Advanced Programming in MATLAB Data Types and Data Structures

Advanced Programming in MATLAB Data Types and Data Structures

Get advanced level, in-depth knowledge of this fourth-generation, multi-paradigm numerical programming language. Learn the essential and unique MATLAB data types necessary for MATLAB programming and data analysis and how to use Cells, Tables, Time Tables, Structures and Map Containers.

Basic Course Description

MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language which is frequenlty being used by engineering and science students. While teaching to students and observing different MATLAB related courses on Simpliv for more than six months now. I realized that there is a need for a course which should cover the key data types such as Cells, tables, time tables, structures and Map containers which should provide the students with the essential skills for taking full advantage of MATLAB strengths in data analysis and programming.

In this course we not only cover these data Types but also demonstrates different functions and operation and their conversions to make analysis and programming a greater experience.

The following are the outlines of this course.

Segment 1: introduction to the Course
Segment 2: Cell Data Type
Segment 3: Table Data Type
Segment 4: Time Table Data Type
Segment 5: Structures
Segment 6: Map Containers
Segment 7: Conversion between Different Data Types
Your Benefits and Advantages

You receive knowledge from a Ph.D. in Computer science with over 10 years of teaching and 15 years of programming experience and another decade of experience in using MATLAB
The instructor has 6 courses on simpliv on MATLAB including a best seller course
The overall rating in these courses are (4.5/5)
If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
You have lifetime access to the course
You have instant and free access to any updates i add to the course
You have access to all Questions and discussions initiated by other students
You will receive my support regarding any issues related to the course
Check out the curriculum and Freely available lectures for a quick insight
Student Testimonials!

This is the second Simpliv class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.

Jeff Philips

Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!

Oamar Kanji

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

Student Testimonials! who are also instructors in the MATLAB category

"Concepts are explained very well, Keep it up Sir...!!!"

Engr Muhammad Absar Ul Haq instructor of course "Matlab keystone skills for Mathematics (Matrices & Arrays)"

It's time to take Action!

Click the "Add to Cart" button at the top right now!

Time is limited and Every second of every day is valuable.

I am excited to see you in the course!

Best Regrads,

Dr. Nouman Azam

Who is the target audience?

Researchers, Entrepreneurs, Instructors, College Students, Engineers, Programmers, Simulators
Basic knowledge
General know how of MATLAB programming
What will you learn
The essential and unique MATLAB data types necessary for MATLAB programming and data analysis
At the end you can confidently use different data types and structures such as Cells, Tables, Time Tables, Structures and Map Containers
You will be able to convert between different data types
To continue:

Data Science vs Data Analytics vs Big Data

Data Science vs Data Analytics vs Big Data

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

We live in a data-driven world. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Now that Hadoop and other frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them.

In this article on Data Science vs Data Analytics vs Big Data, I will be covering the following topics in order to make you understand the similarities and differences between them.
Introduction to Data Science, Big Data & Data AnalyticsWhat does Data Scientist, Big Data Professional & Data Analyst do?Skill-set required to become Data Scientist, Big Data Professional & Data AnalystWhat is a Salary Prospect?Real time Use-case## Introduction to Data Science, Big Data, & Data Analytics

Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics.

What Is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.


It also involves solving a problem in various ways to arrive at the solution and on the other hand, it involves to design and construct new processes for data modeling and production using various prototypes, algorithms, predictive models, and custom analysis.

What is Big Data?

Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.


What is Data Analytics?

Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data.


What Does Data Scientist, Big Data Professional & Data Analyst Do?

What does a Data Scientist do?

Data Scientists perform an exploratory analysis to discover insights from the data. They also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.

Roles of Data Scientist

What do Big Data Professionals do?

The responsibilities of big data professional lies around dealing with huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity.

Roles of Big Data Professiona

Big data professionals describe the structure and behavior of a big data solution and how it can be delivered using big data technologies such as Hadoop, Spark, Kafka etc. based on requirements.

What does a Data Analyst do?

Data analysts translate numbers into plain English. Every business collects data, like sales figures, market research, logistics, or transportation costs. A data analyst’s job is to take that data and use it to help companies to make better business decisions.

Roles of Data Analyst

Skill-Set Required To Become Data Scientist, Big Data Professional, & Data Analyst

What Is The Salary Prospect?

The below figure shows the average salary structure of **Data Scientist, Big Data Specialist, **and Data Analyst.

A Scenario Illustrating The Use Of Data Science vs Big Data vs Data Analytics.

Now, let’s try to understand how can we garner benefits by combining all three of them together.

Let’s take an example of Netflix and see how they join forces in achieving the goal.

First, let’s understand the role of* Big Data Professional* in Netflix example.

Netflix generates a huge amount of unstructured data in forms of text, audio, video files and many more. If we try to process this dark (unstructured) data using the traditional approach, it becomes a complicated task.

Approach in Netflix

Traditional Data Processing

Hence a Big Data Professional designs and creates an environment using Big Data tools to ease the processing of Netflix Data.

Big Data approach to process Netflix data

Now, let’s see how Data Scientist Optimizes the Netflix Streaming experience.

Role of Data Scientist in Optimizing the Netflix streaming experience

1. Understanding the impact of QoE on user behavior

User behavior refers to the way how a user interacts with the Netflix service, and data scientists use the data to both understand and predict behavior. For example, how would a change to the Netflix product affect the number of hours that members watch? To improve the streaming experience, Data Scientists look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted. Another metric is bitrate, that refers to the quality of the picture that is served/seen — a very low bitrate corresponds to a fuzzy picture.

2. Improving the streaming experience

How do Data Scientists use data to provide the best user experience once a member hits “play” on Netflix?

One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.

For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.

By determining all these factors one can improve the streaming experience.

3. Optimize content caching

A set of big data problems also exists on the content delivery side.

The key idea here is to locate the content closer (in terms of network hops) to Netflix members to provide a great experience. By viewing the behavior of the members being served and the experience, one can optimize the decisions around content caching.

4. Improving content quality

Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers.

In addition to the internal quality checks, Data scientists also receive feedback from our members when they discover issues while viewing.

By combining member feedback with intrinsic factors related to viewing behavior, they build the models to predict whether a particular piece of content has a quality issue. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by the Netflix users to close the loop on quality and replace content that does not meet the expectations of the users.

So this is how Data Scientist optimizes the Netflix streaming experience.

Now let’s understand how Data Analytics is used to drive the Netflix success.

Role of Data Analyst in Netflix

The above figure shows the different types of users who watch the video/play on Netflix. Each of them has their own choices and preferences.

So what does a Data Analyst do?

Data Analyst creates a user stream based on the preferences of users. For example, if user 1 and user 2 have the same preference or a choice of video, then data analyst creates a user stream for those choices. And also –
Orders the Netflix collection for each member profile in a personalized way.We know that the same genre row for each member has an entirely different selection of videos.Picks out the top personalized recommendations from the entire catalog, focusing on the titles that are top on ranking.By capturing all events and user activities on Netflix, data analyst pops out the trending video.Sorts the recently watched titles and estimates whether the member will continue to watch or rewatch or stop watching etc.
I hope you have *understood *the *differences *& *similarities *between Data Science vs Big Data vs Data Analytics.

Data Analytics For Beginners

Data Analytics For Beginners

🔥Intellipaat Data Analytics training course: In this data analytics for beginners video you wi...

In this data analytics for beginners video you will see introduction to data analytics, what is data analytics, who is a data analyst and role & responsibilities of a data analyst. There is a use case in data analytics as well to get hands on knowledge.

Why Data Analytics is important?

Data analysis is an internal organisational function performed by Data Analysts that is more than merely presenting numbers and figures to management. It requires a much more in-depth approach to recording, analyzing and dissecting data, and presenting the findings in an easily-digestible format.

Why should you opt for a Data Analytics career?

If you want to fast-track your career then you should strongly consider Data Analytics. The reason for this is that it is one of the fastest growing technology. There is a huge demand for Data Analyst. The salaries for Data Analytics is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Data Analytics tutorial is your stepping stone to a successful career!

Stream - Solution for Big Data Structures

Stream - Solution for Big Data Structures

Now, let’s take a look at how these functional concepts have been applied to building a type of big-data data structure called a stream.

Now, let’s take a look at how these functional concepts have been applied to building a type of big-data data structure called a stream.

What Do We Mean by Stream?

What is a stream, exactly? It’s an ordered sequence of structured events in your data.

These could be actual events, like mouse clicks or page views, or they could be something more abstract, like customer orders, bank transactions, or sensor readings. Typically, though, an event is not a fully rendered view of a large data model but rather something small and measurable that changes over time.

Each event is a point of data, and we expect to get hundreds — or even millions — of these events per second. All of these events taken together in sequence form our stream.

How can we store this kind of data? We could write it to a database table, but if we’re doing millions of row insertions every second, our database will quickly fall over.

So traditional relational databases are out.

Enter the Message Broker

To handle streaming data, we use a special piece of data infrastructure called a message broker.

Message brokers are uniquely adapted to the challenges of event streams. They provide no indexing on data and are designed for quick insertions. On the other end, we can quickly pick up the latest event, look at it, and move on to the next one.

The two sides of this system — inserts on the one end and reads on the other — are referred to as the producer and the consumer, respectively.

We’re going to produce data into our stream — and then consume data out of it. You might also recognize this design from its elemental data structure, the queue.

An In-Memory Buffer

So now we know how we’re going to insert and read data. But how is it going to be stored in-between?

One option is to keep it all in memory. An insert would add a new event to an internal queue in memory. A consumer reading data would remove the event from memory. We could then keep a set of pointers to the front and end of the queue.

But memory is expensive, and we don’t always have a lot of it. What happens when we run out of memory? Our message broker will have to go offline, flush its memory to disk, or otherwise interrupt its operation.

An On-Disk Buffer

Another option is to write data to the local disk. You might be accustomed to thinking of the disk as being slow. It certainly can be. But disk access today with modern SSDs or a virtualized disk — like Amazon’s EBS (Elastic Block Store) — is fast enough for our purposes.

Now that our data can scale with the size of the SSD, we can slap on our server. Or even better, if we’re in a cloud provider, we can add a virtualized disk to scale as much as we need.

Aging Out of Data

But wait a minute. We’re going to be shoveling millions of events into our message broker. Aren’t we going to run out of disk space rather quickly?

That’s why we have a time to live (TTL) for the data. Our data will age out of storage. This setting is usually configurable. Let’s say we set it to one hour. Events in our stream will then only be stored for one hour, and after that, they’re gone forever.

Another way of looking at it is to think of the stream on disk as a circular buffer. The message broker only buffers the last hour of data, which means that the _c_onsumer of this data has to be at most one hour behind.

Introducing Your New Friend, Apache Kafka

In fact, the system I’ve just described is exactly how Apache Kafka works. Kafka is one of the more popular big-data solutions and the best open-source system for streaming available today.

Here’s how we create a producer and write data to it in Kafka using Java.

Properties properties = new Properties();
properties.put("bootstrap.servers", "localhost:9092");
properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer kafkaProducer = new KafkaProducer(properties);
kafkaProducer.send(new ProducerRecord("mytopic", 0, "test message")); // Push a message to topic "mytopic"

Now on the other side we have our consumer, which is going to read that message.

Properties properties = new Properties();
properties.put("bootstrap.servers", "localhost:9092");
properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
properties.put("", "mygroup");
KafkaConsumer kafkaConsumer = new KafkaConsumer(properties);
List topics = new ArrayList();
kafkaConsumer.subscribe(topics); // Subscribe to "mytopic"
ConsumerRecords records = kafkaConsumer.poll(1); // Get back the next record, blocking until it's available

There are some details here that are specific to Kafka’s jargon. First of all, we have to point our consumer/producer code to the Kafka broker and configure how we want it to transfer data back and forth out of the topic. Then, we have to tell it what sort of data to fetch by specifying a topic.

A topic is essentially the name of the stream we’re reading and writing from. When we produce data to Kafka, we have to specify which topic we’re writing to. Likewise, when we create a consumer, we must subscribe to at least one topic.

Notice we don’t have any commands in this API to modify data. All we can do is push a ProducerRecord and get the data back as ConsumerRecords.

That’s all well and good, but what happens in-between?

It’s All About the Log

Kafka’s basic data structure is the log.

You’re familiar with logs, right? If you want to know what’s happening on a server, you look at the system log. You don’t query a log — you just read it from beginning to end.

And on servers with a lot of log data, the data is often rotated so older logs are discarded, leaving only the recent events you’re most likely interested in.

It’s the same thing with Kafka data. Our stream in Kafka is stored in rotating log files. (Actually, a single topic will be split among a bunch of log files, which depending on how we’ve partitioned the topic.)

So how does our consumer of the data know where it left off?

It simply saves an offset value that represents its place in the stream. Think of this as a bookmark. The offset lets the consumer recover if it shuts down and has to resume reading where it left off.

Now We Have Live Data

Now that we have our data in a live stream, we can perform analysis on it in realtime. The details of what we do next will have to be left for another article.

Suffice to say, once we have the data in a stream, we can now start using a stream processor to transform the data, aggregate it, and even query it.

It’s Functional

As we’ll see is the case in many big-data systems, Kafka uses the functional model of computation in its design.

Note that the data in Kafka is immutable. We never go into Kafka data and modify it. All we can do is insert and read the data. And even then, our reading is limited to sequential access.

Sequential access is cheap because Kafka stores the data together on disk. So it’s able to provide efficient access of blocks of data, even with millions of events being inserted every second.

But wait a minute. If the data is immutable, then how do we update a value in Kafka?

Quite simply, we make another insertion. Kafka has the concept of a key for a message. If we push the same key twice and if we enable a setting called log compaction, then Kafka will ensure older values for the same key are deleted. This is all done automagically by Kafka — we never manually set a value, just push the updated value. We can even push a null value to delete a record.

By avoiding mutable data structures, Kafka allows our streaming system to scale to ridiculous heights.

Why Not Data Warehousing?

On first glance, a stream might seem like a clumsy solution. Couldn’t we just design a database that can handle our write volume — something like a data warehouse — and then query it later when processing?

In some cases, yes, data warehousing is good enough. And for certain types of uses, it might even be preferable. If we know we don’t need our data to be live, then it might be more expensive to maintain the infrastructure for streaming.

The turnaround time for processing our data out of a data warehouse will be slow, delayed by hours or perhaps days, but maybe we’re happy with a daily report. Let’s call these solutions batch processing systems.

Batch processing is more commonly found in extract-transform-load (ETL) systems and in business-intelligence departments.

The Limitations of Batch Processing

There are lots of cases where batch processing isn’t good enough.

Consider the case of a bank processing transactions. We have a stream of transactions coming in, showing us how our customers are using their credit cards in real time.

Now let’s say we want to detect fraudulent transactions in the data. Could we do this with a data-warehousing system?

Probably not. If our query takes hours to run, we won’t know if a transaction was fraudulent until it’s too late.

Where Can I Use This?

Streaming isn’t the solution for every problem. For certain types of problems that are becoming increasingly relevant in the modern world, these are critical concepts, but not everyone is building a realtime system.

But Kafka is useful beyond these niche cases.

Message brokers are crucially important in scaling any system. A common pattern with a service-oriented architecture is to allow services to talk to one another via Kafka, as opposed to HTTP calls. Reading from Kafka is inherently asynchronous, which makes it perfect for a generic messaging tier.

Look into using Kafka or a similar message broker (such as Amazon’s Kinesis) for streaming your data any time you have a large write volume that can be processed asynchronously.

A messaging tier might seem like overkill if you’re a small company, but if you have any intention of growing, it’ll pay dividends to get this solution in place before the growing pains start to hurt.


As we’ve seen, functional-programming concepts have made their way into infrastructure components in the world of big data. Kafka is a prime example of a project that uses a very basic immutable data structure — the log — to great effect.

But these components aren’t just niche systems used in cutting edge machine-learning companies. They’re basic tools that can provide scaling advantages for everyone, from large enterprises to quickly growing startups.