Starting the journey with Data — Peeking into my Diploma in Big Data Analytics
Data can tell more than just a story!
The world realized the importance of data with the rise in data analysis tools and techniques. Storing, managing, processing and sharing data related to a business requires professional help and expensive hardware. Good quality data can be used to improve business processes, forecast demands, find weak links and even better manage employees.
My interest in data analytics sparked after taking an elective Data Science course to fulfil the degree requirement. Although I became a cross-platform mobile app developer after graduation but never hesitated to try new stuff. Soon after graduation, I started a postgraduate diploma in Big Data Analytics. The diploma had six courses spread across two modules and covered from basics to intermediate knowledge and since it was designed for both students and professionals from any background so it was easy for everyone in my class.
The diploma courses included:
- Python Foundations for Big Data Analytics
- Big Data Wrangling: Getting Exploratory Insights into your Big Data Lakes
- Business Intelligence (BI) and Big Data Visualization
- Infrastructure Development for Real-Time Big Data Analytics
- Big Data Base Management with NoSQL Data Stores
- Machine Learning for Big Data
The Python foundation course taught Python language basics required for analyzing, cleaning, manipulating and exploring datasets before it is made ready for machine learning models. The course included regular programming tasks using Python language but was focused on improving code efficiency to ensure less process time.
The Data Wrangling course dived into techniques for data manipulation and storage options for big data along with the using technologies for real-time analytics. Hadoop & its architecture, MapReduce, YARN, and Apache Spark were taught with hands-on practice for data wrangling.
The Business Intelligence course covered an introduction to BI, systems used for BI, types of data structure-wise, data types and tools like sqlyog, PowerBI, and OracleBI along with a course project where the student was required to build an interactive dashboard based on the data available. See figures 1 and 2.