Course Title: Certified Senior Big Data Analyst
Proficiency Level: Advanced
Prerequisite Requirements: Prior knowledge in basic data analytics and big data technologies required. Completion of “Certified Big Data Analyst” or equivalent recommended.
Course Description:
Unit 1: Introduction to Advanced Big Data Analytics
Unit 2: Big Data Technologies and Ecosystem
Unit 3: Data Acquisition and Integration
Unit 4: Data Preprocessing and Cleaning
Unit 5: Exploratory Data Analysis (EDA) in Big Data
Unit 6: Advanced Data Visualization Techniques
Unit 7: Machine Learning for Big Data Analysis
Unit 8: Deep Learning and Neural Networks
Unit 9: Natural Language Processing (NLP) in Big Data
Unit 10: Sentiment Analysis and Text Mining
Unit 11: Big Data for Business Intelligence
Unit 12: Predictive Analytics and Modeling
Unit 13: Time Series Analysis in Big Data
Unit 14: Anomaly Detection and Fraud Analytics
Unit 15: Big Data Security and Privacy
Unit 16: Big Data Ethics and Governance
Course Objectives:
- Understand the principles and significance of advanced big data analytics in various industries.
- Familiarize themselves with the latest big data technologies and tools within the big data ecosystem.
- Acquire, clean, and integrate diverse data sources to create a unified data repository.
- Perform exploratory data analysis (EDA) to gain insights and identify patterns in big data.
- Utilize advanced data visualization techniques to effectively communicate complex findings.
- Apply machine learning algorithms to big data for predictive modeling and pattern recognition.
- Explore deep learning and neural networks to analyze unstructured data and perform image and speech recognition tasks.
- Employ natural language processing (NLP) techniques to extract valuable information from text data.
- Conduct sentiment analysis and text mining to understand customer sentiments and opinions.
- Leverage big data for business intelligence and data-driven decision-making.
- Build predictive models to forecast future trends and outcomes using big data.
- Analyze time series data for trend analysis and forecasting in big data scenarios.
- Identify anomalies and detect fraud using advanced analytics on big data.
- Implement security measures and privacy protection techniques for big data environments.
- Understand the ethical implications of big data analytics and govern data usage responsibly.
