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CareerStart's Data Science career track is a career builder for aspiring data scientists. This course is outlined to prepare students to handle the job of a Data Scientist effectively. The purpose of this track is to provide students a strong conceptual knowledge and lots of hands-on exercises to help understand better and to appreciate the best practices used in the industry. The program is very detailed and totally hands-on. Students will get familiar with the various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. The following are just a few of the exercises that students actually perform in class as part of their lab exercises:

  • Evaluating Modelling Methods
  • Reading and getting Data into R
  • Algorithms using Map Reduce
  • Writing Hadoop MapReduce Programs
  • Big data analytics
  • Hadoop Ecosystems and Yarn
  • Hive Architecture and Installation, HBASE

This is a rigorous program and you must be willing to devote a substantial amount of time and effort to complete it. Your gains will be in direct proportion to the amount of effort you put into it.

What is Data Science ?

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. A Data Scientist or an Analyst usually explains what is going on by processing history of the data. Data Scientists not only do exploratory analysis to discover insights but also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier. Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

The following basic knowledge is assumed of each student:

  • Reasonably good programming skills.
  • Working experience with databases.