Course Summary:
This course will introduce the fundamentals of data analytics, including data collection, cleaning, analysis, and visualization. Students will learn about the basics of statistical analysis, machine learning, and data visualization tools and techniques. The course will cover practical applications of data analytics, such as business intelligence, marketing analytics, and customer segmentation. Students will engage in hands-on projects to apply their knowledge and skills in a practical setting.
Learning outcomes of the course:
The following are the learning outcomes of the course:
- Understand the principles and definition of data analytics, and identify the different types of data and data sources.
- Gain knowledge of the data analytics process and understand the key trends and challenges associated with data analytics.
- Learn the principles of data collection and cleaning, as well as various methods and tools used for data collection and cleaning.
- Develop skills in data cleaning and pre-processing techniques, and learn how to assess and improve data quality.
- Acquire a basic understanding of statistical analysis, including descriptive statistics, inferential statistics, and correlation and regression analysis.
- Develop an understanding of the basics of machine learning, including supervised and unsupervised learning algorithms, classification and clustering techniques, and model selection and evaluation.
- Gain knowledge of data visualization principles and understand the types of visualizations and their applications.
- Develop skills in using data visualization tools and software, and learn about design principles and best practices for data visualization.
Overall, the course aims to equip learners with a strong foundation in data analytics, enabling them to collect, clean, analyze, and visualize data effectively, as well as make data-driven decisions
Who should attend this course?
This course would be beneficial for individuals who work with data, such as:
- Data Analysts
- Data Scientists
- Business Analysts
- Marketing Analysts
- Research Analysts
- Data Engineers
- Data Managers
- Database Administrators
- Management Professionals
- Anyone interested in learning about data analytics and its applications in various fields.
Essentially, anyone who wants to develop their skills in data analytics, including data collection, cleaning, analysis, and visualization, would benefit from attending this course.
Course Duration:
This course can be completed in 4-8 Days,+/- depending on your needs.
Course Outline:
- Introduction to Data Analytics
- Definition and principles of data analytics
- Types of data and data sources
- Overview of the data analytics process
- Key trends and challenges in data analytics
- Data Collection and Cleaning
- Principles of data collection and cleaning
- Data collection methods and tools
- Data cleaning and pre-processing techniques
- Data quality assessment and improvement
- Statistical Analysis
- Basics of statistical analysis
- Descriptive statistics and data distributions
- Inferential statistics and hypothesis testing
- Correlation and regression analysis
- Machine Learning
- Introduction to machine learning
- Supervised and unsupervised learning algorithms
- Classification and clustering techniques
- Model selection and evaluation
- Data Visualization
- Principles of data visualization
- Types of visualizations and their applications
- Data visualization tools and software
- Design principles and best practices
