Most popular
-52%
Data Science and Analytics
What you'll learn
Week 1-2: Introduction to Data Science and Business Analytics
The role of data science in business
Overview of the data science process
Key concepts in data analytics
Week 3-4: Data Management and Preprocessing
Data collection and integration techniques
Data cleaning and preprocessing
Introduction to database management and SQL
Week 5-6: Descriptive and Inferential Statistics
Basics of descriptive statistics
Inferential statistics and hypothesis testing
Using statistical software for data analysis (e.g., R, Python)
Week 7-8: Predictive Analytics and Machine Learning
Introduction to predictive modeling
Supervised and unsupervised learning techniques
Implementing machine learning algorithms
Week 9-10: Data Visualization and Reporting
Principles of effective data visualization
Tools for data visualization (e.g., Tableau, Power BI)
Communicating data insights to stakeholders
Week 11-12: Big Data and Advanced Analytics Techniques
Understanding big data technologies and frameworks
Advanced analytics techniques (e.g., text analytics, neural networks)
Case studies in big data analytics
Week 13-14: Ethical and Legal Considerations in Data Science
Data privacy and security issues
Ethical considerations in data analytics
Regulatory compliance in data science
Week 15: Course Review and Final Assessment
Course Materials and Assessment:
Study Materials: Detailed lecture notes, case studies, video tutorials, and recommended readings.
Quizzes/Case Studies: Weekly quizzes and case studies to apply data science concepts in business contexts.
MCQs: Multiple choice questions to evaluate understanding of data science principles.
Simulations: Interactive data science simulations for practical, hands-on experience.
Final Project: Conducting a comprehensive data analytics project, from data collection to interpretation and presentation of findings.
Trending
-52%
Data Science and Analytics
What you'll learn
Week 1-2: Introduction to Data Science and Business Analytics
The role of data science in business
Overview of the data science process
Key concepts in data analytics
Week 3-4: Data Management and Preprocessing
Data collection and integration techniques
Data cleaning and preprocessing
Introduction to database management and SQL
Week 5-6: Descriptive and Inferential Statistics
Basics of descriptive statistics
Inferential statistics and hypothesis testing
Using statistical software for data analysis (e.g., R, Python)
Week 7-8: Predictive Analytics and Machine Learning
Introduction to predictive modeling
Supervised and unsupervised learning techniques
Implementing machine learning algorithms
Week 9-10: Data Visualization and Reporting
Principles of effective data visualization
Tools for data visualization (e.g., Tableau, Power BI)
Communicating data insights to stakeholders
Week 11-12: Big Data and Advanced Analytics Techniques
Understanding big data technologies and frameworks
Advanced analytics techniques (e.g., text analytics, neural networks)
Case studies in big data analytics
Week 13-14: Ethical and Legal Considerations in Data Science
Data privacy and security issues
Ethical considerations in data analytics
Regulatory compliance in data science
Week 15: Course Review and Final Assessment
Course Materials and Assessment:
Study Materials: Detailed lecture notes, case studies, video tutorials, and recommended readings.
Quizzes/Case Studies: Weekly quizzes and case studies to apply data science concepts in business contexts.
MCQs: Multiple choice questions to evaluate understanding of data science principles.
Simulations: Interactive data science simulations for practical, hands-on experience.
Final Project: Conducting a comprehensive data analytics project, from data collection to interpretation and presentation of findings.
Popular Instructors
All Data Science and Analytics Courses
-52%
Data Science and Analytics
Data Science and Analytics is a crucial course in the …
What you'll learn
Week 1-2: Introduction to Data Science and Business Analytics
The role of data science in business
Overview of the data science process
Key concepts in data analytics
Week 3-4: Data Management and Preprocessing
Data collection and integration techniques
Data cleaning and preprocessing
Introduction to database management and SQL
Week 5-6: Descriptive and Inferential Statistics
Basics of descriptive statistics
Inferential statistics and hypothesis testing
Using statistical software for data analysis (e.g., R, Python)
Week 7-8: Predictive Analytics and Machine Learning
Introduction to predictive modeling
Supervised and unsupervised learning techniques
Implementing machine learning algorithms
Week 9-10: Data Visualization and Reporting
Principles of effective data visualization
Tools for data visualization (e.g., Tableau, Power BI)
Communicating data insights to stakeholders
Week 11-12: Big Data and Advanced Analytics Techniques
Understanding big data technologies and frameworks
Advanced analytics techniques (e.g., text analytics, neural networks)
Case studies in big data analytics
Week 13-14: Ethical and Legal Considerations in Data Science
Data privacy and security issues
Ethical considerations in data analytics
Regulatory compliance in data science
Week 15: Course Review and Final Assessment
Course Materials and Assessment:
Study Materials: Detailed lecture notes, case studies, video tutorials, and recommended readings.
Quizzes/Case Studies: Weekly quizzes and case studies to apply data science concepts in business contexts.
MCQs: Multiple choice questions to evaluate understanding of data science principles.
Simulations: Interactive data science simulations for practical, hands-on experience.
Final Project: Conducting a comprehensive data analytics project, from data collection to interpretation and presentation of findings.
