Step into the fantastic world of the future with this introductory course on the Internet of Things. Understand the key terminologies, explore its history, and watch stories of how IoT inventors are creating a brave new world with their path-breaking devices.
This course deals with basic electronics, microcontroller architectures, sensors, human-machine interfaces (HMI) and basic networking. We use the Arduino platform to teach these concepts. After doing this course, you should be able to put together IoT projects by combining micro controllers and sensors and connect them to cloud with mobile applications.
We get you up and running with the Arduino platform, the Arduino Nano. You will install the tools, write and upload your first piece of code to run on a microcontroller.
In case your C code skills are rusty, this video provides a quick refresher. We also look briefly at the differences between plain C and the Arduino Code Language.
We build a slightly more complex program for a microcontroller. This one feature user inputs via button and a way to control the operation of the microcontroller from an app running on your smartphone.
Is there a way to run microcontroller code without having one at your disposal? In this video, we explore simulation technologies that allow you to build rapid prototypes, giving you greater insight into the working of your code.
We begin our exploration of sensors by learning how to read analog signals from them in our microcontroller code. We also look briefly at some issues while using analog sensors.
A quick overview of all the components you will need as you build your Arduino projects, their usage and functions.
Learn to read ambient temperature and humidity from an integrated sensor by talking to it using digital signals.
We delve into the representation of digital signals on the wire and how signals are represented and transmitted between sensors and controller.
We covered a few sensors in detail in the past videos, but there a wide variety of sensors out there that measure all manner of real world phenomena. This video provides a broad overview of commonly available sensors and sensor modules that play well with the Arduino hardware.
Buttons and displays: IoT devices often require a “human-machine interface” and this is often hard to build due to microcontroller limitations. In this video, we look at buttons and display modules and explain how these are controlled from microcontroller code.
We have been doing a lot of microcontroller coding. Now it is time to look under the hood and understand the internal architectures of these machines. We also look at how code and data are organized.
We have been using one microcontroller platform – the Arduino Nano q- but there are several others out there. Here, we take a walk through the world of popular Arduino hardware – both official and community-supported.
This is the first look at the second of our two microcontroller platforms – the nodemcu. This is a true IoT enabled microcontroller, since it has built in Wi-FI. We jump right in and write our very first program for it and control an LED from the cloud!
Time to take a step back and look deeper into how Wi-Fi works. This video teaches you the basics of networking, Wi-Fi architecture and security.
Here are more advanced exercise using the nodemcu. We connect a few sensors to it and build a sort of mini weather station.
Networks allow us to transport data from one point to another. But how is one device controlled by another? This video looks at the client-server architecture and in particularly at HTTP and RESTful interfaces.
In this video, we introduce MQTT, a lightweight yet powerful protocol for messaging. We use it to build an example, which, while similar in functionality to an example we built before, is transparent in terms of its underlying architecture.
Here, we take a deeper look at messaging and the “pub-sub” pattern. We discuss how it is different from REST and why it is often preferred in the case of IoT devices.
In this video on microcontroller internals, we look at common design patterns used in building firmware. We also go over basic concepts like re-entrancy, mutexes and error handling. We also look at performance aspects – latencies, throughput and other parameters.
Time to take a leap from building a demo to building a product. What should you worry about as you put your IoT device into the end-user’s hand? How is the product deployed, discovered, secured and maintained?
The course focuses on higher-level operating systems, advanced networking, user interfaces, multi-media and uses more compute-intensive IoT applications as examples. We use the Raspberry Pi running Linux as the platform of choice, while also exposing the student to other comparable platforms. It is about gateway devices, where one can achieve scaling in amount of processing.
Get up and running with the Raspberry Pi 3. Learn to make the connections, power it up and take a tour of its desktop environment.
Understand the overall plan for this module. We provide a quick overview of what we’ll learn and of the major projects we’ll be doing.
A more fundamental look at booting the Raspberry Pi 3. Understand how to download an operating system, format an SD card and boot the OS. Also, learn the rudiments of the file system.
While we’ll use the Raspbian image as the reference for the rest of this course, we spend some time in this video looking at what other OS options are available for the Raspberry Pi, and why you might want to check them out.
Learn to operate the Raspberry Pi in “headless mode” by logging into it remotely. We’ll also discuss how to move ata (or files) from your PC to the Raspberry Pi and back.
A short primer on Linux terminal commands, or more specifically, a quick tour through the basic command set for the bash shell. Get comfortable operating you Raspberry Pi without needing a GUI interface.
Time to bring out the electronic components. The Raspberry Pi’s connector allows you extend its functionality using external hardware and in this video, we begin to explore how this is done.
High-level operating systems, such as Linux, require the use of a device driver for user programs to access hardware. We look at why this is, what a device driver does, and how to build one.
We continue the journey we started in video 7 and interface yet more hardware to the Raspberry Pi. We learn how to deal with inputs and digital protocols from within user programs.
Blynk provides a simple means to tie together smartphones and IoT gadgets. In this video, we control a NodeMCU from a smartphone via a Blynk server running on Raspberry Pi.
We expand on the previous example and use it as a basis to discuss machine-to-machine communication, an IoT node vs gateway and related concepts.
This video introduces a high-performance application on the Raspberry Pi – a complete content streaming server.
Learn about the Linux boot up sequence, run levels, and how to run programs automatically on boot up.
A more detailed look at the last example that maps out the control and data paths. Learn to think about performance in terms of throughput and latency.
Explore speech processing on Raspberry Pi. Speech recognition is an important emerging interface for IoT devices and this example introduces the topic.
A more detailed look at how the last example was put together. Once again, we look at performance, introduce metrics, and compare with video processing.
Understand how speech processing and natural language interfaces work.
We’ve covered video playback and speech processing. Now, its time to learn video processing. In this video, we set up our example and demonstrate how it works.
Once again, we look under-the-hood at how the demo is put together, the frameworks used and how each is configured.
Image processing can be very demanding on a processor, but it is increasingly becoming easier, even on platform like the Raspberry Pi. This video provides a quick run-down of the major ideas in this field.
In this video, we introduce machine learning in the context of image recognition, more specifically, face recognition.
A quick overview of the algorithms used in the last demo and how they work.
We revisit many of the topics we covered before and view it through the prism of performance. As we seek to use available hardware to the fullest, it is important to understand where we’re spending our platform’s resources, find out about those we’re running out of and learn to optimize the system.
In this course, we explore the rapidly evolving field of cloud computing and its relation to IoT. We will examine cloud offerings from all the leading providers, including Amazon, Google and Microsoft, and learn to design and deploy solutions to them. This course will also have several case studies, where we examine real-world problem areas such as wearables, home automation, and smart cities.
We now take our Express.js server and run it on the cloud using a “platform-as-a-service” provider. This lets us examine the power and limitations of the PaaS approach.
A more in-depth examination of cloud services and the buckets under which they are classified.
We move to IaaS with Amazon’s cloud offering – AWS. This means creating your own server instance in the cloud, choosing and configuring its OS, and running your service – the Express.js server – on top of it.
The number and variety of cloud services providers has grown in the last few years. This video provides an overview of all the main players in the IaaS space.
In this first of our case studies, we examine what an ideal home automation system will look like and how it will be organized. We’ll talk about the nodes, the gateway and the cloud services that such a system would need.
MQTT, Redis: A modern backend requires more than just a webserver. In this video, we expand our backend to include a database and a message queue on our IaaS platform.
A quick start to using docker – a virtual container for services targeting distributed systems.
So far, we have looked at Wi-Fi as the sole connectivity option. We now expand our gaze to include cellular connectivity and LoRa.
IoT technology is relevant for more than just consumer goods. This video looks at how we might go about building smart city solutions.
Connectivity at ultra-short ranges can be just as important as those at longer distance when it comes to certain IoT applications. We look at bluetooth, NFC and other radio frequency alternatives.
This case study imagines a large retail grocery chain and builds a system of how they might benefit from IoT technology.
Our final project builds a complete IoT cloud application using everything we have learnt so for. Sensor data from an IoT device is logged into a time series database. We then build a dashboard to visualize this data. We also build a rules engine to monitor this data and take specific automatic actions when certain preset conditions are triggered.
This course provides an overview and insights into data generation, analysis, and usage from IoT systems, and uses multiple case studies to explain how Analytics is used in IoT scenarios to accomplish desired outcomes.
It covers basic concepts of IoT, along with its roles in making things “smart” like smart cities, smart buildings and smart things.
A basic coverage of key concepts in IoT and how they relate to each other. This includes a coverage of the wide varieties of sensors, and actuators.
This covers the overall IT stack needed to deal with IoT systems including devices, communication, and data.
A detailed coverage of the wide variety of communication infrastructure of IoT, protocols and interfaces for interacting with IoT systems. Web of things is a focused approach using Internet to interact with IoT systems.
Covers a description of the five kinds of analytics – Descriptive, Inferential, Exploratory, Predictive and Prescriptive analytics and a reference model for IoT.
Covers broad business applications of IoT across verticals ranging from healthcare to smart cities to smart environments.
Covers in detail the application of IoT in sports and monitoring health via human activity recognition.
Covers in detail the various facets of smart environments including smart cities and smart buildings.
Covers broadly the data characteristics of IoT data including time series modelling.
Covers in depth the architecture stack to deal with data analytics in IoT. It covers topics like data acquisition, data communication, data security and data analytics.
It covers a broad spectrum of characteristics of IoT analytics products and comparison metrics.
Covers popular KPIs or goals in IoT analytics problems spanning industries.
A detailed analysis of anomaly detection in IoT business scenarios.
A code walkthrough of a detailed anomaly detection algorithm for a smart environment case study.
An end to end case study of Human Activity Recognition as an IT use case: Descriptive, Exploratory, and Predictive Analytics. Includes a detailed real life data set, and steps in walking through the different analytics with an analytics tool.
This course provides a comprehensive understanding of the IoT data analytics life cycle, with specific examples and case studies. It illustrates milestones and outcomes at each stage of the cycle, starting with data acquisition, through cleaning, exploratory analysis, preparation, and final analysis for achieving desired outcomes
A commentary on the overarching structure of this module of IoT analytics training – “Data Science for IoT”.
Overview of RFID: Examines the overview of RFID as a key early IoT technology for retail.
RFID Use Cases: Examines the various use cases of RFID technology across verticals dominated by retail.
Introduction to iBeacon: Explores use of iBeacon as a recent enabling IoT technology for retail.
Introduction to Industrial Internet: Covers the diverse spectrum of issues, framework and enabling technologies for IoT as applied in industry settings like manufacturing, also termed as Industrial Internet of Things (IIoT).
IIoT Use Cases: Covers a wide range of use cases of IIoT in verticals like manufacturing, transportation and others with focus on preventive maintenance.
Industrial Internet Consortium and its Role: Examines the role of IIC consortium in formulating standards, use cases and evangelism of IIoT.
Overview of IoT Data Science Lifecycle: It presents a customized view of the popular data
science process CRISP-DM as specialized for IoT data.
Stages of IoT Data Science Lifecycle: Covers in brief the needs for IoT data as per each stage of the CRISP methodology.
Technique and Tool View: Covers a spectrum of tools and techniques for each IoT data science process stage.
Overview of Data Ingestion Requirements: Covers the wide ranging needs for IoT data use cases from a data acquisition and ingestion perspective including reliable messaging.
IoT Data Ingestion Frameworks: Covers the breadth of product features of various open source and commercial data ingestion frameworks.
Ingestion with Kafka: Includes details of data ingestion capabilities of Apache Kafka.
Apache Storm for Data Ingestion: Includes details of data ingestion capabilities of Apache Storm.
Amazon Kinesis, Apache Flume and Apache Samza: Coverage of these three frameworks for Data Ingestion.
Overview of IoT Data Cleaning : Covers the needs for data cleaning and types of data cleaning as required of IoT data.
Missing Data: In focus analysis of missing data in IoT streams.
Data Imputation: Covers various approaches for Living with missing data via imputing of the missing data.
KNN Based Data Imputation: A focused approach to data imputation for IoT data with K – Nearest Neighbour Algorithm.
Demo of Imputation Using Mean and Median : Demo of the data imputation approach using global mean and median.
Basic Descriptive IoT Analytics: A coverage of basic measures of central tendency and measures of dispersion.
Code Walkthrough of Basic Descriptive IoT Analytics: A code walkthrough of basic measures of central tendency and measures of dispersion.
Sliding Window Based Descriptive Analytics: Sliding window version of basic measures of central tendency and measures of dispersion for IoT data.
Code Walkthrough: Sliding Window Based Descriptive Analytics for IoT data.
Additional Descriptive IoT Analytics: Covers more measures like kurtosis, and correlation across multiple IoT streams.
Code Walkthrough of Additional Descriptive IoT Analytics : Demo of more measures like kurtosis, and correlation across multiple IoT streams.
Streaming SQL: Feature offered in SQL like language for some Stream processing engines to compute measures of central tendency/dispersion on a window in a stream.
Overview of Inferential Analytics: Covers some broad techniques for handling summaries of data as in IoT data including sampling and sketching.
Reservoir Sampling: A popular concise technique for sampling streams of IoT data.
Sketching and Hashing : Covers popular hashing and sketching techniques for streaming data including Min-Count Sketching and Reservoir Sampling.
Supervised Learning and Classification: Covers a brief of classification approaches as used in IoT data.
Streaming Classification with Decision Trees: A detailed analysis of streaming decision trees based classification technique for streaming IoT data.
Demo using MOA: Demo of streaming classification using MOA toolkit.
Exploratory Data Analytics with Clustering for Streaming Data : A thorough analysis of clustering needs for streaming IoT data.
K-Means Clustering: A coverage of basics of K-means clustering the basis for all cluster analysis.
Streaming Clustering Algorithms: A breadth of different clustering algorithms analysed.
Streaming K-Means Clustering Algorithm : An in-depth analysis of streaming K-Means clustering algorithm.
Need for Prescriptive Analytics : A ground level exploration of the different needs for prescriptive analytics for IoT.
Additional Use Cases of Prescriptive Analytics : An in-depth analysis of some IoT analytics use cases requiring Prescriptive Analytics.
Prescriptive Analytics Techniques: Some popular techniques for prescriptive analytics explored.
Prioritization Techniques: An indepth analysis of some key prioritization techniques, key for prescriptive IoT analytics.
Descriptive Analytics: A code walkthrough of descriptive analytics for IoT air quality data.
Inferential and Exploratory Analytics: A code walkthrough of of sampling and clustering of IoT air quality data.
Predictive Analytics with Classification: A decision tree based predictive analytics demo of IoT air quality data.
This course explains how to implement advanced analytics and machine learning algorithms to IoT data to build complex IoT solutions. After doing this course, a student will be able to choose and apply the appropriate ML algorithms including classification and segmentation on IoT datasets and streaming data.
Gain free access to a variety of supplemental resources like handouts, reference material, guides, lecture transcripts and student forums for a period of 12 months.
Get your doubts solved by the SQTL Faculty via email, phone or chat
2 hours of sessions every month, conducted by IT mentors to resolve your questions and doubts.
Access to a cloud-based solution, for hands-on experience with real-life business data using the latest tools
Avail professional guidance on resume building, interview preparation and identification of relevant opportunities, for the IT field.
Help you get your dream job via industry references, interview preparations and specialized walk-in drives at SQTL Campus.
After completing this specialization, you would have mastered the most popular analytical tools – SAS, R, Python and Advanced SAS (SQL and Macros). Your exhaustive data science skill set would enable you to build your analytics career in a better way.
This course is meant for anyone interested in a career in Big Data, IT or database professionals.