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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
With the speed of the progress maintained in the change being brought to the world in terms of electronic and digital aspects, more and more information regarding people keeps getting dumped on the company server. This data is amassed through a variety of means like databases, server logs, and even social media and each bit of it can prove to be useful for the organisation. Therefore, the requirement for data analytics is not just high, it is estimated to grow even further in the coming times.
Advance Diploma in Big Data Analytics prepares its participants for this need and ensures them a career of stability and growth. It will teach them how to figure out hidden correlations in the gathered data, dig out patterns that will allow the companies to gain the edge they need and give the candidates the ability to make better and more accurate decisions.
Simply put, the 4 V’s that make the greatest obstacles for the candidates; variety, volume, veracity, and velocity, will be the greatest strengths of the candidates as a result of this course, in the data analytics domain.
The Highlights
- Government certification
- NIELIT faculty
- 16 week-long
- NIELIT certificate
- Fee waiver for SC/ST students
- Hostel facilities available
Programme Offerings
- Project works
- hands on learning
- Lab facilities
- placement assistance
- Internship
Courses and Certificate Fees
| Certificate Availability |
|---|
| no |
The programme fee for a general category student is Rs. 40,000.
Eligibility Criteria
Education
Only those candidates are eligible to apply for the course who have a degree in B.Tech/ME/BE/B.Sc/M.Sc/DOEACC or PGDCA in either A or B level/3-year diploma degree in either Electronics, Computer Science or IT/BCA/MCA or a degree equivalent to any of these.
The candidates will also need a decent background in the programming field to be eligible to apply.
Certification Qualifying Details
The applicants must finish the course and complete all its contents and projects to gain the certificate at the end of the programme.
What you will learn
Applicants will shine in their fields with the brilliant skill sets they would have developed through this course.
- At the beginning of the course, applicants will master Linux programming step by step, by first learning its basic commands to finally going complete shell scripting.
- Python, one of the most demanded programming skills in the digital field, will be taught in detail to the participants.
- Concepts of Hadoop and Big Data and the ability to configure the data structure as well as administer the processes of the software will be an important part of the candidate’s syllabi.
- Applicants will learn what the concepts of machine learning are, how its algorithms work, and how to build ML applications through it.
- The structures of APIs and configuration of pyspark will be teaching the applicants to run cluster applications.
- The knowledge of R programming is an essential tool for professionals in the field as it enables them to statistically organise data mines and packages.
- Participants will also be taught how to build applications using Java so that it can later be used in Hadoop’s environment.
Who it is for
The course refers to the following people as the ideal students of the programme.
- Enthusiasts of the data mining world who want to gain the skills to leave an impression on the industry are well-suited candidates for the course.
- Freshers who wish to start a new career in this expanding sector will be able to use this programme as a great point to amass all the required tools and insights.
Admission Details
Applicants wanting to pursue the course are asked to follow the below-mentioned steps to apply to the programme.
Step 1: Since the offline admission procedure has been discontinued, for now, the applicants are requested to visit the course’s official website: https://www.nielit.gov.in/calicut/course-calendar?coursecode=SW200
Step 2: When the participants have opened the course website on their systems, they will see a chart on the right-hand side of the page with a note saying ‘Apply Now’.
Step 3: Upon clicking the note, another page will open with the heading ‘Online Registration’.
Step 4: From the page, candidates will have to click on the link of Online registration.
Step 5: An online registration form will open and the candidates will have to register on the site.
Step 6: This step requires that after the candidates have submitted their registration form, they complete the registration process through the payment of the registration fee.
Step 7: After the candidates have made the payments, they will have to wait a little while before the selected list of candidates is published.
Step 8: The rest of the instructions will be given to the applicants if they get selected for the programme.
The Syllabus
- Linux environment
- Commands
- Built-in tools for data analysis.
- Java programming in Linux
- 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 -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
- Machine learning.
- 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
Evaluation process
Applicants do not need to give any special exams for this course. The percentage received by the candidates in their qualifying degree will decide their selection in the programme.