Syllabus Of Diploma in Big Data Aanlytics

The syllabus for a Diploma in Big Data Analytics may vary from one institution to another, but I can provide you with a general outline of the topics typically covered in such a program. Keep in mind that the specific courses and their content may change over time and depending on the institution offering the diploma. Here is a sample syllabus:

Semester 1: Introduction to Big Data Analytics

  1. Introduction to Big Data
    • Definition of Big Data
    • Characteristics of Big Data (Volume, Velocity, Variety, Veracity)
    • Importance and applications of Big Data Analytics
  2. Data Collection and Storage
    • Data sources (structured, semi-structured, unstructured)
    • Data acquisition and collection techniques
    • Data storage technologies (relational databases, NoSQL databases)
  3. Data Preprocessing
    • Data cleaning and transformation
    • Data integration and aggregation
    • Data quality assessment
  4. Programming for Big Data
    • Introduction to programming languages (Python, R)
    • Data manipulation and analysis using programming languages

Reference Books:

  1. “Big Data: A Revolution That Will Transform How We Live, Work, and Think” by Viktor Mayer-Schönberger and Kenneth Cukier
  2. “Big Data Analytics: Turning Big Data into Big Money” by Frank J. Ohlhorst
  3. “Big Data Analytics: Methods and Applications” edited by S. Srinivasan, V. G. S. Kumar, and K. G. Srinivasagan
  4. “Big Data Analytics: A Practical Guide for Managers” by Kim H. Pries and Robert Dunnigan
  5. “Big Data: Principles and best practices of scalable realtime data systems” by Nathan Marz and James Warren
  6. “Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking” by Foster Provost and Tom Fawcett

Semester 2: Big Data Technologies

  1. Hadoop Ecosystem
    • Introduction to Hadoop
    • Hadoop Distributed File System (HDFS)
    • MapReduce programming model
  2. Apache Spark
    • Introduction to Spark
    • Spark architecture and components
    • Spark programming in Scala or Python
  3. NoSQL Databases
    • Types of NoSQL databases (e.g., MongoDB, Cassandra)
    • Data modeling and querying in NoSQL databases

Reference Books:

  1. “Hadoop: The Definitive Guide” by Tom White
  2. “Spark: The Definitive Guide” by Bill Chambers and Matei Zaharia
  3. “Big Data: Principles and best practices of scalable realtime data systems” by Nathan Marz and James Warren
  4. “Kafka: The Definitive Guide” by Neha Narkhede, Gwen Shapira, and Todd Palino
  5. “HBase: The Definitive Guide” by Lars George
  6. “Big Data Analytics with R and Hadoop” by Vignesh Prajapati
  7. “Big Data Technologies and Applications” edited by Borko Furht and Flavio Villanustre

Semester 3: Advanced Topics in Big Data Analytics

  1. Machine Learning for Big Data
    • Introduction to machine learning
    • Supervised and unsupervised learning algorithms
    • Applying machine learning to Big Data
  2. Data Visualization and Reporting
    • Data visualization techniques (e.g., Tableau, Power BI)
    • Design principles for effective data visualization
    • Reporting tools and dashboards
  3. Big Data Analytics in Industry
    • Case studies and real-world applications of Big Data analytics
    • Ethical and legal considerations in Big Data

Reference Books:

  1. “Advanced Analytics with Spark: Patterns for Learning from Data at Scale” by Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills
  2. “Machine Learning Yearning” by Andrew Ng
  3. “Big Data Analytics with Python” by Armando Fandango
  4. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  5. “Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples” by Sayan Mukhopadhyay
  6. “Graph Algorithms: Practical Examples in Apache Spark and Neo4j” by Mark Needham and Amy E. Hodler
  7. “Data Science for Business and Decision Making” by Colleen McCue and James D. Savage

Semester 4: Capstone Project

  1. Capstone Project
    • Students work on a real-world Big Data analytics project
    • Data collection, preprocessing, analysis, and presentation
    • Project presentation and documentation
  2. Research Methods
    • Introduction to research methods and data analysis
    • Literature review and research proposal preparation

Please note that the actual content and order of these courses may vary, and institutions may offer elective courses or additional specialized topics. Additionally, the program may include hands-on lab work, assignments, and assessments to reinforce the concepts learned throughout the program. It’s advisable to check with the specific institution offering the diploma for the most up-to-date and detailed syllabus.