Duration: 1 Month
- 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.
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.
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.
Data Mining – Another career path that supports bioinformatics is the development of new Data Anaytics 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
Week 1 —Time series forecasting
1.1 Introduction: installing Weka packages
1.2 Time series: linear regression with lags
1.3 Using the timeseriesForecasting package
1.4 Looking at forecasts
1.5 Lag creation, and overlay data
1.6 Application: Analyzing infrared data from soil samples
Week 2 — Data stream mining in Weka and MOA
2.1 Incremental classifiers in Weka
2.2 Weka’s MOA package
2.3 The MOA interface
2.4 MOA classifiers and streams
2.5 Classifying tweets
2.6 Application to bioinformatics: Signal peptide prediction
Week 3 — Interfacing to R and other data mining packages
3.1 LibSVM and LibLINEAR
3.2 Setting up R with Weka
3.3 Using R to plot data
3.4 Using R to run a classifier
3.5 Using R to preprocess data
3.6 Application: Functional MRI Neuroimaging data
Week 4 — Distributed processing with Apache SPARK
4.1 What is distributed Weka?
4.2 Installing distributed Weka for Spark
4.3 Using Naïve Bayes and JRip
4.4 Map tasks and Reduce tasks
4.5 Miscellaneous distributed Weka capabilities
4.6 Application: Image classification
Week 5 — Scripting Weka in Python
5.1 Invoking Python from Weka
5.2 Building models
5.4 Invoking Weka from Python
5.5 A challenge, and some Groovy
5.6 Course Summary
Project Work (Optional):
Data Science with Bioinformatics:
Bioinformatics Data Analytics, BigData Analytics , Microarray Data Analysis, Neuroinformatics Data Analysis, Phylogenetic Analysis, Sequence Analysis and more
Call – 8553794025 | firstname.lastname@example.org
Duration: 3 -6 Months