Academy | Syllabus

Certified Data Science Practitioner® (CDSP)

Offered by CertNexus®, Certified Data Science Practitioner® (CDSP) certificate 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. This certification is accredited by the ANSI National Accreditation Board (ANAB) under the ISO/IEC 17024 standard. The Certified Data Science Practitioner® (CDSP) training program offered by Multimatics is designed to help participants gain the 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. The training material is prepared based on the latest edition of CDSP, accompanied by discussions and exercises to work on the questions.

Multimatics is an Authorized Training Partner for the Certified Data Science Practitioner® (CDSP) training and certification program accredited by the CertNexus®.

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+21

Years of Experiences

+6.000

Delivered Training Programs

+50.000

Professionals

90%

Exam Pass Rate

Program Details

Durations

The program is a 5-day intensive training class.

Method of Delivery

The program provided by Multimatics will be delivered through interactive presentation by professional instructor(s), group debriefs, individual and team exercises, behavior modelling and roleplays, one-to-one and group discussion, case studies, and projects.

Who Should Attend?

This program is designed for professionals across different industries seeking to demonstrate the ability to gain insights and build predictive models from data.

Program Objectives

By the end of the program, participants will be able to:

Use data science principles to address business issues
Apply the extract, transform, and load (ETL) process to prepare datasets
Use multiple techniques to analyze data and extract valuable insights
Design a machine learning approach to address business issues
Train, tune, and evaluate classification models
Train, tune, and evaluate regression and forecasting models
Train, tune, and evaluate clustering models
Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance

Examination Details

Duration

120 Minutes

Format

Open Book

Close Book

Level

Passing Grade

65%

Program Modules

Gather data sets
Read Data
Write a query for a SQL database
Write a query for a NoSQL database
Read data from/write data to cloud storage solutions
AWS S3
Google Storage Buckets
Azure Data Lake
Become aware of first-, second-, and third-party data sources
Understand data collection methods
Understand data sharing agreements, where applicable
Explore third-party data availability
Demographic data
Bloomberg
Collect open-source data
Use APIs to collect data
Scrape the web
Generate data assets
Dummy or test data
Randomized data
Anonymized data
AI-generated synthetic data
Clean data sets
Identify and eliminate irregularities in data (e.g., edge cases, outliers)
Nulls
Duplicates
Corrupt values
Parse the data
Check for corrupted data
Correct the data format
Deduplicate data
Apply risk and bias mitigation techniques
Understand common forms of ML bias
Sampling bias
Measurement bias
Exclusion bias
Observer bias
Prejudicial bias
Confirmation bias
Bandwagoning
Identify the sources of bias
Sources of bias include data collection, data labeling, data transformation, data imputation, data selection, and data training methods
Use exploratory data analysis to visualize and summarize the data, and detect outliers and anomalies
Assess data quality by measuring and evaluating the completeness, correctness, consistency, and currency of data
Use data auditing techniques to track and document the provenance, ownership, and usage of data, and applied data cleaning steps
Mitigate the impact of bias
Apply mitigation strategies such as data augmentation, sampling, normalization, encoding, validation
Evaluate the outcomes of bias
Use methods such as confusion matrix, ROC curve, AUC score, and fairness metrics
Monitor and improve the data cleaning process
Establish or adhere to data governance rules, standards, and policies for data and the data cleaning process
Merge and load data sets
Join data from different sources
Make sure a common key exists in all datasets
Unique identifiers
Load data
Load into DB
Load into dataframe
Export the cleaned dataset
Load into visualization tool
Make an endpoint or API
Apply problem-specific transformations to data sets
Apply word vectorization or word tokenization
Word2vec
TF-IDF
Glove
Generate latent representations for image data

Defining the Question to be Addressed through the Application of Data Science

Topic Covered

Identify the project scope
Identify project specifications, including objectives (metrics/KPIs) and stakeholder requirements
Identify mandatory deliverables, optional deliverables
Determine project timeline
Identify project limitations (time, technical, resource, data, risks)
Understand challenges
Understand terminology
Milestone
POC (Proof of concept)
MVP (Minimal Viable Product)
Become aware of data privacy, security, and governance policies
GDPR
HIPPA
California Privacy Act
Obtain permission/access to stakeholder data
Ensure appropriate voluntary disclosure and informed consent controls in place
Classify a question into a known data science problem
Identify references relevant to the data science problem
Optimization problem
Forecasting problem
Regression problem
Classification problem
Segmentation/Clustering problem
Identify data sources and type
Structured/unstructured
Image
Text
Numerical
Categorical
Select modeling type
Regression
Classification
Forecasting
Clustering
Optimization
Recommender systems

Certified Data Science Practitioner® (CDSP)

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Program Details

Who Should Attend?

Program Objectives

Examination Details

Program Modules

02Extracting, Transforming, and Loading Data

03Performing Exploratory Data Analysis

04Building Models

05Testing Models

06Operationalizing the Pipeline

07Communication Findings

Defining the Question to be Addressed through the Application of Data Science

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