets
  • Build training models
    • Define algorithms to try
    • Train model
    • Tune hyperparameters, if applicable
  • Evaluate models
    • Define evaluation metric
    • Compare model outputs
    • Select best performing model
    • Store model for operational use

  • Test hypotheses
    • Design A/B tests
    • Define success criteria for test
    • Evaluate test results
  • Test pipelines
    • Put model into production
    • Ensure model works operationally
    • Monitor pipeline for performance of model over time

  • 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


Fees Structure : 22500 INR / 270 USD
Total No of Class : 49 Video Class
Class Duration : 54 Working Hours
Download Feature : Download Avalable
Technical Support : Call / Whatsapp : +91 8680961847
Working Hours : Monday to Firday 9 AM to 6 PM
Payment Mode : Credit Card / Debit Card / NetBanking / Wallet (Gpay/Phonepay/Paytm/WhatsApp Pay)

Brochure       Buy Now       Sample Demo

Fees Structure : 30500 INR / 365 USD
Class Duration : 60 Days
Class Recording : Live Class Recording available
Class Time : Monday to Firday 1.5 hours per day / Weekend 3 Hours per day
Technical Support : Call / Whatsapp : +91 8680961847
Working Hours : Monday to Firday 9 AM to 6 PM
Payment Mode : Credit Card / Debit Card / NetBanking / Wallet (Gpay/Phonepay/Paytm/WhatsApp Pay)

Download Brochure       Pay Online