Cloudera Data Scientist Course
- Duration: 32 hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The Cloudera Data Scientist Course is designed to empower data professionals with the skills required to extract insights, build predictive models, and solve complex business problems using advanced data science techniques on the Cloudera Data Platform (CDP). This course covers data exploration, statistical analysis, machine learning, model evaluation, and deployment practices using industry-standard tools such as Python, Apache Spark, and Cloudera Machine Learning (CML). Through practical exercises and real-world case studies, participants will gain hands-on experience in designing and operationalizing data science workflows at scale.
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Course Details
- Deep understanding of data science fundamentals and best practices
- Hands-on experience with Python for data analysis and modeling
- Ability to leverage Apache Spark for scalable data processing
- Expertise in building, evaluating, and deploying machine learning models
- Knowledge of end-to-end data science workflows in enterprise environments
- Preparation for advanced analytics and data scientist roles
- Open to data analysts, data scientists, ML engineers, and analytics professionals
- Ideal for those responsible for deriving insights and predictive intelligence from large datasets
- Basic knowledge of Python, SQL, and statistics is recommended
- Familiarity with distributed data processing concepts is beneficial
- Corporate sponsorship or group participation is encouraged
- Master data exploration, processing, and visualization techniques
- Learn how to build and optimize machine learning models
- Gain expertise in using Spark and Python for scalable analysis
- Understand model evaluation, feature engineering, and deployment strategies
- Develop data science workflows that integrate with CDP and CML
- Achieve readiness for real-world data science responsibilities
- Introduction to Data Science & Analytics
- Data Exploration and Preprocessing Techniques
- Statistical Analysis for Data Science
- Machine Learning Fundamentals
- Apache Spark for Scalable Data Processing
- Feature Engineering and Model Training
- Model Evaluation and Tuning
- Deployment and Operationalization with CML
- Real-World Data Science Use Cases
- Hands-On Labs and Practical Projects
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