There are a huge number of ML algorithms out there. Trying to classify them leads to the distinction being made in types of the training procedure, applications, the latest advances, and some of the standard algorithms used by ML scientists in their daily work. There is a lot to cover, and we shall proceed as given in the following listing:
1. Statistical Algorithms
Statistics is necessary for every machine learning expert. Hypothesis testing and confidence intervals are some of the many statistical concepts to know if you are a data scientist. Here, we consider here the phenomenon of overfitting. Basically, overfitting occurs when an ML model learns so many features of the training data set that the generalization capacity of the model on the test set takes a toss. The tradeoff between performance and overfitting is well illustrated by the following illustration:
Overfitting – from Wikipedia
Here, the black curve represents the performance of a classifier that has appropriately classified the dataset into two categories. Obviously, training the classifier was stopped at the right time in this instance. The green curve indicates what happens when we allow the training of the classifier to ‘overlearn the features’ in the training set. What happens is that we get an accuracy of 100%, but we lose out on performance on the test set because the test set will have a feature boundary that is usually similar but definitely not the same as the training set. This will result in a high error level when the classifier for the green curve is presented with new data. How can we prevent this?
Cross-Validation is the killer technique used to avoid overfitting. How does it work? A visual representation of the k-fold cross-validation process is given below:
The entire dataset is split into equal subsets and the model is trained on all possible combinations of training and testing subsets that are possible as shown in the image above. Finally, the average of all the models is combined. The advantage of this is that this method eliminates sampling error, prevents overfitting, and accounts for bias. There are further variations of cross-validation like non-exhaustive cross-validation and nested k-fold cross validation (shown above). For more on cross-validation, visit the following link.
There are many more statistical algorithms that a data scientist has to know. Some examples include the chi-squared test, the Student’s t-test, how to calculate confidence intervals, how to interpret p-values, advanced probability theory, and many more. For more, please visit the excellent article given below:
Classification refers to the process of categorizing data input as a member of a target class. An example could be that we can classify customers into low-income, medium-income, and high-income depending upon their spending activity over a financial year. This knowledge can help us tailor the ads shown to them accurately when they come online and maximises the chance of a conversion or a sale. There are various types of classification like binary classification, multi-class classification, and various other variants. It is perhaps the most well known and most common of all data science algorithm categories. The algorithms that can be used for classification include:
Support Vector Machines
Linear Discriminant Analysis
and many more. A short illustration of a binary classification visualization is given below:
For more information on classification algorithms, refer to the following excellent links:
Regression is similar to classification, and many algorithms used are similar (e.g. random forests). The difference is that while classification categorizes a data point, regression predicts a continuous real-number value. So classification works with classes while regression works with real numbers. And yes – many algorithms can be used for both classification and regression. Hence the presence of logistic regression in both lists. Some of the common algorithms used for regression are
Support Vector Regression
Partial Least-Squares Regression
For more on regression, I suggest that you visit the following link for an excellent article:
Both articles have a remarkably clear discussion of the statistical theory that you need to know to understand regression and apply it to non-linear problems. They also have source code in Python and R that you can use.
Clustering is an unsupervised learning algorithm category that divides the data set into groups depending upon common characteristics or common properties. A good example would be grouping the data set instances into categories automatically, the process being used would be any of several algorithms that we shall soon list. For this reason, clustering is sometimes known as automatic classification. It is also a critical part of exploratory data analysis (EDA). Some of the algorithms commonly used for clustering are:
Hierarchical Clustering – Agglomerative
Hierarchical Clustering – Divisive
K-Nearest Neighbours Clustering
EM (Expectation Maximization) Clustering
Principal Components Analysis Clustering (PCA)
An example of a common clustering problem visualization is given below:
The above visualization clearly contains three clusters.
Another excellent article on clustering refer the link
Dimensionality Reduction is an extremely important tool that should be completely clear and lucid for any serious data scientist. Dimensionality Reduction is also referred to as feature selection or feature extraction. This means that the principal variables of the data set that contains the highest covariance with the output data are extracted and the features/variables that are not important are ignored. It is an essential part of EDA (Exploratory Data Analysis) and is nearly always used in every moderately or highly difficult problem. The advantages of dimensionality reduction are (from Wikipedia):
It reduces the time and storage space required.
Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model.
It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D.
It avoids the curse of dimensionality.
The most commonly used algorithm for dimensionality reduction is Principal Components Analysis or PCA. While this is a linear model, it can be converted to a non-linear model through a kernel trick similar to that used in a Support Vector Machine, in which case the technique is known as Kernel PCA. Thus, the algorithms commonly used are:
Ensembling means combining multiple ML learners together into one pipeline so that the combination of all the weak learners makes an ML application with higher accuracy than each learner taken separately. Intuitively, this makes sense, since the disadvantages of using one model would be offset by combining it with another model that does not suffer from this disadvantage. There are various algorithms used in ensembling machine learning models. The three common techniques usually employed in practice are:
Simple/Weighted Average/Voting: Simplest one, just takes the vote of models in Classification and average in Regression.
Bagging: We train models (same algorithm) in parallel for random sub-samples of data-set with replacement. Eventually, take an average/vote of obtained results.
Boosting: In this models are trained sequentially, where (n)th model uses the output of (n-1)th model and works on the limitation of the previous model, the process stops when result stops improving.
Stacking: We combine two or more than two models using another machine learning algorithm.
(from Amardeep Chauhan on Medium.com)
In all four cases, the combination of the different models ends up having the better performance that one single learner. One particular ensembling technique that has done extremely well on data science competitions on Kaggle is the GBRT model or the Gradient Boosted Regression Tree model.
We include the source code from the scikit-learn module for Gradient Boosted Regression Trees since this is one of the most popular ML models which can be used in competitions like Kaggle, HackerRank, and TopCoder.
In the last decade, there has been a renaissance of sorts within the Machine Learning community worldwide. Since 2002, neural networks research had struck a dead end as the networks of layers would get stuck in local minima in the non-linear hyperspace of the energy landscape of a three layer network. Many thought that neural networks had outlived their usefulness. However, starting with Geoffrey Hinton in 2006, researchers found that adding multiple layers of neurons to a neural network created an energy landscape of such high dimensionality that local minima were statistically shown to be extremely unlikely to occur in practice. Today, in 2019, more than a decade of innovation later, this method of adding addition hidden layers of neurons to a neural network is the classical practice of the field known as deep learning.
Deep Learning has truly taken the computing world by storm and has been applied to nearly every field of computation, with great success. Now with advances in Computer Vision, Image Processing, Reinforcement Learning, and Evolutionary Computation, we have marvellous feats of technology like self-driving cars and self-learning expert systems that perform enormously complex tasks like playing the game of Go (not to be confused with the Go programming language). The main reason these feats are possible is the success of deep learning and reinforcement learning (more on the latter given in the next section below). Some of the important algorithms and applications that data scientists have to be aware of in deep learning are:
Long Short term Memories (LSTMs) for Natural Language Processing
Recurrent Neural Networks (RNNs) for Speech Recognition
Convolutional Neural Networks (CNNs) for Image Processing
Deep Neural Networks (DNNs) for Image Recognition and Classification
Hybrid Architectures for Recommender Systems
Autoencoders (ANNs) for Bioinformatics, Wearables, and Healthcare
Deep Learning Networks typically have millions of neurons and hundreds of millions of connections between neurons. Training such networks is such a computationally intensive task that now companies are turning to the 1) Cloud Computing Systems and 2) Graphical Processing Unit (GPU) Parallel High-Performance Processing Systems for their computational needs. It is now common to find hundreds of GPUs operating in parallel to train ridiculously high dimensional neural networks for amazing applications like dreaming during sleep and computer artistry and artistic creativity pleasing to our aesthetic senses.
Artistic Image Created By A Deep Learning Network. From blog.kadenze.com.
For more on Deep Learning, please visit the following links:
In the recent past and the last three years in particular, reinforcement learning has become remarkably famous for a number of achievements in cognition that were earlier thought to be limited to humans. Basically put, reinforcement learning deals with the ability of a computer to teach itself. We have the idea of a reward vs. penalty approach. The computer is given a scenario and ‘rewarded’ with points for correct behaviour and ‘penalties’ are imposed for wrong behaviour. The computer is provided with a problem formulated as a Markov Decision Process, or MDP. Some basic types of Reinforcement Learning algorithms to be aware of are (some extracts from Wikipedia):
Q-Learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation “model-free”) of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state. Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. “Q” names the function that returns the reward used to provide the reinforcement and can be said to stand for the “quality” of an action taken in a given state.
State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy. This name simply reflects the fact that the main function for updating the Q-value depends on the current state of the agent “S1“, the action the agent chooses “A1“, the reward “R” the agent gets for choosing this action, the state “S2” that the agent enters after taking that action, and finally the next action “A2” the agent choose in its new state. The acronym for the quintuple (st, at, rt, st+1, at+1) is SARSA.
3.Deep Reinforcement Learning
This approach extends reinforcement learning by using a deep neural network and without explicitly designing the state space. The work on learning ATARI games by Google DeepMind increased attention to deep reinforcement learning or end-to-end reinforcement learning. Remarkably, the computer agent DeepMind has achieved levels of skill higher than humans at playing computer games. Even a complex game like DOTA 2 was won by a deep reinforcement learning network based upon DeepMind and OpenAI Gym environments that beat human players 3-2 in a tournament of best of five matches.
For more information, go through the following links:
If reinforcement learning was cutting edge data science, AutoML is bleeding edge data science. AutoML (Automated Machine Learning) is a remarkable project that is open source and available on GitHub at the following link that, remarkably, uses an algorithm and a data analysis approach to construct an end-to-end data science project that does data-preprocessing, algorithm selection,hyperparameter tuning, cross-validation and algorithm optimization to completely automate the ML process into the hands of a computer. Amazingly, what this means is that now computers can handle the ML expertise that was earlier in the hands of a few limited ML practitioners and AI experts.
AutoML has found its way into Google TensorFlow through AutoKeras, Microsoft CNTK, and Google Cloud Platform, Microsoft Azure, and Amazon Web Services (AWS). Currently it is a premiere paid model for even a moderately sized dataset and is free only for tiny datasets. However, one entire process might take one to two or more days to execute completely. But at least, now the computer AI industry has come full circle. We now have computers so complex that they are taking the machine learning process out of the hands of the humans and creating models that are significantly more accurate and faster than the ones created by human beings!
The basic algorithm used by AutoML is Network Architecture Search and its variants, given below:
Network Architecture Search (NAS)
PNAS (Progressive NAS)
ENAS (Efficient NAS)
The functioning of AutoML is given by the following diagram:
If you’ve stayed with me till now, congratulations; you have learnt a lot of information and cutting edge technology that you must read up on, much, much more. You could start with the links in this article, and of course, Google is your best friend as a Machine Learning Practitioner. Enjoy machine learning!
The amount of data produced by humans has exploded to unheard-of levels, with nearly 2.5 quintillion bytes of data created daily. With advances in the Internet of Things and mobile technology, data has become a central interest for most organizations. More importantly than simply collecting it, though, is the real need to properly analyze and interpret the data that is being gathered. Also, most businesses collect data from a variety of sources, and each data stream provides signals that ideally come together to form useful insights. However, getting the most out of your data depends on having the right tools to clean it, prepare it, merge it and analyze it properly.
Here are ten of the best analytics tools your company can take advantage of in 2019, so you can get the most value possible from the data you gather.
What is Big Data?
Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Furthermore, Big Data is nothing but any data which is very big to process and produce insights from it. Also, data being too large does not necessarily mean in terms of size only. There are 3 V’s (Volume, Velocity and Veracity) which mostly qualifies any data as Big Data. The volume deals with those terabytes and petabytes of data which is too large to process quickly. Velocity deals with data moving with high velocity. Continuous streaming data is an example of data with velocity and when data is streaming at a very fast rate may be like 10000 of messages in 1 microsecond. Veracity deals with both structured and unstructured data. Data that is unstructured or time-sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Trending Big Data Tools in 2019
1. Apache Spark
Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.
Spark is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries and streaming. Apart from supporting all these workloads in a respective system, it reduces the management burden of maintaining separate tools.
Apache Spark has the following features.
Speed − Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk. This is possible by reducing the number of reading/write operations to disk. It stores the intermediate processing data in memory.
Supports Multiple languages − Spark provides built-in APIs in Java, Scala, or Python. Therefore, you can write applications in different languages. Spark comes up with 80 high-level operators for interactive querying.
Advanced Analytics − Spark not only supports ‘Map’ and ‘reduce’. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph Algorithms.
2. Apache Kafka
Apache Kafka is a community distributed event streaming platform capable of handling trillions of events a day. Initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log. Since being created and open sourced by LinkedIn in 2011, Kafka has quickly evolved from messaging queue to a full-fledged event streaming platform.
Following are a few benefits of Kafka −
Reliability − Kafka is distributed, partitioned, replicated and fault tolerance
Scalability − Kafka messaging system scales easily without downtime
Durability − Kafka uses Distributed commit log which means messages persists on disk as fast as possible, hence it is durable
Performance − Kafka has high throughput for both publishing and subscribing messages. It maintains stable performance even many TB of messages are stored.
Kafka is very fast and guarantees zero downtime and zero data loss.
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
It provides a high-throughput, low-latency streaming engine as well as support for event-time processing and state management. Flink applications are fault-tolerant in the event of machine failure and support exactly-once semantics. Programs can be written in Java, Scala, Python and SQL and are automatically compiled and optimized into dataflow programs that are executed in a cluster or cloud environment. Flink does not provide its own data storage system, but provides data source and sink connectors to systems such as Amazon Kinesis, Apache Kafka, Alluxio, HDFS, Apache Cassandra, and ElasticSearch.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Following are the few advantages of using Hadoop:
Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores
Hadoop does not rely on hardware to provide fault-tolerance and high availability
You can add or remove the cluster dynamically and Hadoop continues to operate without interruption
Another big advantage of Hadoop is that apart from being open source, it is compatible with all the platforms
The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. Cassandra’s support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
Cassandra has become so popular because of its outstanding technical features. Given below are some of the features of Cassandra:
Elastic Scalability — Cassandra is highly scalable; it allows to add more hardware to accommodate more customers and more data as per requirement
Always on Architecture — Cassandra has no single point of failure and it is continuously available for business-critical applications that cannot afford a failure
Fast linear-scale Performance — Cassandra is linearly scalable, i.e., it increases your throughput as you increase the number of nodes in the cluster. Therefore it maintains a quick response time
Flexible Data Storage — Cassandra accommodates all possible data formats including: structured, semi-structured, and unstructured. It can dynamically accommodate changes to your data structures according to your need
Easy Data Distribution — Cassandra provides the flexibility to distribute data where you need by replicating data across multiple data centers
Transaction Support — Cassandra supports properties like Atomicity, Consistency, Isolation, and Durability (ACID)
Fast Writes — Cassandra was designed to run on cheap commodity hardware. It performs blazingly fast writes and can store hundreds of terabytes of data, without sacrificing the read efficiency
6. Apache Storm
Apache Storm is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. The storm is simple, can be used with any programming language, and is a lot of fun to use!
It has many use cases: real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. The storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant guarantees your data will be processed, and is easy to set up and operate.
RapidMiner is a data science software platform by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.
8. Graph Databases (Neo4J and GraphX)
Graph databases are NoSQL databases which use the graph data model comprised of vertices, which is an entity such as a person, place, object or relevant piece of data and edges, which represent the relationship between two nodes.
They are particularly helpful because they highlight the links and relationships between relevant data similarly to how we do so ourselves.
Even though graph databases are awesome, they’re not enough on their own.
Advanced second-generation NoSQL products like OrientDB, Neo4j are the future. The modern multi-model database provides more functionality and flexibility while being powerful enough to replace traditional DBMSs.
9. Elastic Search
Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.
Following are advantages of using elastic search:
Elasticsearch is over Java, which makes it compatible on almost every platform.
It is real time, in other words, after one second the added document is searchable in this engine.
Also, it is distributed, which makes it easy to scale and integrate into any big organization.
Creating full backups are easy by using the concept of the gateway, which is present in Elasticsearch.
Handling multi-tenancy is very easy in Elasticsearch
Elasticsearch uses JSON objects as responses, which makes it possible to invoke the Elasticsearch server with a large number of different programming languages.
Elasticsearch supports almost every document type except those that do not support text rendering.
Exploring and analyzing big data translates information into insight. However, the massive scale, growth and variety of data are simply too much for traditional databases to handle. For this reason, businesses are turning towards technologies such as Hadoop, Spark and NoSQL databases to meet their rapidly evolving data needs. Tableau works closely with the leaders in this space to support any platform that our customers choose. Tableau lets you find that value in your company’s data and existing investments in those technologies so that your company gets the most out of its data. From manufacturing to marketing, finance to aviation– Tableau helps businesses see and understand Big Data.
Understanding your company’s data is a vital concern. Deploying any of the tools listed above can position your business for long-term success by focusing on areas of achievement and improvement.
Follow this link, if you are looking to learn more about data science online!
Over the last few years, blockchain has been one of the hottest areas of technology development across industries. It’s easy to see why. There seems to be no end to the myriad ways that forward-thinking businesses are finding. Furthermore, they are doing this to adapt the technology to suit a variety of use cases and applications. Much of the development, however, has come in one of two places. One is deep-pocket corporations and crypto-startups.
That means that the latest in blockchain technology is out of reach for businesses in the small and midsize enterprise (SME) sector. This leads to creating something of a digital divide that seems to be widening every day. But, there are a few blockchain projects that promise to democratise the technology for SMEs. Furthermore, this could even do the same for Big Data and analytics, to boot.
In this blog, we will explore the basics of both big data and blockchain. Furthermore, we will analyse the advantages of combining both big data and blockchain. In the end, we will have a look the applications in real-world and wrap up with predictions about blockchain in future!
What is Big Data?
Big data, in general, refers to sets of data that are so large in volume and complexity. Traditional data processing software are not capable of capturing and processing this data within a reasonable amount of time.
These big data sets can include structured, unstructured, and semistructured data, each of which can go through analysis for insights.
How much data actually constitutes “big” is open to debate. But it can typically be in multiples of petabytes — and for the largest projects in the exabytes range.
Often, big data is a combination of the three Vs:
an extreme volume of data
a broad variety of types of data
the velocity at which the data needs processing and analysis
The data that constitutes big data stores can come from sources like web sites, social media, desktop and mobile apps etc. The concept of big data comes components that enable organisations to put the data into practical use. Furthermore, they can solve a number of business problems with this. These include the IT infrastructure to support big data; the analytics applied to the data; technologies needed for big data projects; related skill sets; and the actual use cases that make sense for big data.
What is Block Chain?
The blockchain is a technology that is revolutionising the way the internet works. Some of the main distinguishing points of blockchain technology are:
The technology works by creating a series of data records where each new record resides in a block and has a link to the previous record. The term blockchain is derived from this system of linking blocks of data.
Blockchain technology makes possible a distributed ledger system which makes records more transparent.
It uses cryptography to protect user information, and the distributed ledger system is almost, if not impossible, to hack.
Forms the backbone of cryptocurrency but also has several other applications.
Cryptocurrency exchanges on the blockchain network can be central or a network.
Decentralised cryptocurrency exchanges are virtually impossible to hack because there are multiple nodes supporting the system.
Blockchain technology has made peer to peer sharing of content possible without the need for a middleman platform.
Regardless of what you share via the blockchain network, you retain ownership of your content unless you sell it to someone.
Personal information is highly secure and under protection with private key cryptography.
In a nutshell, the blockchain is a network technology that provides users with a chance to share content or make transactions securely without the need for a middleman or a central governing system.
What are the Blocks?
In very simple terms, a block, which is part of the blockchain, is a data file that records any type of transaction on the network. Data resides permanently on the block and becomes part of the chain and impossible to tamper with. For example, if you buy two bitcoins, the transaction is available in a block along with your private key. The private key is your digital signature and links the transaction to you. It is now forever recorded in one block that on that date, you bought two bitcoins.
If you want to buy something with one bitcoin, you will need to provide your private key. A bitcoin miner will use your key to track the last transaction to you and can verify that you have two bitcoins. When you use one bitcoin, that transaction resides in a new block and linked to your last transaction with a series of characters. In this way, all your transactions are audited on the network.
What are Hashes?
One of the reasons the blockchain is so popular is because the information on it, although distributed, is highly encrypted. Data on the blockchain is under encryption by creating a hash. An algorithm is required to create a hash, and it acts by taking the transaction information and converting it to a series of numbers and letters. Hashes are always of the same length.
On the surface, a hash does not make sense to anyone. This is where miners come in. Miners have the special skill set and the resources to decipher a hash and verify the transaction. Miners get paid in bitcoins that undergo generation every time they deliver a service.
What are the Nodes?
The blockchain and cryptocurrency have become synonymous with being decentralised. Decentralisation forms the entire basis of the transparency and the security of the system. But, even a decentralised system requires a support system to give it some form and structure. This support system comes in the form of nodes.
Nodes are focal points of activity spread all over the blockchain network. It is at nodes that blockchain copies are available, transactions undergo processing, and records are available. Nodes consist of individuals that are connected to the system via their own device. Each cryptocurrency has its own set of nodes to keep track of its coins.
The advantage of blockchain is that it is decentralised — no single person or company controls data entry or its integrity; however, the sanctity of the blockchain is through check continuously happening by every computer on the network. As all points hold the same information, corrupt data at point “A” can’t become part of the chain because it won’t match up with the equivalent data at points “B” and “C”.
With the above in mind, blockchain is immutable — information remains in the same state for as long as the network exists.
Why combine Big Data with Blockchain
Instead of uploading data to a cloud server or storing it in a single location, blockchain breaks everything into small chunks and distributes them across the entire network of computers. It effectively cuts out the middle man. There is no need to engage a third-party to process a transaction. You don’t have to place your trust in a vendor or service provider when you can rely on a decentralized, immutable ledger. Also, everything that occurs on the blockchain is encrypted and it’s possible to prove that data has not been altered. Because of its distributed nature, you can check file signatures across all the ledgers on all the nodes in the network and verify that they haven’t been changed
2. Data Quality
Blockchain provides superior Data Security and Data Quality and, as a consequence, is changing the way people approach Big Data. This can be quite useful, as security remains a primary concern for the Internet of Things (IoT) ecosystems. IoT systems expose a variety of devices and huge amounts of data to security breaches. Blockchain has great potential for blocking hackers and providing security in a number of fields, ranging from banking to healthcare to Smart Cities.
This is one of the main ways in which blockchain sets itself apart from the traditional models of technology that are common today. Blockchain does not require any identity for the network layer itself. This means no name, email, address or any other information is needed to download and start utilizing the technology. This lack of a hard requirement of personal information means that there is no central server storing users’ information, making blockchain technology considerably more secure than a central server which can be breached, putting its users’ sensitive data at risk.
One of the most appealing aspects of blockchain technology is the degree of privacy that it can provide. However, this leads to some confusion about how privacy and transparency can effectively coexist. The transparency of a blockchain stems from the fact that the holdings and transactions of each public address are open to viewing. Using an explorer, with a user’s public address, it is possible to view their holdings and their transactions. This level of transparency has not existed within financial systems before, especially in regards to large businesses, and adds a degree of accountability that has not existed to date.
These days, the trend in business processes is undeniably moving away from slow, manual methods and toward greater automation and centralization. Automating your processes has a number of benefits: completing tasks faster, increasing visibility, standardizing outputs, reducing errors, and lowering costs, just to name a few. Although automation has done a great deal to help companies become more efficient and productive, there’s further change on the horizon. In particular, blockchain workflow automation can help organizations that rely heavily on transactions and document-based processes to take the next step in their digital transformation.
1. Anti Money Laundering
Blockchain technology and its ledger allows for more transparency with regulators improving the reporting process. Furthermore, the shared and immutable ledger allows for unaltered transaction history. Also, the ledger can act as a central hub for data storage to process transactions. It can act with the activity across with risk officers within the financial services companies and regulators.
Improved identity management using encryption-based technology on a decentralized network could be established. Furthermore, digital identity improvements can help financial institutions meet the ever-changing KYC and CDD requirements. Moreover, this can happen simultaneously reducing the costs associated with implementing a robust KYC program. Ultimately, financial crimes and compliance violations could be reduced in the long term.
Blockchain technology is present in every sphere of our lives from banking to healthcare and beyond. Furthermore, cybersecurity is an industry which has a lot to gain by this technology with a scope for more in the future. Also, by removing much of the human element from data storage, blockchains significantly mitigate the risk of human error, which is the largest cause of data breaches. The reason why this technology has high popularity is that you can put any digital asset or transaction into the blockchain, the industry does not matter. Additionally, blockchain technology can prevent any type of data breaches, identity thefts, cyber-attacks or foul play in transactions. Hence, the data remains private and secure.
3. Supply Chain Monitoring
The possibilities for application of Blockchain in Big-Data Supply Chain solutions are present in this KPMG Report. The goods are in addition to the Blockchain and a Mobile App monitors the status of the goods as they are in transportation. Data is available with all parties in “near real-time” according to the report. Among the benefits include verification of Product Labeling Claims and that of Product Origins. And most important is the possibility of ensuring human rights with regard to fair wages etc.
4. Financial AI Systems
In terms of financial transactions, Blockchain is taking off in a major way and is set to become a significant aspect of monetary transactions. There are many other innovative ways wherein Big Data and Blockchain can be synchronous to deliver powerful products in the financial services industry. Auditing can have enhancements in a very thorough manner by Blockchain implementation. Also, the Ernst & Young Report states that the “time for experimentation is now.”
5. Automobile AI Systems
The Automobile industry is entering an altogether new phase of existence as cars are now more in sharing, self-driven and available with a host of sensor and communication technologies. As automobiles become autonomous, the range of options available using Blockchain begins with the complete standardisation of vehicle data which makes up a 100 per cent information automobile market.
6. Medical Records
This is an area where records are crucial and are always reside and scrutinized. When the Big Data systems that power this data-oriented sector is put through a Blockchain system, all records preserve with a clear track record while all migrations and interpretations that have been made to records are maintainable in a transparent manner. Also, systems have been in talks whereby researchers can contribute to mining in return for data at an aggregate level. Google is also developing a Blockchain system towards ensuring the security of health records.
Blockchain technology is just one of the ways to evolve automation and business process management in the future. While Blockchains are still early in the technology life cycle the constant stress tests by wider public adoption will only make the ecosystem more robust by improving on the building blocks already in motion. No doubt that blockchain is promising for data science. But, the truth is that we do not have many blockchain technology systems on an industrial scale. Furthermore, for data scientists, this means that it will take a while for the data treasure that Blockchain technology has to offer.
Follow this link, if you are looking to learn more about data science online!
Now unless you’ve been a hermit or a monk living in total isolation, you will have heard of Amazon Web Services and AWS Big Data. It’s a sign of an emerging global market and the entire world becoming smaller and smaller every day. Why? The current estimate for the cloud computing market in 2020, according to Forbes (a new prediction, highly reliable), is a staggering 411 Billion USD$! Visit the following link to read more and see the statistics for yourself:
To know more, refer to Wikipedia for the following terms by clicking on them, which mark, in order the evolution of cloud computing (I will also provide the basic information to keep this article as self-contained as possible):
This was the beginning of the revolution called cloud computing. Companies and industries across verticals understood that they could let experts manage their software development, deployment, and management for them, leaving them free to focus on their key principle – adding value to their business sector. This was mostly confined to the application level. Follow the heading link for more information, if required.
PaaS began when companies started to understand that they could outsource both software management and operating systems and maintenance of these platforms to other companies that specialized in taking care of them. Basically, this was SaaS taken to the next level of virtualization, on the Internet. Amazon was the pioneer, offering SaaS and PaaS services worldwide from the year 2006. Again the heading link gives information in depth.
After a few years in 2011, the big giants like Microsoft, Google, and a variety of other big names began to realize that this was an industry starting to boom beyond all expectations, as more and more industries spread to the Internet for worldwide visibility. However, Amazon was the market leader by a big margin, since it had a five-year head start on the other tech giants. This led to unprecedented disruption across verticals, as more and more companies transferred their IT requirements to IaaS providers like Amazon, leading to (in some cases) savings of well over 25% and per-employee cost coming down by 30%.
After all, why should companies set up their own servers, data warehouse centres, development centres, maintenance divisions, security divisions, and software and hardware monitoring systems if there are companies that have the world’s best experts in every one of these sectors and fields that will do the job for you at less than 1% of the cost the company would incur if they had to hire staff, train them, monitor them, buy their own hardware, hire staff for that as well – the list goes on-and-on. If you are already a tech giant like, say Oracle, you have everything set up for you already. But suppose you are a startup trying to save every penny – and there and tens of thousands of such startups right now – why do that when you have professionals to do it for you?
There is a story behind how AWS got started in 2006 – I’m giving you a link, so as to not make this article too long:
OK. So now you may be thinking, so this is cloud computing and AWS – but what does it have to do with Big Data Speciality, especially for startups? Let’s answer that question right now.
A startup today has a herculean task ahead of them.
Not only do they have to get noticed in the big booming startup industry, they also have to scale well if their product goes viral and receives a million hits in a day and provide security for their data in case a competitor hires hackers from the Dark Web to take down their site, and also follow up everything they do on social media with a division in their company managing only social media, and maintain all their hardware and software in case of outages. If you are a startup counting every penny you make, how much easier is it for you to outsource all your computing needs (except social media) to an IaaS firm like AWS.
You will be ready for anything that can happen, and nothing will take down your website or service other than your own self. Oh, not to mention saving around 1 million USD$ in cost over the year!If you count nothing but your own social media statistics, every company that goes viral has to manage Big Data! And if your startup disrupts an industry, again, you will be flooded with GET requests, site accesses, purchases, CRM, scaling problems, avoiding downtime, and practically everything a major tech company has to deal with!
Bro, save your grey hairs, and outsource all your IT needs (except social media – that you personally need to do) to Amazon with AWS!
And the Big Data Speciality?
Having laid the groundwork, let’s get to the meat of our article. The AWS certified Big Data Speciality website mentions the following details:
The AWS Certified Big Data – Specialty exam validates technical skills and experience in designing and implementing AWS services to derive value from data. The examination is for individuals who perform complex Big Data analyses and validates an individual’s ability to:
Implement core AWS Big Data services according to basic architecture best practices
Design and maintain Big Data
Leverage tools to automate data analysis
So, what is an AWS Big Data Speciality certified expert? Nothing more than an internationally recognized certification that says that you, as a data scientist can work professionally in AWS and Big Data’s requirements in Data Science.
Please note: the eligibility criteria for an AWS Big Data Speciality Certification is:
Minimum five years hands-on experience in a data analytics field
Background in defining and architecting AWS Big Data services with the ability to explain how they fit in the data life cycle of collection, ingestion, storage, processing, and visualization
Experience in designing a scalable and cost-effective architecture to process data
To put it in layman’s terms, if you, the data scientist, were Priyanka Chopra, getting the AWS Big Data Speciality certification passed successfully is the equivalent of going to Hollywood and working in the USA starring in Quantico!
Suddenly, a whole new world is open at your feet!
But don’t get too excited: unless you already have five years experience with Big Data, there’s a long way to go. But work hard, take one step at a time, don’t look at the goal far ahead but focus on every single day, one day, one task at a time, and in the end you will reach your destination. Persistence, discipline and determination matters. As simple as that.
Five Advantages of an AWS Big Data Speciality
1. Massive Increase in Income as a Certified AWS Big Data Speciality Professional (a long term 5 years plus goal)
Everyone who’s anyone in data science knows that a data scientist in the US earns an average of 100,000 USD$ every year. But what is the average salary of an AWS Big Data Speciality Certified professional? Hold on to your hat’s folks; it’s 160,000 $USD starting salary. And with just two years of additional experience, that salary can cross 250,000 USD$ every year if you are a superstar at your work. Depending upon your performance on the job! Do you still need a push to get into AWS? The following table shows the average starting salaries for specialists in the following Amazon products: (from www.dezyre.com)
Top Paying AWS Skills According to Indeed.com
Elastic MapReduce (EMR)
Key Management Service
2. Wide Ecosystem of Tools, Libraries, and Amazon Products
Amazon Web Services, compared to other Cloud IaaS services, has by far the widest ecosystem of products and tools. As a Big Data specialist, you are free to choose your career path. Do you want to get into AI? Do you have an interest in ES3 (storage system) or HIgh-Performance Serverless computing (AWS Lambda). You get to choose, along with the company you work for. I don’t know about you, but I’m just writing this article and I’mseriouslyexcited!
3. Maximum Demand Among All Cloud Computing jobs
If you manage to clear the certification in AWS, then guess what – AWS certified professionals have by far the maximum market demand! Simply because more than half of all Cloud Computing IaaS companies use AWS. The demand for AWS certifications is the maximum right now. To mention some figures: in 2019, 350,000 professionals will be required for AWS jobs. 60% of cloud computing jobs ask for AWS skills (naturally, considering that it has half the market share).
4. Worldwide Demand In Every Country that Has IT
It’s not just in the US that demand is peaking. There are jobs available in England, France, Australia, Canada, India, China, EU – practically every nation that wants to get into IT will welcome you with open arms if you are an AWS certified professional. And look no further than this site. AWS training will be offered soon, here: on Dimensionless.in. Within the next six months at the latest!
5. Affordable Pricing and Free One Year Tier to Learn AWS
Amazon has always been able to command the lowest prices because of its dominance in the market share. AWS offers you a free 1 year of paid services on its cloud IaaS platform. Completely free for one year. AWS training materials are also less expensive compared to other offerings. The following features are offered free for one single year under Amazon’s AWS free tier system:
The following is a web-scrape of their free-tier offering:
AWS Free Tier One Year Resources Available
There were initially seven pages in the Word document that I scraped from www.aws.com/free. To really have a look, go to the website on the previous link and see for yourself on the following link (much more details in much higher resolution). Please visit this last mentioned link. That alone will show you why AWS is sitting pretty on top of the cloud – literally.
Right now, AWS rules the roost in cloud computing. But there is competition from Microsoft, Google, and IBM. Microsoft Azure has a lot of glitches which costs a lot to fix. Google Cloud Platform is cheaper but has very high technical support charges. A dark horse here: IBM Cloud. Their product has a lot of offerings and a lot of potential. Third only to Google and AWS. If you are working and want to go abroad or have a thirst for achievement, go for AWS. Totally. Finally, good news, all Dimensionless current students and alumni, the languages that AWS is built on has 100% support for Python! (It also supports, Go, Ruby, Java, Node.js, and many more – but Python has 100% support).
Keep coming to this website – expect to see AWS courses here in the near future!
International Data Corp. (IDC) expects worldwide revenue for big data and business analytics (BDA) solutions to reach $260 billion in 2022, with a compound annual growth rate (CAGR) of 11.9%. It values the current market at $166 billion, up 11.7% over 2017.
The industries making the largest investments in big data and business analytics solutions are banking, manufacturing, professional services, and government. At a high level, organizations are turning to Big Data and analytics solutions to navigate the convergence of their physical and digital worlds
In this blog, we will be looking into various Big Data solutions provided by AWS(Amazon Web Services). This will give an idea about different services available on AWS for obtaining Big Data capabilities for their Businesses/Organisations.
Also, if you are looking to learn Big Data, then you will really like this amazing course
What is Big Data?
Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Big Data comprises of 4 important V’s which defines the characteristics of Big Data. Let us discuss these ones before moving to AWS
Volume — The name ‘Big Data’ itself is related to a size which is enormous. Size of data plays a very crucial role in determining value out of data. Also, whether a particular data is Big Data or not, is dependent upon the volume of data. Hence, ‘Volume’ is one of the important characteristic while dealing with ‘Big Data’.
Variety — The next aspect of ‘Big Data’ is its variety. Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data. Nowadays, analysis applications use data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. This variety of unstructured-data poses certain issues for storage, mining and analyzing data.
Velocity — The term ‘velocity’ refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data. Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. Also, the flow of data is massive and continuous.
Variability — This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
If you are looking to learn Big Data online then follow the link here
What is AWS?
AWS comprises of many different cloud computing products and services. The highly profitable Amazon division provides servers, storage, networking, remote computing, email, mobile development and security. Furthermore. AWS can be split into two main products: EC2, Amazon’s virtual machine service and S3, a storage system by Amazon. It is so large and present in the computing world that it’s now at least 10 times the size of its nearest competitor and hosts popular websites like Netflix and Instagram
AWS is split into 12 global regions, each of which has multiple availability zones in which its servers are located. These serviced regions are split in order to allow users to set geographical limits on their services (if they so choose), but also to provide security by diversifying the physical locations in which data is held.
AWS solutions for Big Data
AWS has numerous solutions for all the development and deployment purposes. Also, in the field of Data Science and Big Data, AWS has come up with recent developments in different aspects of Big Data handling. Before jumping to tools, let us understand different aspects in Big Data for which AWS can provide solutions
Data Ingestion Collecting the raw data — transactions, logs, mobile devices and more — is the first challenge many organizations face when dealing with big data. A good big data platform makes this step easier, allowing developers to ingest a wide variety of data — from structured to unstructured — at any speed — from real-time to batch.
Storage of Data Any big data platform needs a secure, scalable, and durable repository to store data prior to or even after processing tasks. Depending on your specific requirements, you may also need temporary stores for data-in-transit
Data Processing This is the step where data transformation happens from its raw state into a consumable format — usually by means of sorting, aggregating, joining and even performing more advanced functions and algorithms. The resulting data sets undergo storage for further processing or made available for consumption via business intelligence and data visualization tools.
Visualisation Big data is all about getting high value, actionable insights from your data assets. Ideally, data is available to stakeholders through self-service business intelligence and agile data visualization tools that allow for fast and easy exploration of datasets. Depending on the type of analytics, end-users may also consume the resulting data in the form of statistical “predictions” — in the case of predictive analytics — or recommended actions — in the case of prescriptive analytics.
AWS tools for Big Data
In the previous sections, we looked at the fields in Big Data where AWS can provide solutions. Additionally, AWS has multiple tools and services in its arsenal to enable customers with the capabilities of Big Data
Let us look at the various solutions provided by AWS for handling different stages involved in handling Big Data
Kinesis Amazon Kinesis Firehose is a fully managed service for delivering real-time streaming data directly to Amazon S3. Kinesis Firehose automatically scales to match the volume and throughput of streaming data and requires no ongoing administration. Kinesis Firehose is configurable to transform streaming data before it’s stored in Amazon S3. Its transformation capabilities include compression, encryption, data batching, and Lambda functions. Kinesis Firehose can compress data before it’s storage in Amazon S3. It currently supports GZIP, ZIP, and SNAPPY compression formats. GZIP is a better choice because it can be used by Amazon Athena, Amazon EMR, and Amazon Redshift. Kinesis Firehose encryption supports Amazon S3 server-side encryption with AWS Key Management Service (AWS KMS) for encrypting delivered data in Amazon S3
Snowball You can use AWS Snowball to securely and efficiently migrate bulk data from on-premises storage platforms and Hadoop clusters to S3 buckets. After you create a job in the AWS Management Console, a Snowball appliance will be automatically shipped to you. After a Snowball arrives, connect it to your local network, install the Snowball client on your on-premises data source, and then use the Snowball client to select and transfer the file directories to the Snowball device. The Snowball client uses AES-256-bit encryption. No encryption keys with the Snowball device the makes data transfer process is highly secure. After the data transfer is complete, the Snowball’s E Ink shipping label will automatically update. Ship the device back to AWS. Upon receipt at AWS, data transfer takes place from the Snowball device to your S3 bucket and stored as S3 objects in their original/native format. Snowball also has an HDFS client, so data migration may happen directly from Hadoop clusters into an S3 bucket in its native format.
Amazon S3 Amazon S3 is secure, highly scalable, durable object storage with millisecond latency for data access. S3 can store any type of data from anywhere — websites and mobile apps, corporate applications, and data from IoT sensors or devices. It can also store and retrieve any amount of data, with unmatched availability, and built from the ground up to deliver 99.999999999% (11 nines) of durability. S3 Select focuses on data read and retrieval, reducing response times up to 400%. S3 provides comprehensive security and compliance capabilities that meet even the most stringent regulatory requirements.
AWS Glue AWS Glue is a fully manageable service that provides a data catalogue to make data in the data lake discoverable. Additionally, it has the ability to do extract, transform, and load (ETL) to prepare data for analysis. Also, the inbuilt data catalogue is like a persistent metadata store for all data assets, making all of the data searchable, and queryable in a single view.
EMR For big data processing using the Spark and Hadoop, Amazon EMR provides a managed service that makes it easy, fast, and cost-effective to process vast amounts data. Furthermore, EMR supports 19 different open-source projects including Hadoop, Spark, and HBase. Also it comes with managed EMR Notebooks for data engineering, data science development, and collaboration. Each project updates in EMR within 30 days of a version release. It ensures you have the latest and greatest from the community, effortlessly.
Redshift For data warehousing, Amazon Redshift provides the ability to run complex, analytic queries against petabytes of structured data. Also, it includes Redshift Spectrum that runs SQL queries directly against Exabytes of structured or unstructured data in S3 without the need for unnecessary data movement. Amazon Redshift is less than a tenth of the cost of traditional solutions. Start small for just $0.25 per hour, and scale out to petabytes of data for $1,000 per terabyte per year.
Amazon QuickSight For dashboards and visualizations, Amazon Quicksight provides you fast, cloud-powered business analytics service. It makes it easy to build stunning visualizations and rich dashboards. Additionally, they can be accessed from any browser or mobile device.
Amazon Web Services provides a fully integrated portfolio of cloud computing services. Furthermore, tt helps you build, secure, and deploy your big data applications. Also, with AWS, there’s no hardware to procure and infrastructure to maintain and scale. Due to this, you can focus your resources on uncovering new insights. With new features added constantly, you’ll always be able to leverage the latest technologies without making long-term investment commitments.
Additionally, if you are interested in learning Big Data and NLP, click here to get started
Furthermore, if you want to read more about data science, you can read our blogs here
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