Certified Data Science Practitioner (CDSP) Course Overview

Certified Data Science Practitioner (CDSP) Course Overview

The Certified Data Science Practitioner (CDSP) course is a comprehensive training program designed to equip learners with the essential skills and knowledge required to tackle real-world business issues using data science. From initiating and formulating data science projects in Module 1 to effectively communicating results to stakeholders in Module 8, the course covers the full spectrum of data science workflow.

Learners will master the art of Extracting, Transforming, and Loading Data in Module 2, followed by Analyzing Data in Module 3 using various statistical techniques and visualizations. They will then dive into Designing a Machine Learning Approach in Module 4 and further develop specialized skills in Developing Classification Models, Regression Models, and Clustering Models in Modules 5, 6, and 7 respectively.

By the end of the course, participants will be able to deliver end-to-end data science solutions, from Finalizing a Data Science Project to implementing these solutions in a production environment, ensuring they are well-prepared to become proficient data science professionals.

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  • Live Online Training (Duration : 40 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

To ensure a successful learning experience in the Certified Data Science Practitioner (CDSP) course, students should meet the following minimum prerequisites:

  • Basic understanding of statistics and probability concepts.
  • Familiarity with any programming language, preferably Python, as it is commonly used in data science tasks.
  • Knowledge of core data handling operations, such as sorting, filtering, and aggregating data.
  • An ability to comprehend and articulate business problems and objectives.

Please note, while prior experience in data science is not strictly required, a general understanding of the data analytics process will be beneficial. This course is designed to be accessible to those with a foundational skill set and a willingness to learn and engage with the material presented.

Target Audience for Certified Data Science Practitioner (CDSP)

The Certified Data Science Practitioner (CDSP) course equips learners with practical skills to solve business problems using data science methodologies.

Target audience for the CDSP course includes:

  • Aspiring Data Scientists
  • Data Analysts seeking to upgrade their skills
  • Business Analysts interested in data science techniques
  • IT Professionals looking to transition into data-centric roles
  • Software Developers wanting to specialize in machine learning
  • Statisticians aiming to apply their knowledge in the tech industry
  • Professionals in data-driven fields (e.g., Finance, Healthcare, Marketing)
  • Graduates with a background in STEM fields exploring data science careers
  • Managers overseeing data-driven projects who require a deeper understanding
  • Entrepreneurs looking to leverage data science in their ventures

Learning Objectives - What you will Learn in this Certified Data Science Practitioner (CDSP)?

Introduction to Learning Outcomes:

The Certified Data Science Practitioner (CDSP) course is designed to equip learners with the skills to address business problems through data analytics, machine learning model development, and effective communication of results.

Learning Objectives and Outcomes:

  • Understand and initiate the data science process to address specific business issues.
  • Formulate data science problems and hypotheses to guide analysis.
  • Extract, transform, and load data from various sources for analytical purposes.
  • Examine and explore data distributions using statistical methods and visualizations.
  • Preprocess and clean data to prepare for machine learning algorithms.
  • Identify and apply appropriate machine learning concepts to design solutions.
  • Train, tune, and evaluate the performance of classification models.
  • Develop and assess the accuracy of regression models in predicting outcomes.
  • Implement clustering algorithms and evaluate their effectiveness in data segmentation.
  • Communicate analytical results effectively to stakeholders and implement models into production environments.