Learn SAS® Data Science by Epoch Research Institute India Pvt. Ltd. (www.epoch.co.in)

What is data scientist?
Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.

SAS Data Science Advantage:
Comprehensive Training:
 Learn big data, advanced analytics and data science your way – in the classroom or in a blended format at your own pace.
Globally Recognized Credentials: 
Programs include practice exams and exam vouchers for earning a certification.
Complete Toolset:
Learn using SAS, R, Python, Pig, Hive and Hadoop.
Hands-On Learning:
Get full access to SAS software for learning and practice for the duration of the program.
Real-World Case Studies:
Practical application based on actual case studies enhances your learning.
Personal Coach: 
SAS experts are available to guide you, answer questions and keep you on track.

Typical job duties for data scientists:
There's not a definitive job description when it comes to a data scientist role. But here are a few things you'll likely be doing:

• Collecting large amounts of unruly data and transforming it into a more usable format.
• Solving business-related problems using data-driven techniques.
• Working with a variety of programming languages, including SAS, R and Python.
• Having a solid grasp of statistics, including statistical tests and distributions.

• Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
• Communicating and collaborating with both IT and business.
• Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line.

What’s in a data scientist’s toolbox?
These terms and technologies are commonly used by data scientists:

• Data visualization: the presentation of data in a pictorial or graphical format so it can be easily analyzed.
• Machine learning: a branch of artificial intelligence based on mathematical algorithms and automation.
• Deep learning: an area of machine learning research that uses data to model complex abstractions.

• Pattern recognition: technology that recognizes patterns in data (often used interchangeably with machine learning).
• Data preparation: the process of converting raw data into another format so it can be more easily consumed.
• Text analytics: the process of examining unstructured data to glean key business insights.

 
A side from those qualities, you’ll also need a solid understanding of:

• Statistics and machine learning.
• Coding languages such as SAS, R or Python.
• Databases such as MySQL and Postgres.
• Data visualization and reporting technologies.
• Hadoop and MapReduce.

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