Frequently Asked Questions
Q. What are the Class timings?
Answer: The classes will take place on Saturdays & Sundays from 10:30 am to 1:30 pm
Q. How do I enrol in the course?
Answer: Visit https://www.fsm.ac.in/big-data-and-data-analytics-program and click on “Apply” to enrol in the course or you can register yourself at exed@fsm.ac.in
Q. To whom I can contact for more information about the course?
Answer: You can mail us on exed@fsm.ac.in or contact on 9166085159
Q. Will I get a physical or e-certificate?
Answer: You will receive the Physical certificate, it’s a joint certification by UCR, USA and FORE, India.
Q. On which platform will the classes be held?
Answer: The classes will be either on MS Teams, WebEx.
Q. Will I get the recording for every session so that recall it whenever I want?
Answer: Recordings will be available, but for a limited time period.
Q. Will there be further negotiations regarding the course fee except given in the brochure?
Answer: There is early bird discount of Rs 5000/- and corporate discount as well, bring your friend along with you and both will get the discount.
You can mail us on exed@fsm.ac.in
Q. Do we get the software to work on it while attending the classes?
Answer: You will receive the required software, reading material, worksheets once the course started.
Q. What qualifications does the faculty have?
Answer: There are six faculty members, five are from IITs with 20-40 years of work experience and one is from UCR, USA. Please refer the brochure for more details.
Q. Is there any placement benefit?
Answer: There will not be any placement guarantee, however will send your CV to our past participants, who are at very senior level.
Q. Can GST be waived off?
Answer: No, it’s as per the government rules
Q. How many students are there in the Batch?
Answer: Generally, 40-50 students are there in the Batch.
Q. Will there be any discount, if I will not take the Certificate?
Answer: Certificate is part of the programme.
Q. Hope the course material is included in the cost?
Answer: Course material cost is Rs 5500/-+GST, this is not included in the fees.
Q. What are the learnings a participant may expect from the course?
Answer:
- Critically analyse existing Big Data datasets and implementations, taking practicality, and usefulness metrics into consideration. Ability to select and implement machine learning techniques and computing environment that are suitable for the applications under consideration
- Ability to solve problems associated with batch learning and online learning, and the big data characteristics such as high dimensionality, dynamically growing data and in particular scalability issues
- Ability to understand and apply scaling up machine learning techniques and associated computing techniques and technologies.
- Ability to recognize and implement various ways of selecting suitable model parameters for different machine learning techniques.
- Ability to integrate machine learning libraries and mathematical and statistical tools with modern technologies like hadoop and mapreduce.
- Employ advanced statistical analytical skills to test assumptions, and to generate and present new information and insights from large datasets
- Have a critical understanding of complex computing application areas and apply skills in advanced topics to find resolution, such as cloud computing and security aspects
Q. Please share the course curriculum.?
Answer:
Quick course Overview
Machine Learning Algorithm
- Python: Data structures in Python, Pandas and Numpy.
- Data exploration using pandas & numpy
- Data Visualization
- Cluster Analysis
- Classification Analysis: Decision tree Induction
- Neural Network
- Random Forest and Regression Trees
- Dimensionality Reduction
- Evaluating Classification Performance Bias-variance trade-off; L1 & L2 regularization
- Gradient Boosting Technique for Machine Learning - Hadoop, Spark & Kafka Eco-Systems
- Light GBM: Light Gradient Boosting Machine
- eXtreme Gradient Boosting (XGBoost)
- Catboost Modeling Techniques
- Hyper-parameter optimization using various techniques including Bayes Optimization
- Feature Engineering; Feature Selection and Feature importance
- Interpreting Machine Learning Models using Partial Dependence Plots and LIME
- Out-of-core Machine Learning with Gigabytes of data
Streaming Analytics
- Introduction to Hadoop and its ecosystem; Hadoop file storage formats
- Linux and Hadoop shell commands
- Hadoop streaming
- Hive on Tez and hadoop
- Pig on Tez and Hadoop
- Spark: Machine Learning; Structured Streaming; Deep Learning; Building data pipelines with Hadoop, kafka and NoSQL databases; Learning Koalas; Spark Delta Lake
- Apache Kafka: Building complex Data pipelines; transforming streaming data.
Deep Learning and AI
- Auto encoders and anomaly detection
- Deep Learning with Convolution Neural Network
- Using very Deep Convolution networks and Data Augmentation
- Transfer Learning with ResNet50 and InceptionV3
- Generative-Adversarial Networks (GAN)
- Recurrent Neural Networks
- Natural Language Processing
- Applying deep learning techniques to structured (tabular) datasets
- Experimenting with Tensor Board on Google Colaboratory
NoSQL and Graph Databases
- Introduction to NoSQL Databases and CAP theorem
- MongoDB Document Database; Installation; Querying & Aggregation pipelines; Full text search; Replication; Sharding operations; Transactions; Data pipelines with Spark and Flume; Building Machine learning models using Spark with mongoDB as backend
- Hbase column family database on Hadoop; hbase-hive integration; Data-pipeline using flume
- TIG Stack: telegraf, Influx DB & Grafana for Time Series and IOT Data
- Gephi Open Graph Visualization Platform
- Neo4j Graph Database
