It would be live & interactive online session with Industry expert Instructor.
IoT has opened new doors in the field of science and technology and is continuously changing our personal and professional lives, consumer habits and the way we do business. In the manner the IoT is advancing by 2020 it will become a norm and more than 50 billion devices are estimated to be connected to the internet.
Collabera TACT’s IoT corporate training is designed to help participants understand the concepts and uses of IoT. This corporate training not only includes the theoretical understanding of different sensors, cloud platforms and gateways but also provides hands-on integration experience of IoT components with Cloud platform. With the help of this training, participants will be able to gain in-depth understanding of IoT applications and should be able to build their own end-to-end IoT solution.
Introduction- Concepts and Technologies behind Internet of Things (IoT)
Concepts & Definitions –Identification, localization, wireless protocols, data storage and security; Collecting, communicating, coordinating, and leveraging the data from connected devices; understand how to develop and implement IoT technologies, solutions, and applications. Machine Learning, Distributed Computing, Artificial Intelligence.
- IoT Network Architecture
- IoT Device Architecture
- IoT Application Architecture
- Client Server vs Publish Subscribe Architecture
IoT Device Design & Management
- Sensors – Classification & selection criteria based on nature, frequency and amplitude of signal
- Embedded Development Boards – Arduino, Raspberry Pi, Intel Galileo, ESP8266
- Interfacing peripherals & Programming GPIOs – Input/output peripherals, Sensor modules
- Design Considerations – Cost, Performance, Scalability & Power Consumption tradeoffs
- Overview of Operating Systems for IoT Devices: Linux, Contiki, Riot, Brillo
IoT Communication Protocols
- Wired Communication Protocols – UART, USART, SPI, I2C, ModBUS, CAN
- Wireless Communication Protocols – Bluetooth, Beacons, WiFi, Overview of Zigbee, 6lowPAN, LPWAN and other IoT communication technologies and protocols (coverage area, frequency range, power usage, interference aspects and legalities)
- Networking Protocols – OSI Reference Model, TCP/IP, Ethernet
- Application Protocols – HTTP, Web sockets, MQTT, Overview of CoAP, XMPP, AMQP
- Device management, discovery, addressing
- Device to Device or Machine to Machine communication (M2M)
Concept & Architecture of Cloud
- Role of Cloud Computing in IoT
- Tools, API and Platform for integration of IoT devices with Cloud
- IoT cloud platform and integration with Gateway (Thingspeak, AWS IoT and Pubnub)
- Web services and APIs
Overview of IoT Analytics and Security
Overview of IoT Analytics and Security
The objective of this training program is to re-skill data scientists. The volume of data is rapidly increasing with proliferation of IoT devices. IoT has turned everything into potential source of data. Data in its raw form is not always useful. Data need to be processed to transform into information. The volume, velocity and variety of data have made conventional processing and analytical approaches obsolete.
The course introduces participants to fundamental understanding of sensor data, systems, and innovative and novel analytical approaches. Machine learning methods are used for data analysis, this is where they are similar to data mining, but the main goal of machine learning is to automate decision models. Algorithms are the heart and soul of machine learning and they help computers to find hidden insights. So in essence machine learning algorithms need to be learnt. The machine needs to learn from data. Data will have multi dimensions- Type (quantitative or qualitative), amount (big or small size) and number of variables available to solve a problem. Learning algorithms should also be as general purpose as possible. We should be looking for algorithms that can be easily applied to a broad class of learning problems.
R and Python are leading programming languages that have an array of packages for IoT data analytics. This course introduces R, python and various advance python packages being used in IoT analytics. Standard R & Python IDEs are going to be used to perform hands-on sessions/programming exercises.
Computer fundamentals, IoT basics, Programming fundamentals and knowledge of statistics
Understanding Data, Information, knowledge and Wisdom (DIKW Pyramid), Types of Data, Physical and logical representation of Data, Natural languages – Symbolic representation, Computer languages – Data Encoding, Storage and interpretation
Handling of sensor data, data pre-processing and integration of different data sources, Heterogeneity and distributed nature, Selection of sensor to capture right set of data, Analog to digital conversion, Time and frequency domain analysis, Sampling theorem, Aliasing, Selection and cleaning, Edge analytics
Statistics is about extracting meaning from data, Techniques for visualizing relationships in data and systematic techniques for understanding the relationships, Exploring data – visualization, Correlation and Regression, Probability distributions.
Concept of machine learning, Introduction to R programming, Regression- Linear and non linear, Algorithms- MLR, Logistics and nonlinear regression, Classification, Algorithms- SVM, decision trees, boosted decision trees, Naïve bayes, Quality of classification – Concepts of ROC, hit rate, kappa statistics and K-S statistics, Feature selection – Learn feature selection methods for regression- Ridge and LASSO
Feature selection methods for classification methods- Information value based, filter based and wrapper based, Algorithms and techniques for marketing analytics – Conjoint analysis, Hidden Markov models
The training was really interactive as well as we got all our queries sorted by the expert.
Training was useful.
Training was good.