Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Data analysts and aspiring data professionals
• IT professionals transitioning to data roles
• Business analysts and decision-makers
• Software engineers and developers working with large datasets
• University students or graduates in computer science, engineering, or related fields
• Anyone interested in gaining foundational skills in big data analysis

Prerequisites: Basic understanding of programming (preferably Python) and data concepts is helpful but not required.

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants should be able to:

• Understand the core concepts and challenges of big data and its analysis.
• Differentiate between traditional and big data analysis approaches.
• Work with structured and unstructured datasets.
• Apply data cleaning and preparation techniques for big data.
• Perform basic statistical analysis and data summarization.
• Create meaningful visualizations from large datasets.
• Understand the fundamentals of tools such as Hadoop, Spark, and Python for big data analysis.
• Interpret analytical outputs to support data-driven decision-making.

DAY 1

Introduction to Big Data and Data Analysis

• Welcome and introduction
• Pre-test
• What is Big Data? Characteristics (Volume, Velocity, Variety, Veracity, Value)
• Differences between traditional and big data analytics
• Big data sources and types: Structured, Semi-structured, Unstructured
• Introduction to data analysis lifecycle
• Overview of big data platforms (Hadoop, Spark, NoSQL)
• Hands-on Exercise

DAY 2

Data Acquisition, Cleaning, and Preparation

• Data ingestion methods: Batch vs. Real-time
• Tools and technologies for data ingestion (Kafka, Flume, Sqoop)
• Data cleaning and preprocessing techniques
• Handling missing data and outliers
• Data transformation and normalization
• Hands-on Exercise

DAY 3

Exploratory Data Analysis (EDA)

• Introduction to EDA: Goals and importance
• Summary statistics (mean, median, mode, variance, correlation)
• Identifying patterns and anomalies
• Data visualization techniques for big data
• Tools for EDA: Pandas, Matplotlib, Seaborn
• Hands-on Exercise

DAY 4

Introduction to Big Data Tools for Analysis

• Introduction to Hadoop and HDFS
• Overview of Apache Spark for big data analysis
• Spark components: Spark SQL, Spark MLlib, Spark Streaming
• Introduction to PySpark: DataFrames and RDDs
• Hands-on Exercise

DAY 5

Applied Data Analysis & Case Studies

• Basic predictive analytics overview (regression, classification intro)
• Building a simple data pipeline
• Interpreting analysis results for decision-making
• Real-world case studies in:

• Retail analytics
• Social media data analysis
• IoT/sensor data

• Final Project
• Review, and Q&A
• Post-test
• Certificate ceremony