What you will learn
- Understand the fundamentals of data analytics and its applications
- Learn various data analysis techniques, including descriptive, diagnostic, predictive, and prescriptive analytics
- Gain proficiency in data visualization tools and techniques
- Utilize statistical methods to analyze and interpret data
- Apply machine learning algorithms for predictive modeling and pattern recognition
- Learn how to clean, preprocess, and prepare data for analysis
- Develop the ability to communicate insights effectively through data storytelling
Beneficial for
- Data Analysts
- Business Analysts
- Data Scientists
- Data Engineers
- IT Professionals interested in data analysis
Course Pre-requisite
- Basic understanding of statistics and probability
- Familiarity with data manipulation using spreadsheet software (e.g., Excel)
- Basic programming knowledge (e.g., Python, R) is beneficial but not mandatory
Course Outline
Overview of data analytics and its significance
Key concepts and terminologies in data analytics
Applications of data analytics across different industries
Understanding data types and structures
Data cleaning and preprocessing techniques
Exploratory data analysis (EDA) using statistical methods and visualization tools
Summarizing and visualizing data using descriptive statistics
Understanding central tendency, dispersion, and distribution of data
Creating visualizations such as histograms, box plots, and scatter plots
Identifying patterns and relationships in data
Correlation and regression analysis techniques
Root cause analysis and hypothesis testing
Introduction to predictive modeling
Supervised and unsupervised learning algorithms
Model evaluation and validation techniques
Optimizing decision-making using prescriptive analytics
Linear and integer programming techniques
Simulation and optimization methods
Principles of effective data visualization
Tools and techniques for data visualization (e.g., Tableau, Power BI)
Designing interactive dashboards and reports
Introduction to machine learning concepts and algorithms
Supervised and unsupervised learning techniques
Implementing machine learning models for predictive analytics
Introduction to big data technologies (e.g., Hadoop, Spark)
Processing and analyzing large-scale datasets
Distributed computing and parallel processing techniques
Understanding ethical considerations in data analytics
Privacy regulations and compliance (e.g., GDPR, HIPAA)
Best practices for ethical data handling and usage
