Most popular
-52%
Data Science and Analytics
20 hours
Beginner
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
20 hours
Beginner
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
Select Categories
Filter by
Instructor
Level
Language
Duration
We found 1 course available for you
-52%
Data Science and Analytics
20 hours
Beginner
Data Science and Analytics is a crucial course in the …
₹1,450.00₹3,000.00
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.
