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PG Diploma in Big Data Analytics (PG-DBDA)

NSQF level: 8

The theoretical and practical mix of the Post Graduate Diploma in Big Data Analytics (PG-DBDA) programme has the following focus:

  • To explore the fundamental concepts of big data analytics with in-depth knowledge and understanding of the big data analytics domain
  • To understand the various search methods and visualization techniques and to use various techniques for mining data stream
  • To analyze and solve problems conceptually and practically from diverse industries, such as government manufacturing, retail, education, banking/ finance, healthcare and pharmaceutical
  • To undertake consulting and industrial projects with significant data analysis component for better understanding of the theoretical concepts from statistics, economics and building future solutions data analytics to make an impact in the technological advancement
  • To use advanced analytical tools/ decision-making tools/ operation research techniques to analyze the complex problems and get ready to develop such new techniques for the future
  • To learn Cloud Computing, accessing resources and services needed to perform functions with dynamically changing needs
  • Graduate in Engineering (10+2+4 or 10+3+3 years) in IT / Computer Science / Electronics / Telecommunications / Electrical / Instrumentation, OR
  •  MSc/MS (10+2+3+2 years) in Computer Science, IT, Electronics with Mathematics in 10+2, OR
  • 4-year Graduation in Bioinformatics, OR
  • Post Graduate Degree in Mathematics / Statistics / Physics ,   OR
  • MCA
  • The candidates must have secured a minimum of 55% marks in their qualifying examination
The total fees of the course is Rs. 103,500/- plus Goods and Service Tax (GST) currently 18%.

The course fees has to be paid in two installment as per the schedule.
  • First installment is Rs. 10,000/- plus Goods and Service Tax (GST)  currently 18%.
  • Second installment is Rs. 93,500/- plus Goods and Service Tax (GST) currently 18%.
  

Installation (Ubuntu and CentOS), Basics of Linux, Configuring Linux, Shells, Commands, and Navigation, Common Text Editors, Administering Linux, Introduction to Users and Groups, Linux shell scripting, shell computing, Introduction to enterprise computing, Remote access

Introduction to Cloud Computing: Cloud Computing Basics, Understanding Cloud Vendors (AWS/Azure/GCP), Definition, Characteristics, Components, Cloud provider, SAAS, PAAS, IAAS and other Organizational scenarios of clouds, Administering & Monitoring cloud services, benefits and limitations, Deploy application over cloud. Comparison among SAAS, PAAS, IAAS, Cloud Products and Solutions, Cloud Pricing, Compute Products and Services, Elastic Cloud Compute, Dashboard, Launching Linux VM, Accessing Linux VM, Launching and Accessing Windows server VM, Launching WordPress website, Storage, Databases, Migration Hub, Security, identity and Compliance, Monitoring and Management Services, Analytics on Cloud, Machine Learning framework on Cloud, Hadoop Framework on cloud

  

Python Programming: Python basics, If, If- else, Nested if-else, Looping, For, While, Nested loops, Control Structure, Break, Continue, Pass, Strings and Tuples, Accessing Strings, Basic Operations, String slices, Working with Lists, Accessing list, Operations, Function and Methods, Files, Modules, Dictionaries, Functions and Functional Programming, Declaring and calling Functions, Declare, assign and retrieve values from Lists, Introducing Tuples, Accessing tuples, Visualizing using  Matplotlib, Seaborn, OOPs concept, Class and object, Attributes, Inheritance, Overloading, Overriding, Data hiding, Operations Exception, Exception Handling, except clause, Try-finally clause, User Defined Exceptions, Data wrangling, Data cleaning

R Programming: Reading and Getting Data into R, Exporting Data from R, Data Objects-Data Types & Data Structure. Viewing Named Objects, Structure of Data Items, Manipulating and Processing Data in R (Creating, Accessing, Sorting data frames, Extracting, Combining, Merging, reshaping data frames), Control Structures, Functions in R (numeric, character, statistical), working with objects, Viewing Objects within Objects, Constructing Data Objects, Packages – Tidyverse, Dplyr, Tidyr etc., Queuing Theory, Non parametric Tests- ANOVA, chi-Square, t-Test, U-Test, Interactive reporting with R markdown, Introduction to Rshiny 

  

Oops Concepts, Data Types, Operators and Language, Constructs, Inner Classes and Inheritance, Interface and Package, Exceptions, Collections, Threads, Java.lang, Java.util, Java Virtual Machine, Reflection in JVM, JVM’s architecture, Lambda Expressions, Functional Programming and Interfaces, Introduction to Streams, Introduction of JDBC API

  

Introduction to Business Analytics using some case studies, Summary Statistics, Making Right Business Decisions based on data, Statistical Concepts, Descriptive Statistics and its measures, Probability theory, Probability Distributions (Continuous and discrete- Normal, Binomial and Poisson distribution) and Data, Sampling and Estimation, Statistical Interfaces, Predictive modeling and analysis, Bayes’ Theorem, Central Limit theorem, Data Exploration & preparation, Concepts of Correlation,  Covariance, Outliers, Regression Analysis, Forecasting Techniques, Simulation and Risk Analysis, Optimization, Linear, Nonlinear, Integer, Overview of Factor Analysis, Directional Data Analytics, Functional Data Analysis , Predictive Modelling (From Correlation To Supervised Segmentation): Identifying Informative Attributes, Segmenting Data By Progressive Attributive, Models, Induction And Prediction, Supervised Segmentation, Visualizing Segmentations, Trees As Set Of Rules, Probability Estimation; Overfitting And Its Avoidance: Generalization, Holdout Evaluation Vs Cross Validation; Decision Analytics: Evaluating Classifiers, Analytical Framework, Evaluation, Baseline, Performance And Implications For Investments In Data; Evidence And Probabilities: Explicit Evidence Combination With Bayes Rule, Probabilistic Reasoning; Business Strategy: Achieving Competitive Advantages, Sustaining Competitive Advantages

Python Libraries – Pandas, Numpy, Scipy, Scrapy, Plotly, Beautiful soup

  

Database Concepts (File System and DBMS), OLAP vs OLTP, Database Storage Structures (Tablespace, Control files, Data files), Structured and Unstructured data, SQL Commands (DDL, DML & DCL), Stored functions and procedures in SQL, Conditional Constructs in SQL, data collection, Designing Database schema, Normal Forms and ER Diagram, Relational Database modelling, Stored Procedures. The tools and how data can be gathered in a systematic fashion, Data ware Housing concept, No-SQL, Data Models - XML, working with MongoDB, Cassandra- overview, architecture, comparison with MongoDB, working with Cassendra, Connecting DB’s with Python, Introduction to Data Driven Decisions, Enterprise Data Management, data preparation and cleaning techniques

  

Introduction to Big Data- Big Data - Beyond The Hype, Big Data Skills And Sources Of Big Data, Big Data Adoption, Research And Changing Nature Of Data Repositories, Data Sharing And Reuse Practices And Their Implications For Repository Data Curation, Overlooked And Overrated Data Sharing, Data Curation Services In Action, Open Exit: Reaching The End Of The Data Life Cycle, The Current State Of Meta-Repositories For Data, Curation Of Scientific Data At Risk Of Loss: Data Rescue And Dissemination

Hadoop: Introduction of Big data programming-Hadoop, The ecosystem and stack, The Hadoop Distributed File System (HDFS), Components of Hadoop, Design of HDFS, Java interfaces to HDFS, Architecture overview, Development Environment, Hadoop distribution and basic commands, Eclipse development, The HDFS command line and web interfaces, The HDFS Java API (lab), Analyzing the Data with Hadoop, Scaling Out, Hadoop event stream processing, complex event processing, MapReduce Introduction, Developing a Map Reduce Application, How Map Reduce Works, The MapReduce Anatomy of a Map Reduce Job run, Failures, Job Scheduling, Shuffle and Sort, Task execution, Map Reduce Types and Formats, Map Reduce Features, Real-World MapReduce,

Hadoop Environment: Setting up a Hadoop Cluster, Cluster specification, Cluster Setup and Installation, Hadoop Configuration, Security in Hadoop, Administering Hadoop, HDFS – Monitoring & Maintenance, Hadoop benchmarks,

Apache Airflow: Introduction to Data warehousing and Data lakes, Designing Data warehousing for an ETL Data Pipeline, Designing Data Lakes for ETL Data Pipeline, ETL vs ELT

Introduction to HIVE, Programming with Hive: Data warehouse system for Hadoop, Optimizing with Combiners and Practitioners (lab), Bucketing, more common algorithms: sorting, indexing and searching (lab), Relational manipulation: map-side and reduce-side joins (lab), evolution, purpose and use, Case Studies on Ingestion and warehousing

HBase: Overview, comparison and architecture, java client API, CRUD operations and security

Apache Spark APIs for large-scale data processing: Overview, Linking with Spark, Initializing Spark, Resilient Distributed Datasets (RDDs), External Datasets, RDD Operations, Passing Functions to Spark, Job optimization, Working with Key-Value Pairs, Shuffle operations, RDD Persistence, Removing Data, Shared Variables, EDA using PySpark, Deploying to a Cluster Spark Streaming, Spark MLlib and ML APIs, Spark Data Frames/Spark SQL, Integration of Spark and Kafka, Setting up Kafka Producer and Consumer, Kafka Connect API, Mapreduce, Connecting DB’s with Spark

  

Business Intelligence- requirements, content and managements, information Visualization, Data analytics Life Cycle, Analytic Processes and Tools, Analysis vs. Reporting, MS Excel: Functions, Formula, charts, Pivots and Lookups, Data Analysis Tool pack: Descriptive Summaries, Correlation, Regression, Introduction to Power BI, Modern Data Analytic Tools, Visualization Techniques, Visual Encodings, Visualization algorithms, Data collection and binding, Cognitive issues, Interactive visualization, Visualizing big data – structured vs unstructured, Visual Analytics, Geo-mapping, Dashboard Design

Case Studies on Business intelligence, Analytics, Industry/Enterprise reports etc.

  

Supervised and Unsupervised Learning , Uses of Machine learning , Clustering, K means, Hierarchical Clustering, Decision Trees, Classification problems, Bayesian analysis and Naïve Bayes classifier, Random forest, Gradient boosting Machines, Association rules learning, PCA, Apriori, Support vector Machines, Linear and Non liner classification,  ARIMA, XG Boost, CAT Boost, Neural Networks and its application, Tensorflow 2.x framework: Deep learning algorithms, KNN, NLP, Bert in NLP,NLP transformers, NLTK, Introduction to Pytorch framework, AI and its application

  


Project

120 Hours  
  

Software: A Process, Various Phases in s/w Development, Software life cycle agile model (Self Study of other models), Introduction to Coding Standards, Software Quality Assurance

After completing this courses students shall be expert in following things:

  • To make data driven decisions which provide them a competitive advantage
  • Big Data provides a spring board for AI and they will be ready for Industry 4.0
  • Big Data skills are in high demand for Analytics Professionals and they will edge ahead against their competitors
  • Studying Big Data will broaden their horizon by Surpassing Market Forecast / Predictions for Big Data Analytics
C-DACs - Advanced Computing Training School
Address
:
No.1, Old Madras Road Above Bank of Mysore, Near NGEF Bengaluru
Karnataka 560038
Telephone
:
66116400 /01/02/03, 080-66116560
Contact Person
:
Binu George & M Savithri
Fax
:
080-25247724
e-Mail
:
actsb[at]cdac[dot]in
Courses
:
PG-DAC, PG-DESD, e-DESD, PG-DBDA, PG-DIoT, e-DAC, e-DBDA
C-DACs - Advanced Computing Training School
Address
:
"TIDEL Park", 8th Floor,'D' Block (North), No.4, Rajiv Gandhi Salai, Taramani Chennai
Tamilnadu 600113
Telephone
:
044-22540028,22542226/7
Contact Person
:
Ms.SUNANDHA D
Fax
:
+91-44-22542294
e-Mail
:
chnacts[at]cdac[dot]in
Courses
:
PG-DAC, PG-DESD, e-DESD, PG-DBDA, PG-DIoT, e-DAC, e-DBDA
C-DACs - Advanced Computing Training School
Address
:
Plot E 2/1, Blok - GP, Sector - V, Saltlake Electronics Complex, Bidhannagar, Kolkata
West Bengal 700091
Telephone
:
033 2357 5989 / 9846
Contact Person
:
Asok Bandyopadhyay
Fax
:
033 23575141
e-Mail
:
asok[dot]bandyopadhyay[at]cdac[dot]in
Courses
:
PG-DAC, PG-DBDA, e-DAC, e-DBDA
USMs Shriram Mantri Vidyanidhi Info Tech Academy
Address
:
5th Floor, Vidyanidhi School, Vidyanidhi Road, JVPD Scheme, Juhu Mumbai
Maharashtra 400049
Telephone
:
022 - 26255629
Contact Person
:
Ms. Savita Thakur
Fax
:
022 - 26255629
e-Mail
:
training[dot]vita[at]gmail[dot]com
Courses
:
PG-DAC, PreDAC, PG-DBDA, e-DAC, e-DBDA
C-DACs - Advanced Computing Training School
Address
:
B-30, Sector 62, Institutional Area, Noida
Uttar Pradesh 201307
Telephone
:
0120-3063371-73
Contact Person
:
Mr. V.K. Sharma
Fax
:
0120-3063374
e-Mail
:
cdacacts-noida[at]cdac[dot]in
Courses
:
PG-DAC, PG-DVLSI, PG-DGi, PG-DESD, PG-DMC, PG-DITISS, PG-DAI, e-DESD, PG-DBDA, PG-DIoT, e-DAC, e-DBDA, e-DAI, e-DITISS
C-DAC Advanced Computing Training School
Address
:
C-DAC Innovation Park Sr. No. 34/B/1 Panchvati, Pashan Pune
Maharashtra 411008
Telephone
:
18008430222
Contact Person
:
Mr. Parimal Wagh
Fax
:
NA
e-Mail
:
acts[at]cdac[dot]in
Courses
:
PG-DAC, PG-DVLSI, PG-DESD, PG-DITISS, PG-DAI, e-DESD, PG-DBDA, PG-DIoT, e-DAC, e-DBDA, e-DAI, e-DITISS, PG-DHPCSA
Institute for Advanced Computing and Software Development
Address
:
Dr. D.Y. Patil Educational Complex, Sector 29, Near Akurdi Railway Station, Pradhikaran, Nigdi Pune
Maharashtra 411044
Telephone
:
020 – 27659509, 27652794
Contact Person
:
Wg. Cdr. P.V.C. Patil (Retd)
Fax
:
020 –27650229
e-Mail
:
ittrg[at]iacsd[dot]com
Courses
:
PG-DAC, PreDAC, PG-DITISS, DASDM, PG-DBDA, e-DAC, e-DBDA, e-DITISS
Knowledge-Divine Information Technology Pvt. Ltd.
Address
:
Office No: 2, 1st Floor, "Gokhale Sanchit" Survey No: 846, BMCC Road, Deccan Gymkhana, Pune
Maharashtra 411004
Telephone
:
020 25648081/82
Contact Person
:
Mr. Nachiketas Bhatkar, CEO
Fax
:
020 41051819
e-Mail
:
contact[at]know-it[dot]co[dot]in
Courses
:
PG-DAC, PreDAC, DASDM, PG-DBDA, e-DAC, e-DBDA
Sunbeam Institute of Information Technology
Address
:
Sunbeam IT Park, Phase-II (Rajiv Gandhi Infotech Park) Hinjewadi, Pune
Maharashtra 411057
Telephone
:
7410071951
Contact Person
:
Mr. Nitin Kudale, C.E.O.
Fax
:
020 –24260308
e-Mail
:
siit[at]sunbeaminfo[dot]com
Courses
:
PG-DAC, PG-DESD, PG-DMC, DASDM, e-DESD, PG-DBDA, e-DAC, e-DMC, e-DBDA

Q. What is the Eligibility for PG-Diploma in Big Data Analytics?  
A. The eligibility Criteria for PG-DBDA is Candidate holding any one of the following degrees

  • Graduate in Engineering (10+2+4 or 10+3+3 years) in IT / Computer Science / Electronics / Telecommunications / Electrical / Instrumentation. OR MSc/MS (10+2+3+2 years) in Computer Science, IT, Electronics. 
  • Mathematics in 10+2 (exempted for candidates with Diploma + Engineering) OR
  • Post Graduate Degree in Engineering Sciences with corresponding basic degree (e.g. MSc in Computer Science, IT, Electronics) OR 4-year Graduation in Bioinformatics, OR
  • Post Graduate Degree in Mathematics / Statistics / Physics / MBA Systems, OR 
  • MCA
  • The candidates must have secured a minimum of 55% marks in their qualifying examination

Q. What is the selection criterion?  

A. The selection process consists of a C-DAC Common Admission Test (C-CAT).

Q. What is Fee of course? 

A. The fees for the PG-DBDA course is Rs. 1, 03,500/- (Rupees One Lakh three thousand five hundred only) plus 18 % GST. Due to pandemic scenario a discount of 10% is offered for March 2021 batch.

Q. When the course does commence?  

A. The Course commences twice in the year i.e. February & August. Admission Process       starts in the month of November & May for the respective batches.

Q. Duration of the course?  

A. It’s 24 weeks full-time course

Q. Infrastructure Facilities available? 
 
A. Fully equipped classrooms with adequate capacity to accommodate students and 
     state-of-art labs to explore your computing skills

Q. Hostel & Canteen facility available?  

A. Accommodation for out station candidates is facilitated by some of centers. Please 
     refer Admission Booklet.
 
Q. Bank loan assistance for the other centers?  

A. Facility of educational loans is available for the selected candidates, which will be 
     provided by Nationalized banks only.

Q. Revision of the course contents, is it every six months?  

A. The course contents are revised according to the real world needs and when found 
     relevant to emerging trends.

Q. Do you have centralized placement cell?  

A. Yes. We do have a Centralized Placement Programme where the respective center
     actively participates to organize the campus interviews for all the students.
 
Q. What is the value of the course in the international market?  

A. The course has been a trend-setting course due to its unique curriculum and the 
     opportunities that it generates; hence it gives the edge over for the students and 
     gives an international edge.