Most of the working professionals struggle to find a project after learning new technologies. 

Project based learning is a step to bridge that gap.

 Analytics Track

  • Mathematics for Data Science
  • Analytical Techniques and Machine Learning  
  • Deep Learning with TensorFlow 
  • Spark, Pig, Hive

 Big Data Track

  • Hadoop Ecosystem (Hadoop, Pig, Hive, Flume, Sqoop)
  • Spark (SparkSQL, DataFrames, Streaming, MLib)
  • Kafka, NiFi, Integrations
  • NoSQL Architecture, Data Modeling
Project Based Learning (Analytics)
Project Based Learning (Big Data)

Before you take a technology learning decision ask following questions

  • Would you have greater chance of success learning a single technology / language like Python / Java or would you rather learn a full technology track which would help you deliver real life projects? 
  • What skills are required to work as big data engineer, data analyst and data scientist? 

First step is to understand the learning track to start working as a data engineer or data analyst or data scientist – though technology stack could change but core concepts would remain the same. 

For example Data engineer could typically start with Data Structures + Java + HDFS + MR + Pig + Hive + Kafka + NiFi + Scala + Spark + JavaScript + NoSQL databases + Data Integration and transformation

Whereas Data scientist could typically start with Algebra + Statistics + Analytical techniques + Python + R + TensorFlow + Artificial Intelligence + Deep learning + Spark

Add to these few common skills like Cloud + Devops