Course Details

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

• Data Analysts and Business Analysts
• IT professionals seeking to move into data roles
• Software Engineers and Developers
• Data Scientists (beginner to intermediate)
• Database Administrators and BI Developers
• Professionals working in operations, marketing, finance, or engineering seeking to leverage data
• Anyone interested in learning practical Big Data and Analytics skills

• 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 will be able to:

• Understand the core concepts of big data and data analytics.
• Work with key big data technologies such as Hadoop, Spark, and NoSQL databases.
• Apply data analytics techniques including descriptive, predictive, and prescriptive analytics.
• Use Python and relevant libraries (e.g., Pandas, NumPy, Scikit-learn) for data analysis.
• Visualize and interpret data using effective dashboards and visualization tools.
• Implement data pipelines and use cases with real-world data.

Day 1

Introduction to Big Data and Data Analytics

• Welcome and introduction
• Pre-test
• Introduction to Data Science, Analytics, and Big Data
• Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
• Big Data Characteristics: The 5Vs (Volume, Velocity, Variety, Veracity, Value)
• Big Data Ecosystem Overview (Hadoop, Spark, Kafka, etc.)
• Data Sources: Structured, Semi-structured, Unstructured
• Overview of Data Processing Pipeline
• Case Study

Day 2

Data Acquisition, Storage, and Preprocessing

• Data Collection Methods: APIs, Web Scraping, Logs, Sensors, etc.
• Data Storage Technologies:

• Relational Databases (SQL)
• NoSQL Databases (MongoDB, Cassandra, HBase)
• Data Lakes vs Data Warehouses

• Data Cleaning and Preprocessing Techniques:

• Handling Missing Data
• Outlier Detection
• Data Normalization and Transformation

• Hands-on exercise

Day 3

Big Data Tools and Platforms

• Introduction to Hadoop and HDFS Architecture
• MapReduce Programming Model
• Apache Spark
• Data Ingestion Tools: Apache NiFi, Sqoop, Flume
• Real-Time Data Streaming Overview: Kafka and Spark Streaming
• Hands-on exercise

Day 4

Data Analytics and Machine Learning

• Data Exploration and Feature Engineering
• Introduction to Machine Learning for Big Data

• Supervised vs Unsupervised Learning
• Classification, Regression, Clustering Techniques

• Popular Algorithms: Decision Trees, Random Forest, K-Means, Linear Regression
• Model Evaluation and Validation
• Hands-on exercise

Day 5

Data Visualization, Dashboarding, and Final Project

• Data Visualization Principles and Best Practices
• Tools for Data Visualization:

• Python Libraries (Matplotlib, Seaborn, Plotly)
• Tableau / Power BI (Overview)

• Designing Effective Dashboards
• Introduction to Business Intelligence (BI) Concepts
• Final Group Project
• Course Recap, and Q&A
• Post-test
• Certificate ceremony