CSBDA – Certified Senior Big Data Analyst


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.
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