राष्ट्रीय इलेक्ट्रॉनिकी एवं सूचना प्रौद्योगिकी संस्थान
National Institute of Electronics & Information Technology
|Course||:||Advanced Diploma in BigData Analytics|
|Starting Date||:||18 March,2019|
|Last Date To Apply||:||06 March,19|
In today’s world there is data available in abundance from variety of sources like web server logs, social media, and large databases and from diverse domains like Ecommerce, Medical, Scientific etc. Big data analytics is the process of examining these data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. Business people, Doctors, Scientists etc. can use this to improve their services.
The main challenge to the analysis of big data comes because of the 4 V’s– volume, velocity, variety and veracity. For effective analytics, we need to deal with high volume of data of different variety which is being generated in high velocity. The data what is available from such sources is highly unstructured which calls for analytics on the same.
The objective is to make the participants capable of identifying and applying appropriate techniques and tools to solve problems in managing huge quantity of data.
After undergoing this course the participants will become data engineers who can perform analytics operations on data using various tools. They can develop, maintain and evaluate Big Data Solutions for organizations.
|Sl. No||Course Modules||Weeks|
|1||Linux concepts, Java programming||2|
|4||BigData concepts, Hadoop and MapReduce||5|
|5||Hive, Pig, Sqoop, Flume, HBase, SPARK, Machine Learning|
Linux and Java (2 weeks)
Linux environment, commands, built-in tools for data analysis.
Java programming in Linux
R (3 weeks)
Setting up R environment, Variables, Data Types - Vectors, Factors, Lists, Matrices, Arrays, Data Frames, Subsetting. Control Structures, Functions, Debugging tools,. Reading data – Text, CSV, HTML, JSON, MySQL. Grouping functions-apply, lapply, sapply, mapply. Data visualization - barplot, pie, scatterplot, histogram, scatter matrix, ggplot. Statistical Analysis of data-Summary Statistics, Tabulation methods. Probability distributions in R- Normal distribution, Poisson distribution, Binomial distribution. Correlation and Regression, Hypothesis Testing, Graph visualization using igraph, Developing GUI with Shiny, Machine Learning in R
Python (3 weeks)
Python -features, program execution, data structures, List, Dictionary, Tuples, If statements, looping and loop control statements, Functions and Modules, Generators, import statement, namespaces-packages, Class concepts, Exception handling, Regular Expressions, Database access, XML parsing, Python for data analytics – using numpy, matplotlib and pandas,scipy, sci-kit learn and machine learning.
BigData&Hadoop (5 weeks)
Hadoop Architecture and HDFS, Mapreduce Architecture with examples, YARN Architecture, nosql databases with examples, transferring data with Sqoop, data ingestion into Hadoop with Flume, Familiarization of Spark, Mllib and machine learning, Oozie, HBase, Hive and HiveQL, Pig, Distributed processing on a Cluster, Integrating R and Hadoop
Mini project (3 weeks)
General Candidates: Course fee is 40,000 + GST at actuals
SC/ST Candidates: Tuition Fees/Examination fees are waived for SC/ST students admitted under SCSP/TSP. However they are required to remit an amount of 4720 as Advance caution/security deposit. This amount will be considered as caution/security deposit and will be refunded after successful completion of the course. If the student fails to complete the course successfully this amount along with any other caution/security deposits by the student will be forfeited.
ME/MTech/BE/BTech/MSc/BSc / 3 year Diploma in (IT/Computer Science/Electronics), MCA/BCA /Degree holders with PGDCA or DOEACC A or B level or equivalent to any of these with good computer programming knowledge.
|Last date for submitting application:||06-March-2019|
|Selection intimation through website:||07-March-2019 (After 5.00 PM)|
For more details like How to Apply, Placement, Hostel, etc please see the Course Calendar
For more Information Contact the Course Coordinator, Prasoon Kumar KG , Email:firstname.lastname@example.org , Phone 0495 2287266(ext. 239).