BDAP-301
| Formats: | Asynchronous |
| Blended | |
| Online | |
| Onsite | |
| Part-time | |
| Level: | Advanced |
| Prerequisites: | |
| Recommended Knowledge | |
| Intermediate Python Programmin | |
| Basic SQL Knowledge | |
| Foundational Data Concepts | |
| Core Python | |
Formats: We offer our training content in a flexible format to suit your needs. Contact Us if you wish to know if we can accommodate your unique requirements.
Level: We are happy to customize course content to suit your skill level and learning goals. Contact us for a customized learning path.
Big Data Analysis with Python (BDAP-301)
Big Data Analysis with Python: Unlocking Insights from Massive Datasets
Are you ready to transform overwhelming volumes of data into strategic advantages? In today's data-driven world, the ability to effectively analyse big data is no longer a luxury—it's a necessity. Businesses are drowning in information, but few possess the skills to extract meaningful insights that drive growth and innovation.
This is where Big Data Analysis with Python comes in. Designed for data professionals, analysts, and developers in South Africa, this comprehensive course will equip you with the in-demand skills to master the tools and techniques required to process, analyse, and visualise vast datasets using Python and its powerful ecosystem. Move beyond traditional spreadsheets and unlock the true potential of your data.
Target Audience
This course is ideal for professionals seeking to advance their skills in big data processing and analysis, including:
Data Analysts & BI Developers
Looking to handle larger datasets and more complex transformations.
Junior Data Scientists
Aiming to apply their Python skills in a big data environment.
Software Engineers & Developers
Interested in building scalable data-intensive applications.
Database Professionals
Wanting to transition into big data roles.
Prerequisite Skills
- Intermediate Python Programming: Solid understanding of Python syntax, data structures (lists, dictionaries, sets), functions, and object-oriented programming concepts.
- Basic SQL Knowledge: Familiarity with relational databases and SQL queries.
- Foundational Data Concepts: Basic understanding of data types, data relationships, and data quality.
- (Desirable but not strictly required): Prior experience with Pandas for data manipulation.
What One Will Learn (Learning Outcomes)
Upon completion of this course, you will be able to:
- Understand the Big Data Landscape: Grasp the core concepts, challenges, and key components of the big data ecosystem.
- Master PySpark: Efficiently use Apache Spark's Python API to process, transform, and analyse massive datasets.
- Develop Data Pipelines: Design and implement robust ETL/ELT pipelines for batch and real-time data processing.
- Optimise Data Storage: Apply best practices for data partitioning, file formats (Parquet, ORC), and Spark caching to enhance performance.
- Perform Advanced Analytics: Conduct exploratory data analysis and build foundational machine learning models on big data.
- Navigate Cloud Data Platforms: Understand the architecture and basic usage of big data services on AWS, Azure, and Google Cloud Platform.
- Troubleshoot & Tune: Identify and resolve common issues and optimise the performance of Spark applications.
Target Market
This course is aimed at the growing demand for big data skills within the South African market, across various sectors including:
Financial Services
Banks, insurance companies, fintech startups.
Telecommunications
Network data analysis, customer behaviour.
Retail & E-commerce
Consumer analytics, supply chain optimisation.
Mining & Energy
Operational efficiency, predictive maintenance.
Healthcare
Patient data analysis, clinical research.
Government & Public Sector
Data-driven policy making, service delivery.
Big Data Labs is particularly relevant for individuals and corporate teams in major South African economic hubs like Gauteng (Johannesburg, Pretoria), Western Cape (Cape Town), and KwaZulu-Natal (Durban), where data-intensive industries are prominent.
Course Outline: Big Data Analysis with Python
This course provides a hands-on, practical journey through the essential concepts and tools for big data analysis using Python, building from foundational principles to advanced techniques.
Module 1: Introduction to Big Data and Python for Analytics
- Understanding Big Data: Characteristics (Volume, Velocity, Variety, Veracity) and challenges
- Overview of the Big Data Ecosystem: Hadoop, Spark, and cloud platforms
- Python for Data Science Refresher: NumPy, Pandas basics for large datasets
- Setting up Your Big Data Analysis Environment: Anaconda, PySpark installation, cloud environment overview
Module 2: Data Acquisition and Storage for Big Data
- Working with Large Files: Efficiently reading CSV, JSON, Parquet, and Avro formats
- Distributed File Systems Concepts: HDFS, and cloud object storage (AWS S3, Azure Data Lake Storage, Google Cloud Storage)
- Connecting to Big Data Sources: Basic interaction with NoSQL (e.g., MongoDB, Cassandra) and SQL databases in a big data context
Module 3: Core Big Data Processing with Apache Spark and PySpark
- Introduction to Apache Spark: Architecture, components (SparkSession, RDDs, DataFrames)
- Getting Started with PySpark: Setting up and running Spark jobs locally and in the cloud
- DataFrame API Essentials: Data loading, selection, filtering, and basic transformations
- Spark Transformations vs. Actions: Understanding lazy evaluation
Module 4: Advanced Data Wrangling and Transformation with PySpark
- Data Cleaning Techniques: Handling missing values, outliers, and data inconsistencies at scale
- Complex Data Manipulation: Joins, unions, aggregations, and window functions in PySpark
- User-Defined Functions (UDFs): Extending Spark's capabilities with custom Python logic
- Working with Semi-Structured Data: JSON and XML processing in Spark
Module 5: Optimising Big Data Storage and Performance
- Columnar Storage Formats: Deep dive into Parquet and ORC for efficient big data storage
- Data Partitioning and Bucketing: Strategies for query optimisation and performance
- Spark Caching and Persistence: Best practices for iterative computations
- Memory Management in Spark: Tuning Spark applications for optimal resource usage
Module 6: Introduction to Real-time Data Streaming with PySpark
- Concepts of Stream Processing: Why real-time matters in big data
- Introduction to Spark Structured Streaming: Processing data from Kafka, file systems, and other sources
- Building Simple Streaming Applications: Real-time aggregation and output
Module 7: Big Data Analytics, Machine Learning, and Visualization
- Exploratory Data Analysis (EDA) on Big Datasets: Summarising and visualising large-scale data
- Introduction to Spark MLlib: Scalable Machine Learning with PySpark (Regression, Classification, Clustering basics)
- Integrating with Visualization Tools: Best practices for visualising insights from big data using Python libraries (e.g., Matplotlib, Seaborn, Plotly) on aggregated data.
Module 8: Cloud Big Data Platforms and Deployment Strategies
- Overview of Managed Cloud Data Services: AWS EMR, Azure Synapse Analytics, Google Dataproc, Databricks
- Deploying PySpark Applications to the Cloud: Practical considerations and basic deployment patterns
- Cost Management in Cloud Big Data: Strategies for optimising cloud expenditure
- Project Work: An end-to-end big data analysis project applying learned concepts