SAS

Course: Statistical Analysis System (SAS)
Duration: 1 Month
Eligibility:
·         Graduate or PGs from a recognized University in Life Science ( Biotech / Botany / Zoology / Microbiology / Chemistry) in Science with Physics, Mathematics, Statistics/ Nursing / Home Science / Food and Nutrition / Agriculture / Dairy technology / Horticulture / Forestry / Fisheries) OR
·         Graduate from a recognized University in Health Sciences (MBBS / BDS / BAMS /BHMS / BUMS / BVSc. / BSSM / BNYS) OR
·         Graduate from a recognized University in Allied Health Sciences (BMLT / BScMLT / BPT / BMIT / BSc MIT / BHIA / BScHIA / BOT / BSc (Sp & Hg)/ BASLP / BSc Opt. / Pharmacy (BPharma) OR
·         BE ,M.E in Biotech / BCA / BSc IT ,M.Sc IT / BSc CS, M.Sc CS
·         Post graduate and doctoral students, researchers & corporate working people related to life sciences.

About Course:
Although analytics has been around for a long while, it wasn’t until the last 5 to 10 years that its importance in the business field has been realised. It was in the last 10 years that technology has been revolutionized and we now produce about 2.5 quintillion bytes of data every day. This is more data than that was collected in two years, previously. What has also changed in the last decade is that we now have the means to sift through these 2.5 quintillion bytes of data in a reasonable amount of time. All these changes have major implications for organizations today.
In organizations, analytics enables professionals to convert extensive data and statistical and quantitative analysis into powerful insights that can drive efficient decisions.
Therefore with analytics organizations can now base their decisions and strategies on data rather than on gut feelings. Moreover with the rate at which this data can be analyzed, organizations are able to keep tabs on the customer trends in near real time. As a result effectiveness of a strategy can be determined almost immediately. Thus with powerful insights, analytics promises reduced costs and increased profits.
The analytics Industry is one of the fastest growing in modern times with it poised to become a $50 billion market by 2017. With this sudden surge in the analytics industry there is a tremendous increase in the demand for analytics expertise across all domains, throughout all major organizations across the globe. It has been predicted that by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. 

·         Data Analytics  users access data resources to perform job duties in specific application domains. Bench-based researchers, both in academia and in industry, provide the classic example of a bioinformatics user, but this group is broadening in scope. For example, medical professionals (e.g., physicians and genetic counselors) utilize bioinformatics resources in medical contexts for the purposes of diagnosis, treatment, and counseling of patients.
·         Data scientists are biologists who employ computational methods in order to advance the scientific understanding of living systems.
·         Data engineers create the novel computational methods needed by bioinformatics users and scientists. Thus, a bioinformatics engineer must have strengths in computational and statistical sciences and must have general competency in biomedical sciences.


Once trained in the field, there are a number of opportunities to build a career. Some examples are listed below:

Individual contributor - Many scientific labs, both in the academic and commercial sector, are hiring people trained in bioinformatics to support the research of the lab. Positions are available for various levels and types of training. People in these positions generally work on a specific area of research.

Core facilities - Many institutions create a central resource for labs in an institution. These resources are call core facilities. Members of such groups often have a mix of skills and work on many different research projects with researchers in many different labs.

Educators - There is a demand for teaching bioinformatics at many different levels. Some Ph.D. level bioinformaticians will pursue an academic career, build their own research agenda and teach at the university level. In addition, there are a number of institutions who host a dedicated facility to teach bioinformatics to people inside the institution as well as to the greater community.

Software developers - Another career path that supports bioinformatics is the development of new algorithms and new tools. There are companies dedicated to building and deploying computational tools. Other bioinformatics software developers are hired within core facilities and within individual research labs.

Industries for this skilled Professional
Bio IT company, Biotechnology, Pharmaceutical , Hospitals, Clinical Organization , Diagnostic Laboratories, Health care Industry, Academic and research institutions


Syllabus:
  • Introduction to Analytics
  • Introduction to SAS, GUI
  • Types of Libraries, Creating
  • Variable Attributes
    1. Name, Type, Format, Informat, Label
  • Introduction to Data steps and Proc steps
  • DATA Understanding
    1. Reading, Importing, Exporting and Copying Data
  • Conditional Statements (Where, If, If then Else)
  • Appending, Merging and Sorting Datasets
  • Proc steps like " Proc Means, Proc Freq, Proc Sort
  • Output Delivery System (ODS)
  • SAS Functions and Options
  • List Input, Delimiters, Reading missing Values, and non standard values
  • Do loops
  • Generating Data
    1. Execution
    2. Output Statements
    3. Nesting Do loops
    4. Do While and Do Until Statement
  • Arrays
    1. Dimensions
    2. Array elements and Range
    3. Proc report
  • Introduction to Data base, Relational Data base concepts
  • Proc SQL, Data integrity Constraints, Creating table and Inserting Values
  • Proc SQL codes to
    1. Retrieve & Summarize data
    2. Group, Sort & Filter
    3. Using Joins
    4. Indexes
  • Macros
    1. Defining and calling a macro
    2. Macro Parameters and Variables
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            Bangalore | Karnataka | India
 Contact No :  8553794025| info@biocurationssl.com


Project Work (Optional):
Statistical Analysis in Bioinformatics
 BigData Analytics , Microarray Data Analysis, Neuroinformatics  Analysis, Phylogenetic  Analysis, Sequence Analysis, Matlab Bioinformatics Tool Box, Matlab Microarray data analysis.


Duration: 3 -6 Months
Fees: 6000 INR


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