Certified Data Science Practitioner (CDSP)
Data Science Practitioner
Certified Data Science Practitioner (CDSP)


- 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.

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Fundamental Learnings
Training by Top Microsoft-Certified Trainers
1 Day of Live, Instructor-Led Sessions
Latest, Up-to-date Curriculum, Approved by Industry Experts
Access to a Digital Library of Learning Resources
Comprehensive Knowledge of Core Learnings
Blend of classroom sessions and hands-on training
Prerequisites for this Courese
Prerequisites and Eligibility
- 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.

Things Included in the course learning
Course Curriculum
Lesson 1: Addressing Business Issues with Data Science
Lesson 2: Extracting, Transforming, and Loading Data
Lesson 3: Analyzing Data
Lesson 4: Designing a Machine Learning Approach
Lesson 5: Developing Classification Models
Lesson 6: Developing Regression Models
Lesson 7: Developing Clustering Models
Lesson 8: Finalizing a Data Science Project
Things Included in the course learning
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What will I learn in this course
Material Includes
- 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.
Who is this course for
- 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

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