Data Analytics is going to be a minimal requirement for every job in the future as data becomes the competitive edge and IP for companies across the world. Data mining, analysis, visualization and identifying patterns has become easier with open source technologies and freemium tools. This course teaches data analytics beginning with Pandas, which has become the de facto standard to programmatically analyze data. Further, we delve into Apache Spark Concepts, which is the most popular big data processing framework out there and an evolutionary step after Hadoop. Finally we teach concepts of how to visualize your data, put together stories that you can build opportunities on and make a positive impression in your work environment(s)
- Aspiring data scientists, data analysts and data engineers.
- Any individual who intends to move their career towards data domain.
- Instructor-Led Courses
- Hands-on Labs
- Knowledge Checks
This course includes activities that will allow you to test new skills and apply knowledge through hands-on lab activities
- A desktop (PC/MAC) with at least 2 gig memory and 10GB free space with at least 1.5GHz processor
- A html5 compatible browser like Chrome, Firefox, Waterfox, Safari
- Registration done upfront
Pradeep is a passionate technical and business leader with over 19 years of experience managing, coaching and growing teams around the globe ranging from Fortune 500 to small and medium companies. Pradeep has an MBA degree from Duke University that complements his technical ability to engage both business leaders and technical architects that brings clarity and simplicity to conversations. He has spent the past several years helping organizations increase their agility through a combination of Lean and Agile principles coupled with application engineering, data engineering with continuous improvement practices.
- Recognize terminology and concepts as they to data analytics, statistics & data processing
- Use Python programming language to code away
- Understand Pandas API for data indexing, transforming, aggregating and visualization
- Understand Apache Spark framework and leveraging big data processing
- Real time use cases with appreciation towards statistics, that forms the fundamental ground for data analytics and data science
- Trade off scenarios on when to use Pandas, Spark and/or both together
- Use Jupyter notebook interface over the web to write code, and version control with git
- Use Pandas and Spark to redefine the traditional data warehouse design, develop and persist
- Understand how to go from static visualization to dynamic and how to publish insights to an internet website with rich analytics that is your own