About Internship Program
● Learn job critical skills like Big Data & Hadoop frameworks, and leverage the functionality of AWS services.
● The Big Data Engineer course helps you learn to use the database management tool and MongoDB via
interactive sessions & projects.
Advantages
● Understand Big Data and its analytics in the real world.
● Analyze the Big Data framework like Hadoop and NoSQL to efficiently store and process Big Data to generate
analytics.
● Design of Algorithms to solve Data-Intensive Problems using the Map Reduce Paradigm.
● Design and Implementation of Big Data Analytics using pig and spark to solve data-intensive problems and
generate analytics.
● Design and Implementation of Big Data Analytics using pig and spark to solve data-intensive problems and
generate analytics.
● Implement Big Data Activities using Hive.
Who Should Attend
Beneficial for - Students of UG/PG Programs & Research Scholars of Information Technology and Computer Science,
etc.
Program Details
- Duration: 45 days
- Date: 23rd May to 6th July 2023
- Monday to Saturday
- Time: 10:00 am to 4:00 pm
- Venue: Kalinga University
- Program Fee: 10 ,000/-
Program Schedule
| Module-I Introduction to BIG DATA |
||
|---|---|---|
| S.No. | Topics | Duration |
| 01 | Introduction to Big data | |
| 02 | The three Vs of big data | |
| 03 | The value—and truth—of big data | |
| 04 | The history of big data | 5 days |
| 05 | Big data use cases | |
| 06 | Big data challenges | |
| 07 | How big data works |
| Module-II Introduction to Enabling Technologies for Big Data |
||
|---|---|---|
| S.No. | Topics | Duration |
| 01 | Predictive Analytics | |
| 02 | NoSQL Databases | |
| 03 | Knowledge Discovery Tools | |
| 04 | Stream Analytics, In-memory Data Fabric | |
| 05 | Distributed Storage | 5 days |
| 06 | Data Virtualization | |
| 07 | Data Integration | |
| 08 | Data Preprocessing | |
| 09 | Data Quality |
| Module-III Introduction to Big Data Platforms |
||
|---|---|---|
| S.No. | Topics | Duration |
| 01 | Apache Hadoop, Apache storm | |
| 02 | Cloudera | |
| 03 | Amazon Web Services | 5 days |
| 04 | Oracle | |
| 05 | Snowflake |
| Module-IV Big Data and Analytics |
||
|---|---|---|
| S.No. | Topics | Duration |
| 01 | Classification of Digital Data | |
| 02 | Structured and Unstructured Data | |
| 03 | Data Warehouse | 5 days |
| 04 | Environment Big Data Analytics | |
| 05 | Classification of Analytics | |
| 06 | Big Data Analytics importance |
| Module-V Introduction to Mongodb and Mapreduce |
||
|---|---|---|
| S.No. | Topics | Duration |
| 01 | Why Mongo DB | |
| 02 | Terms used in RDBMS and Mongo DB | |
| 03 | Data Types | 5 days |
| 04 | MongoDB Query Language | |
| 05 | Introduction to Big Data |
| Module-VI Introduction to Mapreduce |
||
|---|---|---|
| S.No. | Topics | Duration |
| 01 | Introduction to Mapreduce | |
| 02 | Mapper- Reducer | |
| 03 | Combiner - Partitioner | 5 days |
| 04 | Searching | |
| 05 | Sorting | |
| 06 | Compression |
| Module-VII Introduction to Hive and Pig | ||
|---|---|---|
| S.No. | Topics | Duration |
| 01 | Introduction to Hive | |
| 02 | Introduction to Architecture | |
| 03 | File Formats | |
| 04 | Hive Query Language Statements | 5 days |
| 05 | Partitions – Bucketing – Views | |
| 06 | Features – Philosophy - Use Case for Pig - Pig Latin Overview - Pig Primitive Data Types |
|
| 07 | Complex Data Types - Piggy Bank - User-Defined Functions - Parameter Substitution – Diagnostic |
|
| 08 | Example using Pig Technology |
| Module-VIII Introduction to Data Analytics with R |
||
|---|---|---|
| S.No. | Topics | Duration |
| 01 | Introduction to Machine Learning | |
| 02 | Supervised Learning | |
| 03 | Unsupervised Learning | |
| 04 | Regression Model | |
| 05 | Clustering | 10 days |
| 06 | Collaborative Filtering | |
| 07 | Associate Rule Making | |
| 08 | Decision Tree | |
| 09 | Big Data Analytics with BigR |
Resource Persons
- Mr. Omprakash Dewangan
HoD Incharge
Dept. of Computer Science & Faculty of Information Technology
- Mr. Pawan Kumar Jaiswal
Assistant Professor
Faculty of Information Technology
