Big Data Fundamentals (Hadoop & Spark) Professional Certificate

Big Data Fundamentals teaches you how modern organisations store, process and analyse data that is too large, too fast or too varied for a spreadsheet or a single database. Mobile money operators, telecoms, banks, humanitarian agencies and e-commerce platforms in Africa now generate millions of records every day, and engineers and analysts who can work with this data at scale are in high demand.

You will start with the ideas behind big data and distributed computing, then learn the Hadoop ecosystem (HDFS, MapReduce, YARN and Hive) so that you understand the foundations every modern platform builds on. The heart of the course is Apache Spark: you will write real PySpark code in free Google Colab notebooks, using DataFrames, Spark SQL, joins, window functions and efficient file formats such as Parquet, and learn how partitions, shuffles and caching affect performance. You will also explore streaming with Kafka and Spark Structured Streaming, NoSQL databases, cloud data warehouses and lakehouses such as Databricks, and machine learning at scale with Spark MLlib.

The capstone gives you a realistic dataset of 1.5 million mobile money transactions from South Sudan, Uganda, Kenya and Rwanda, complete with duplicates and data quality problems. You will clean it, analyse usage patterns and service failures, and detect suspicious transaction behaviour, just as a data team at a mobile money operator would.

Basic Python and SQL are recommended; our Python and data analytics courses are good preparation. Everything runs in a web browser with free tools, so no powerful computer is needed. How the course works: six modules, 23 lessons with more than 35 videos from respected educators including Fireship, Computerphile, freeCodeCamp, Databricks, codebasics and Simplilearn, a hands-on activity in every lesson, a quiz after every module, a capstone project and a final certification examination. Most learners finish in 8 to 10 weeks at 4 to 5 hours per week.