basics of linux , hadoop, hive, etl, data, databases, programming, mathematics, analysis
Learn data science from ground up in easy and simple explanation. As this career in data science comprise different skills, this course makes it easy to understand what all we need to gain knowledge to progress in this career. This is not about machine learning which is a small part of data science itself. Gaining knowledge on data will solve a lot of issues an organization faced with when thinking about implementing a data science solution. One can learn two different aspects, one is data and the other is analytics. So learn different ways of data acquisition, cleanup, processing, integrating and creating simple summary reports in this course. Once you know and have data, simply pass this data as input to any of your ML models for best results, but we need data as the raw material for data science and Machine Learning.
Who this course is for:
Anyone who wants to learn data science
1 section • 16 lectures • 1h 35m total length
simple use case
steps in data science
data sourcing and formatting
data quality security of data
data quality continued
Profiling data for data quality
Change data as per needs
Questions on data integration
regression in Machine learning a high level introduction
Experienced IT professional for more than 20 years in Client Server to Big data technologies as a developer, analyst, tester and a manager. Versatile domain experience in ECM/BPM, Banking and Healthcare. Well versed in SDLC , Waterfall and Agile methodologies for software development. Cloudera and Horton works experience in Big Data. Jira and Rally for Agile project management.