Data Science
Curriculum
- 9 Sections
- 50 Lessons
- 10 Weeks
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- Introduction to Data ScienceThis section introduces students to the field of data science, explaining its role in extracting insights from data and solving real-world problems. Students will learn about data science workflows, tools, and career opportunities.6
- Data Collection and CleaningHigh-quality analysis depends on clean, accurate data. Students will learn how to collect, clean, and preprocess data from multiple sources to prepare it for analysis.6
- Exploratory Data Analysis (EDA)Students learn how to explore datasets, identify patterns, and uncover insights. This section emphasizes visual and statistical methods to summarize data effectively.6
- Programming for Data ScienceStudents will gain proficiency in programming languages essential for data science, such as Python or R, to manipulate, analyze, and visualize data efficiently.6
- Statistical Analysis and ProbabilityThis section teaches students statistical methods and probability concepts used to analyze data and draw meaningful conclusions.6
- Machine Learning FundamentalsStudents learn how to build predictive models and apply machine learning techniques to solve data problems.6
- Advanced Data Science TechniquesThis section introduces advanced topics such as natural language processing, clustering, and time series analysis to handle complex data scenarios.6
- Data Ethics and Best PracticesStudents learn ethical standards, data governance, and best practices to ensure responsible handling of data.5
- Capstone Project – Real-World Data Science ApplicationIn the final section, students integrate all learned skills to complete a real-world data science project. They will process data, apply machine learning, visualize results, and present actionable insights.6
Visualizing and Presenting Insights
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