Data Science was called “The sexiest work of the 21st Century” by the Harvard Review. Data researchers as problematic solvers and analysts identify patterns, notice developments and make fresh findings and often use real-time information, machine learning, and IA. This is where Data Science Course comes into the picture.
There is a strong demand for information researchers and qualified data scientists. Projections from IBM suggest that by 2020 the figure of information researchers will achieve 28%. In the United States alone, there will be 2,7 million positions for all US information experts. In addition, we were provided more access to detailed analyzes by strong software programs.
Dimensionless Tech offers the finest online data science course and big data coaching to meet the requirement, offering extensive course coverage and case studies, completely hands-on-driven meetings with personal attention to each individual. This assessment is a gold mine with invaluable insights. To satisfy the elevated requirement. We only provide internet LIVE instruction for instructors and not instruction in the school.
About Dimensionless Technologies
Dimensionless Technologies is a training firm providing online live training in the sector of data science. Courses include–R&P data science, deep learning, large-scale analysis. It was created in 2014, with the goal of offering quality data science training for an inexpensive cost, by 2 IITians Himanshu Arora & Kushagra Singhania. Dimensionless provides a range of internet Data Science Live lessons. Dimensionless intends to overcome the constraints by giving them the correct skillset with the correct methodology, versatile, adaptable and versatile at the correct moment, which will assist learners to create informed business choices and sail towards a successful profession.
Why Dimensionless Technologies
Experienced Faculty and Industry experts
Data science is a very vast field and hence a comprehensive grasp over this subject requires a lot of effort. With our experienced faculties, we are committed to impart quality and practical knowledge to all the learners. Our faculty through their vast experience (10 plus industry experience) in the data science industry is best suited to show the right path to all students towards their success journey on the path of data science. Our trainer’s boast of their high academic career as well (IITian’s)!
End to End domain-specific projects
We, at Dimensionless, believe that concepts can be learned best when all the theory learned in the classroom can actually be implemented. With our meticulously designed courses and projects, we make sure our students get hands-on the projects ranging from pharma, retail, and insurance domains to banking and financial sector problems! End-to-end projects make sure that students understand the entire problem-solving lifecycle in data science
Up to date and adaptive courses
All our courses have been developed based on the recent trends in data science. We have made sure to include all the industry requirements for data scientists. Courses start from level 0 and assume no prerequisites. Courses make learners traverse from basic introductions to advanced concepts gradually with the constant assistance of our experienced faculties. Courses cover all the concepts to a great depth such that learners are never left wanting for more! Our courses have something or other for everyone whether you are a beginner or a professional.
Dimensionless technologies have all the required hardware setup from running a regression equation to training a deep neural network. Our online-lab provides learners with a platform where they can execute all their projects. A laptop with bare minimum configuration (2GB RAM and Windows 7) is sufficient enough to pave your way into the world of deep learning. Pre-setup environments save a lot of time of learners in installing all the required tools. All the software requirements are loaded right in front of the accelerated learning
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Dimensionless provides classes through live interactive classes on our platform. All the classes are taken live by instructors and are not in any pre-recorded format. Such format enables our learners to keep up their learning in the comfort of their own homes. You don’t need to waste your time and expenses in any travel and can take classes from any location of your preference. Also, after each class, we provide the recorded video of it to all our learners so that they can go through it to clear all their doubts. All trainers are available to post classes to clear the doubts as well
Lifetime access to study materials
Dimensionless provides lifetime access to the learning material provided in the course. Many other course providers provide access only till the time one is continuing with classes. With all the resources available thereafter, learnings for our students will not stop even after they have taken up our entire course
Dimensionless technologies provide placement assistance to all its students. With highly experienced faculties and contacts in the industry, we make sure our students get their data science job and kick start their career. We help in all stages of placement assistance. From resume-building to final interviews, Dimensionless technologies is by your side to help you achieve all your goals
Course completion certificate
Apart from the training, we issue a course completion certificate once the training is complete. The certificate brings credibility to the resume of the learners and will help them in fetching their data science dream jobs
Small batch sizes
We make sure that we have small batch sizes of students. Keeping the batch size small allows us to focus on students individually and impart them a better learning experience. With personalized attention, we make sure students are able to learn as much possible and helps us to clear all their doubts as well
If you want to start a profession in data science, dimensionless systems have the correct classes for you. Not just all key ideas and techniques are covered but they are also implemented and used in real-world company issues.
You can follow this link for our Big Data course! This course will equip you with the exact skills required. Packed with content, this course teaches you all about AWS tools and prepares you for your next ‘Data Engineer’ role
I have just completed my survey of data (from articles, blogs, white papers, university websites, curated tech websites, and research papers all available online) about predictive analytics.
And I have a reason to believe that we are standing on the brink of a revolution that will transform everything we know about data science and predictive analytics.
But before we go there, you need to know: why the hype about predictive analytics? What is predictive analytics?
Let’s cover that first.
Importance of Predictive Analytics
By PhotoMix Ltd
According to Wikipedia:
Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.
Predictive analytics is why every business wants data scientists. Analytics is not just about answering questions, it is also about finding the right questions to answer. The applications for this field are many, nearly every human endeavor can be listed in the excerpt from Wikipedia that follows listing the applications of predictive analytics:
Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, mobility, healthcare, child protection, pharmaceuticals, capacity planning, social networking, and a multitude of numerous other fields ranging from the military to online shopping websites, Internet of Things (IoT), and advertising.
In a very real sense, predictive analytics means applying data science models to given scenarios that forecast or generate a score of the likelihood of an event occurring. The data generated today is so voluminous that experts estimate that less than 1% is actually used for analysis, optimization, and prediction. In the case of Big Data, that estimate falls to 0.01% or less.
Common Example Use-Cases of Predictive Analytics
Components of Predictive Analytics
A skilled data scientist can utilize the prediction scores to optimize and improve the profit margin of a business or a company by a massive amount. For example:
If you buy a book for children on the Amazon website, the website identifies that you have an interest in that author and that genre and shows you more books similar to the one you just browsed or purchased.
YouTube also has a very similar algorithm behind its video suggestions when you view a particular video. The site identifies (or rather, the analytics algorithms running on the site identifies) more videos that you would enjoy watching based upon what you are watching now. In ML, this is called a recommender system.
Netflix is another famous example where recommender systems play a massive role in the suggestions for ‘shows you may like’ section, and the recommendations are well-known for their accuracy in most cases
Google AdWords (text ads at the top of every Google Search) that are displayed is another example of a machine learning algorithm whose usage can be classified under predictive analytics.
Departmental stores often optimize products so that common groups are easy to find. For example, the fresh fruits and vegetables would be close to the health foods supplements and diet control foods that weight-watchers commonly use. Coffee/tea/milk and biscuits/rusks make another possible grouping. You might think this is trivial, but department stores have recorded up to 20% increase in sales when such optimal grouping and placement was performed – again, through a form of analytics.
Bank loans and home loans are often approved with the credit scores of a customer. How is that calculated? An expert system of rules, classification, and extrapolation of existing patterns – you guessed it – using predictive analytics.
Allocating budgets in a company to maximize the total profit in the upcoming year is predictive analytics. This is simple at a startup, but imagine the situation in a company like Google, with thousands of departments and employees, all clamoring for funding. Predictive Analytics is the way to go in this case as well.
IoT (Internet of Things) smart devices are one of the most promising applications of predictive analytics. It will not be too long before the sensor data from aircraft parts use predictive analytics to tell its operators that it has a high likelihood of failure. Ditto for cars, refrigerators, military equipment, military infrastructure and aircraft, anything that uses IoT (which is nearly every embedded processing device available in the 21st century).
Fraud detection, malware detection, hacker intrusion detection, cryptocurrency hacking, and cryptocurrency theft are all ideal use cases for predictive analytics. In this case, the ML system detects anomalous behavior on an interface used by the hackers and cybercriminals to identify when a theft or a fraud is taking place, has taken place, or will take place in the future. Obviously, this is a dream come true for law enforcement agencies.
So now you know what predictive analytics is and what it can do. Now let’s come to the revolutionary new technology.
End-to-End Predictive Analytics Product – for non-tech users!
In a remarkable first, a research team at MIT, USA have created a new science called social physics, or sociophysics. Now, much about this field is deliberately kept highly confidential, because of its massive disruptive power as far as data science is concerned, especially predictive analytics. The only requirement of this science is that the system being modeled has to be a human-interaction based environment. To keep the discussion simple, we shall explain the entire system in points.
All systems in which human beings are involved follow scientific laws.
These laws have been identified, verified experimentally and derived scientifically.
Bylaws we mean equations, such as (just an example) Newton’s second law: F = m.a (Force equals mass times acceleration)
These equations establish laws of invariance – that are the same regardless of which human-interaction system is being modeled.
Hence the term social physics – like Maxwell’s laws of electromagnetism or Newton’s theory of gravitation, these laws are a new discovery that are universal as long as the agents interacting in the system are humans.
The invariance and universality of these laws have two important consequences:
The need for large amounts of data disappears – Because of the laws, many of the predictive capacities of the model can be obtained with a minimal amount of data. Hence small companies now have the power to use analytics that was mostly used by the FAMGA(Facebook, Amazon, Microsoft, Google, Apple) set of companies since they were the only ones with the money to maintain Big Data warehouses and data lakes.
There is no need for data cleaning. Since the model being used is canonical, it is independent of data problems like outliers, missing data, nonsense data, unavailable data, and data corruption. This is due to the orthogonality of the model ( a Knowledge Sphere) being constructed and the data available.
Performance is superior to deep learning, Google TensorFlow, Python, R, Julia, PyTorch, and scikit-learn. Consistently, the model has outscored the latter models in Kaggle competitions, without any data pre-processing or data preparation and cleansing!
Data being orthogonal to interpretation and manipulation means that encrypted data can be used as-is. There is no need to decrypt encrypted data to perform a data science task or experiment. This is significant because the independence of the model functioning even for encrypted data opens the door to blockchain technology and blockchain data to be used in standard data science tasks. Furthermore, this allows hashing techniques to be used to hide confidential data and perform the data mining task without any knowledge of what the data indicates.
Are You Serious?
That’s a valid question given these claims! And that is why I recommend everyone who has the slightest or smallest interest in data science to visit and completely read and explore the following links:
Now when I say completely read, I mean completely read. Visit every section and read every bit of text that is available on the three sites above. You will soon understand why this is such a revolutionary idea.
These links above are articles about the social physics book and about the science of sociophysics in general.
For more details, please visit the following articles on Medium. These further document Endor.coin, a cryptocurrency built around the idea of sharing data with the public and getting paid for using the system and usage of your data. Preferably, read all, if busy, at least read Article No, 1.
Upon every data set, the first action performed by the Endor Analytics Platform is clustering, also popularly known as automatic classification. Endor constructs what is known as a Knowledge Sphere, a canonical representation of the data set which can be constructed even with 10% of the data volume needed for the same project when deep learning was used.
Creation of the Knowledge Sphere takes 1-4 hours for a billion records dataset (which is pretty standard these days).
Now an explanation of the mathematics behind social physics is beyond our scope, but I will include the change in the data science process when the Endor platform was compared to a deep learning system built to solve the same problem the traditional way (with a 6-figure salary expert data scientist).
From Appendix A: Social Physics Explained, Section 3.1, pages 28-34 (some material not included):
Prediction Demonstration using the Endor System:
Data: The data that was used in this example originated from a retail financial investment platform and contained the entire investment transactions of members of an investment community. The data was anonymized and made public for research purposes at MIT (the data can be shared upon request).
Summary of the dataset: – 7 days of data – 3,719,023 rows – 178,266 unique users
Automatic Clusters Extraction: Upon first analysis of the data the Endor system detects and extracts “behavioral clusters” – groups of users whose data dynamics violates the mathematical invariances of the Social Physics. These clusters are based on all the columns of the data, but is limited only to the last 7 days – as this is the data that was provided to the system as input.
Behavioural Clusters Summary
Number of clusters:268,218 Clusters sizes: 62 (Mean), 15 (Median), 52508 (Max), 5 (Min) Clusters per user:164 (Mean), 118 (Median), 703 (Max), 2 (Min) Users in clusters: 102,770 out of the 178,266 users Records per user: 6 (Median), 33 (Mean): applies only to users in clusters
Prediction Queries The following prediction queries were defined: 1. New users to become “whales”: users who joined in the last 2 weeks that will generate at least $500 in commission in the next 90 days 2. Reducing activity : users who were active in the last week that will reduce activity by 50% in the next 30 days (but will not churn, and will still continue trading) 3. Churn in “whales”: currently active “whales” (as defined by their activity during the last 90 days), who were active in the past week, to become inactive for the next 30 days 4. Will trade in Apple share for the first time: users who had never invested in Apple share, and would buy it for the first time in the coming 30 days
Knowledge Sphere Manifestation of Queries It is again important to note that the definition of the search queries is completely orthogonal to the extraction of behavioral clusters and the generation of the Knowledge Sphere, which was done independently of the queries definition.
Therefore, it is interesting to analyze the manifestation of the queries in the clusters detected by the system: Do the clusters contain information that is relevant to the definition of the queries, despite the fact that:
1. The clusters were extracted in a fully automatic way, using no semantic information about the data, and –
2. The queries were defined after the clusters were extracted, and did not affect this process.
This analysis is done by measuring the number of clusters that contain a very high concentration of “samples”; In other words, by looking for clusters that contain “many more examples than statistically expected”.
A high number of such clusters (provided that it is significantly higher than the amount received when randomly sampling the same population) proves the ability of this process to extract valuable relevant semantic insights in a fully automatic way.
Comparison to Google TensorFlow
In this section a comparison between prediction process of the Endor system and Google’s TensorFlow is presented. It is important to note that TensorFlow, like any other Deep Learning library, faces some difficulties when dealing with data similar to the one under discussion:
1. An extremely uneven distribution of the number of records per user requires some canonization of the data, which in turn requires:
2. Some manual work, done by an individual who has at least some understanding of data science.
3. Some understanding of the semantics of the data, that requires an investment of time, as well as access to the owner or provider of the data
4. A single-class classification, using an extremely uneven distribution of positive vs. negative samples, tends to lead to the overfitting of the results and require some non-trivial maneuvering.
This again necessitates the involvement of an expert in Deep Learning (unlike the Endor system which can be used by Business, Product or Marketing experts, with no perquisites in Machine Learning or Data Science).
An expert in Deep Learning spent 2 weeks crafting a solution that would be based on TensorFlow and has sufficient expertise to be able to handle the data. The solution that was created used the following auxiliary techniques:
1.Trimming the data sequence to 200 records per customer, and padding the streams for users who have less than 200 records with neutral records.
2.Creating 200 training sets, each having 1,000 customers (50% known positive labels, 50% unknown) and then using these training sets to train the model.
3.Using sequence classification (RNN with 128 LSTMs) with 2 output neurons (positive, negative), with the overall result being the difference between the scores of the two.
Observations (all statistics available in the white paper – and it’s stunning)
1.Endor outperforms Tensor Flow in 3 out of 4 queries, and results in the same accuracy in the 4th . 2.The superiority of Endor is increasingly evident as the task becomes “more difficult” – focusing on the top-100 rather than the top-500.
3.There is a clear distinction between “less dynamic queries” (becoming a whale, churn, reduce activity” – for which static signals should likely be easier to detect) than the “Who will trade in Apple for the first time” query, which are (a) more dynamic, and (b) have a very low baseline, such that for the latter, Endor is 10x times more accurate!
4.As previously mentioned – the Tensor Flow results illustrated here employ 2 weeks of manual improvements done by a Deep Learning expert, whereas the Endor results are 100% automatic and the entire prediction process in Endor took 4 hours.
Clearly, the path going forward for predictive analytics and data science is Endor, Endor, and Endor again!
Predictions for the Future
Personally, one thing has me sold – the robustness of the Endor system to handle noise and missing data. Earlier, this was the biggest bane of the data scientist in most companies (when data engineers are not available). 90% of the time of a professional data scientist would go into data cleaning and data preprocessing since our ML models were acutely sensitive to noise. This is the first solution that has eliminated this ‘grunt’ level work from data science completely.
The second prediction: the Endor system works upon principles of human interaction dynamics. My intuition tells me that data collected at random has its own dynamical systems that appear clearly to experts in complexity theory. I am completely certain that just as this tool developed a prediction tool with human society dynamical laws, data collected in general has its own laws of invariance. And the first person to identify these laws and build another Endor-style platform on them will be at the top of the data science pyramid – the alpha unicorn.
Final prediction – democratizing data science means that now data scientists are not required to have six-figure salaries. The success of the Endor platform means that anyone can perform advanced data science without resorting to TensorFlow, Python, R, Anaconda, etc. This platform will completely disrupt the entire data science technological sector. The first people to master it and build upon it to formalize the rules of invariance in the case of general data dynamics will for sure make a killing.
It is an exciting time to be a data science researcher!
Data Science is a broad field and it would require quite a few things to learn to master all these skills.
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!
So you want to learn data science but you don’t know where to start? Or you are a beginner and you want to learn the basic concepts? Welcome to your new career and your new life! You will discover a lot of things on your journey to becoming a data scientist and being part of a new revolution. I am a firm believer that you can learn data science and become a data scientist regardless of your age, your background, your current knowledge level, your gender, and your current position in life. I believe – from experience – that anyone can learn anything at any stage in their lives. What is required is just determination, persistence, and a tireless commitment to hard work. Nothing else matters as far as learning new things – or learning data science – is concerned. Your commitment, persistence, and your investment in your available daily time is enough.
I hope you understood my statement. Anyone can learn data science if you have the right motivation. In fact, I believe anyone can learn anything at any stage in their lives, if they invest enough time, effort and hard work into it, along with your current occupation. From my experience, I strongly recommend that you continue your day job and work on data science as a side hustle, because of the hard work that will be involved. Your commitment is more important than your current life situation. Carrying on a full-time job and working on data science part-time is the best way to go if you want to learn in the best possible manner.
Technical Concepts of Data Science
So what are the important concepts of data science that you should know as a beginner? They are, in order of sequential learning, the following:
Statistics & Probability
Data Preparation and Data ETL*
Machine Learning with Python and R
Data Visualization and Summary
*Extraction, Transformation, and Loading
Now if you were to look at the above list an go to a library, you would, most likely, come back with 9-10 books at an average of 1000 pages each. Even if you could speed-read, 10,000 pages is a lot to get through. I could list the best books for each topic in this post, but even the most seasoned reader would balk at 10,000 pages. And who reads books these days? So what I am going to give you is a distilled extract on each of those topics. Keep in mind, however, that every topic given above could be a series of blog posts in its own right, and these 80-word paragraphs are just a tiny taste of each topic and there is an ocean of depth involved in every topic. You might ask if that is the case, how can everybody be a possible candidate for data scientist role? Two words: Persistence and Motivation. With the right amount of these two characteristics, anyone can be anything they want to be.
1) Python Programming:
Python is one of the most popular programming languages in the world. It is the ABC of data science because Python is the language every beginner starts with on data science. It is universally used for any purposes since it is so amazingly versatile. Python can be used for web applications and websites with Django, microservices with Flask, general programming projects with the standard library from PyPI, GUIs with PyQt5 or Tkinter, Interoperability with Jython (Java), Cython (C) and nearly other programming language are available today.
Of course, Python is the also first language used for data science with the standard stack of scikit-learn (machine learning), pandas (data manipulation), matplotlib and seaborn (visualization) and numpy (vectorized computation). Nowadays, the most common technology used is the Anaconda distribution, available from www.anaconda.com. Current version 2018.12 or Anaconda Distribution 5. To learn more about Python, I strongly recommend the following books: Head First Python and the Python Cookbook.
2) R Programming
R is The Best Language for statistical needs since it is a language designed by statisticians, for statisticians. If you know statistics and mathematics well, you will enjoy programming in R. The language gives you the best support available for every probability distribution, statistics functions, mathematical functions, plotting, visualization, interoperability, and even machine learning and AI. In fact, everything that you can do in Python can be done in R. R is the second most popular language for data science in the world, second only to Python. R has a rich ecosystem for every data science requirement and is the favorite language of academicians and researchers in the academic domain.
Learning Python is not enough to be a professional data scientist. You need to know R as well. A good book to start with is R For Data Science, available at Amazon at a very reasonable price. Some of the most popular packages in R that you need to know are ggplot2, ThreeJS, DT (tables), network3D, and leaflet for visualization, dplyr and tidyr for data manipulation, shiny and R Markdown for reporting, parallel, Rcpp and data.table for high performance computing and caret, glmnet, and randomForest for machine learning.
3) Statistics and Probability
This is the bread and butter of every data scientist. The best programming skills in the world will be useless without knowledge of statistics. You need to master statistics, especially practical knowledge as used in a scientific experimental analysis. There is a lot to cover. Any subtopic given below can be a blog-post in its own right. Some of the more important areas that a data scientist needs to master are:
Succinctly, linear algebra is about vectors, matrices and the operations that can be performed on vectors and matrices. This is a fundamental area for data science since every operation we do as a data scientist has a linear algebra background, or, as data scientists, we usually work with collections of vectors or matrices. So we have the following topics in Linear Algebra, all of which are covered in the following world-famous book, Linear Algebra and its Applications by Gilbert Strang, an MIT professor. You can also go to the popular MIT OpenCourseWare page, Linear Algebra (MIT OCW). These two resources cover everything you need to know. Some of the most fundamental concepts that you can also Google or bring up on Wikipedia are:
5) Data Preparation and Data ETL (Extraction, Transformation, and Loading)
By IAmMrRob on Pixabay
Yes – welcome to one of the more infamous sides of data science! If data science has a dark side, this is it. Know for sure that unless your company has some dedicated data engineers who do all the data munging and data wrangling for you, 90% of your time on the job will be spent on working with raw data. Real world data has major problems. Usually, it’s unstructured, in the wrong formats, poorly organized, contains many missing values, contains many invalid values, and contains types that are not suitable for data mining.
Dealing with this problem takes up a lot of the time of a data scientist. And your data scientist’s analysis has the potential to go massively wrong when there is invalid and missing data. Practically speaking, unless you are unusually blessed, you will have to manage your own data, and that means conducting your own ETL (Extraction, Transformation, and Loading). ETL is a data mining and data warehousing term that means loading data from an external data store or data mart into a form suitable for data mining and in a state suitable for data analysis (which usually involves a lot of data preprocessing). Finally, you often have to load data that is too big for your working memory – a problem referred to as external loading. During your data wrangling phase, be sure to look into the following components:
Automating the Data ETL Pipeline
Automation of Data Validation and Verification
Usually, expert data scientists try to automate this process as much as possible, since a human being would be wearied by this task very fast and is remarkably prone to errors, which will not happen in the case of a Python or an R script doing the same operations. Be sure to try to automate every stage in your data processing pipeline.
6) Machine Learning with Python and R
An expert machine learning scientist has to be proficient in the following areas at the very least:
Data Science Topics Listing – Thomas
Now if you are just starting out in Machine Learning (ML), Python, and R, you will gain a sense of how huge the field is and the entire set of lists above might seem more like advanced Greek instead of Plain Jane English. But not to worry; there are ways to streamline your learning and to consume as little time as possible in learning or becoming able to learn nearly every single topic given above. After you learn the basics of Python and R, you need to go on to start building machine learning models. From experience, I suggest you break up your time into 50% of Python and 50% of R and spend as much time as possible spending time without switching your languages or working between languages. What do I mean? Spend maximum time learning one programming language at one time. That will prevent syntax errors and conceptual errors and language confusion problems.
Now, on the job, in real life, it is much more likely that you will work in a team and be responsible for only one part of the work. However, if your working in a startup or learning initially, you will end up doing every phase of the work yourself. Be sure to give yourself time to process information and to spend sufficient time for your brain to rest and get a handle on the topics you are trying to learn. For more info, do check out the Learning How to Learn MOOC on Coursera, which is the best way to learn mathematical or scientific topics without ending up with burn out. In fact, I would recommend this approach to every programmer out there trying to learn a programming language, or anything considered difficult, like Quantum Mechanics and Quantum Computation or String Theory, or even Microsoft F# or Microsoft C# for a non-Java programmer.
Common tools that you have with which you can produce powerful visualizations include:
Google Data Studio
Microsoft Power BI Desktop
Some involve coding, some are drag-and-drop, some are difficult for beginners, some have no coding at all. All of these tools will help you with data visualization. But one of the most overlooked but critical practical functions of a data scientist has been included under this heading: summarisation.
Summarisation means the practical result of your data science workflow. What does the result of your analysis mean for the operation of the business or the research problem that you are currently working on? How do you convert your result to the maximum improvement for your business? Can you measure the impact this result will have on the profit of your enterprise? If so, how? Being able to come out of a data science workflow with this result is one of the most important capacities of a data scientist. And most of the time, efficient summarisation = excellent knowledge of statistics. Please know for sure that statistics is the start and the end of every data science workflow. And you cannot afford to be ignorant about it. Refer to the section on statistics or google the term for extra sources of information.
How Can I Learn Everything Above In the Shortest Possible Time?
You might wonder – How can I learn everything given above? Is there a course ora pathway to learn every single concept described in this article at one shot? It turns out – there is. There is a dream course for a data scientist that contains nearly everything talked about in this article.
Want to Become a Data Scientist? Welcome to Dimensionless Technologies! It just so happens that the course: Data Science using Python and R, a ten-week course that includes ML, Python and R programming, Statistics, Github Account Project Guidance, and Job Placement, offers nearly every component spoken about above, and more besides. You don’t know to buy the books or do any of the courses other than this to learn the topics in this article. Everything is covered by this single course, tailormade to convert you to a data scientist within the shortest possible time. For more, I’d like to refer you to the following link:
Does this seem too good to be true? Perhaps, because this is a paid course. With a scholarship concession, you could end up paying around INR 40,000 for this ten-week course, two weeks of which you can register for 5,000 and pay the remainder after two weeks trial period to see if this course really suits you. If it doesn’t, you can always drop out after two weeks and be poorer by just 5k. But in most cases, this course has been found to carry genuine worth. And nothing worthwhile was achieved without some payment, right?
In case you want to learn more about data science, please check out the following articles:
Europe has more than 307 million people on Facebook
There are five new Facebook profiles created every second!
More than 300 million photos get uploaded per day
Every minute there are 510,000 comments posted and 293,000 statuses updated (on Facebook)
And all this data was gathered 21st May, last year!
Photo by rawpixel on Unsplash
So I decided to do a more up to date survey. The data below was from an article written on 25th Jan 2019, given at the following link:
By 2020, the accumulated volume of big data will increase from 4.4 zettabytes to roughly 44 zettabytes or 44 trillion GB.
Originally, data scientists maintained that the volume of data would double every two years thus reaching the 40 ZB point by 2020. That number was later bumped to 44ZB when the impact of IoT was brought into consideration.
The rate at which data is created is increased exponentially. For instance, 40,000 search queries are performed per second (on Google alone), which makes it 3.46 million searches per day and 1.2 trillion every year.
Freshers in Analytics get paid more than then any other field, they can be paid up-to 6-7 Lakhs per annum (LPA) minus any experience, 3-7 years experienced professional can expect around 10-11 LPA and anyone with more than 7-10 years can expect, 20-30 LPA.
Opportunities in tier 2 cities can be higher, but the pay-scale of Tier 1 cities is much higher.
E-commerce is the most rewarding career with great pay-scale especially for Fresher’s, offering close to 7-8 LPA, while Analytics service provider offers the lowest packages, 6 LPA.
It is advised to combine your skills to attract better packages, skills such as SAS, R Python, or any open source tools, offers around 13 LPA.
Machine Learning is the new entrant in analytics field, attracting better packages when compared to the skills of big data, however for a significant leverage, acquiring the skill sets of both Big Data and Machine Learning will fetch you a starting salary of around 13 LPA.
Combination of knowledge and skills makes you unique in the job market and hence attracts high pay packages.
Picking up the top five tools of big data analytics, like R, Python, SAS, Tableau, Spark along with popular Machine Learning Algorithms, NoSQL Databases, Data Visualization, will make you irresistible for any talent hunter, where you can demand a high pay package.
As a professional, you can upscale your salary by upskilling in the analytics field.
So there is no doubt about the demand or the need for data scientists in the 21st century.
Now we have done a survey for India. but what about the USA?
The following data is an excerpt from an article by IBM< which tells the story much better than I ever could:
Jobs requiring machine learning skills are paying an average of $114,000.
Advertised data scientist jobs pay an average of $105,000 and advertised data engineering jobs pay an average of $117,000.59% of all Data Science and Analytics (DSA) job demand is in Finance and Insurance, Professional Services, and IT.
Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020.
By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.
Data Science and Analytics (DSA) jobs remain open an average of 45 days, five days longer than the market average.
And yet still more! Look below:
By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000 The following is the summary of the study that highlights how in-demand data science and analytics skill sets are today and are projected to be through 2020.
There were 2,350,000 DSA job listings in 2015
By 2020, DSA jobs are projected to grow by 15%
Demand for Data scientists and data engineers is projectedto grow byneary40%
DSA jobs advertise average salaries of 80,265 USD$
81% of DSA jobs require workers with 3-5 years of experience or more.
Machine learning, big data, and data science skills are the most challenging to recruit for and potentially can create the greatest disruption to ongoing product development and go-to-market strategies if not filled.
So where does Dimensionless Technologies, with courses in Python, R, Deep Learning, NLP, Big Data, Analytics, and AWS coming soon, stand in the middle of all the demand?
The answer: right in the epicentre of the data science earthquake that is no hitting our IT sector harder than ever.The main reason I say this is because of the salaries increasing like your tummy after you finish your fifth Domino’s Dominator Cheese and Pepperoni Pizza in a row everyday for seven days! Have a look at the salaries for data science:
Do you know which city in India pays highest salaries to data scientist?
Mumbai pays the highest salary in India around 12.19L p.a.
Report of Data Analytics Salary of the Top Companies in India
Accenture’s Data Analytics Salary in India: 90% gets a salary of about Rs 980,000 per year
Tata Consultancy Services Limited Data Analytics Salary in India: 90% of the employees get a salary of about Rs 550,000 per year. A bonus of Rs 20,000 is paid to the employees.
EY (Ernst & Young) Data Analytics Salary in India: 75% of the employees get a salary of Rs 620,000 and 90% of the employees get a salary of Rs 770,000.
HCL Technologies Ltd. Data Analytics Salary in India: 90% of the people are paid Rs 940,000 per year approximately.
In the USA
To convert into INR, in the US, the salaries of a data scientist stack up as follows:
Lowest: 86,000 USD = 6,020,000 INR per year (60 lakh per year)
Average: 117,00 USD = 8,190,000 INR per year (81 lakh per year)
Highest: 157,000 USD = 10,990,000 INR per year(109 lakh per year or approximately one crore)
at the exchange rate of 70 INR = 1 USD.
By now you should be able to understand why everyone is running after data science degrees and data science certifications everywhere.
The only other industry that offers similar salaries is cloud computing.
A Personal View
On my own personal behalf, I often wondered – why does everyone talk about following your passion and not just about the money. The literature everywhere advertises“Follow your heart and it will lead you to the land of your dreams”. But then I realized – passion is more than your dreams. A dream, if it does not serve others in some way, is of no inspirational value. That is when I found the fundamental role – focus on others achieving their hearts desires, and you will automatically discover your passion. I have many interests, and I found my happiness doing research in advanced data science and quantum computing and dynamical systems, focusing on experiments that combine all three of them together as a single unified theory. I found that that was my dream. But, however, I have a family and I need to serve them. I need to earn.
Thus I relegated my dreams of research to a part-time level and focused fully on earning for my extended family, and serving them as best as I can. Maybe you will come to your own epiphany moment yourself reading this article. What do you want to do with your life? Personally, I wish to improve the lives of those around me, especially the poor and the malnourished. That feeds my heart. Hence my career decision – invest wisely in the choices that I make to garner maximum benefit for those around me. And work on my research papers in the free time that I get.
So my hope for you today is: having read this article, understand the rich potential that lies before you if you can complete your journey as a data scientist. The only reason that I am not going into data science myself is that I am 34 years old and no longer in the prime of my life to follow this American dream. Hence I found my niche in my interest in research. And further, I realized that a fundamental ‘quantum leap’ would be made if my efforts were to succeed. But as for you, the reader of this article, you may be inspired or your world-view expanded by reading this article and the data contained within. My advice to you is: follow your heart. It knows you best and will not betray you into any false location. Data science is the future for the world. make no mistake about that. And – from whatever inspiration you have received go forward boldly and take action. Take one day at a time. Don’t look at the final goal. Take one day at a time. If you can do that, you will definitely achieve your goals.
The salary at the top, per year. From glassdoor.com. Try not to drool. 🙂
Finding Your Passion
Many times when you’re sure you’ve discovered your passion and you run into a difficult topic, that leaves you stuck, you are prone to the famous impostor syndrome. “Maybe this is too much for me. Maybe this is too difficult for me. Maybe this is not my passion. Otherwise, it wouldn’t be this hard for me.” My dear friend, this will hit you. At one point or the other. At such moments, what I do, based upon lessons from the following course, which I highly recommend to every human being on the planet, is: Take a break. Do something different that completely removes the mind from your current work. Be completely immersed in something else. Or take a nap. Or – best of all – go for a run or a cycle. Exercise. Workout. This gives your brain cells rest and allows them to process the data in the background. When you come back to your topic, fresh, completely free of worry and tension, completely recharged, you will have an insight into the problem for you that completely solves it. Guaranteed. For more information, I highly suggest the following two resources:
or the most popular MOOC of all time, based on the same topic: Coursera
Learning How to Learn – Coursera and IEEE
This should be your action every time you feel stuck. I have completely finished this MOOC and the book and it has given me the confidence to tackle any subject in the world, including quantum mechanics, topology, string theory, and supersymmetry theory. I strongly recommend this resource (from experience).
So Dimensionless Technologies (link given above) is your entry point to all things data science. Before you go to TensorFlow, Hadoop, Keras, Hive, Pig, MapReduce, BigQuery, BigTable, you need to know the following topics first:
All the best. Your passion is not just a feeling. It is a choice you make the day in and a day out whether you like it or not. That is the definition of character – to do what must be done even if you don’t feel like it. Internalize this advice, and there will be no limits to how high you can go.All the best!
Modern technologies such as artificial intelligence, machine learning, data science, and Big Data have become the phrases everyone talks about, but no one fully understands them. To a layman, they seem very complex. All these words resemble a business executive or a student from a non-technical background. People are often confused by words such as AI, ML, and data science.
People are often confused about using technology for growing their business. With a plethora of technologies available and rise and shine of data science in recent times, the decision makes individuals & companies face the consent dilemma of whether to choose big data or ML or data science which can boost their businesses. In this blog, we will understand different concepts and have a look at this problem.
Let us understand key terms first i.e data science, machine learning, and big data
What is Data Science
Data science is the umbrella under which all these terminologies take the shelter. Data science is a like a complete subject which has different stages within itself. Suppose a retailer wants to forecast the sales of an X item present in its inventory in the coming month. This is a business problem and data science aims to provide optimal solutions for the same.
Data science enables us to solve this business problem with a series of well-defined steps.
Driving insights and generating BI report
Taking insight-bases decisions
Generally, these are the steps we mostly follow to solve a business problem. All the terminologies related to data science falls under different steps which we are going to understand just in a while. Different terminologies fall under different steps listed above.
You can learn more about the different component in data science from here
If you want to learn data science online then follow the link here
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.
Characteristics Of ‘Big Data’
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 can actually be considered as a Big Data or not, is dependent upon the volume of data. Hence, ‘Volume’ is one characteristic which needs to be considered 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. 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 Machine Learning
At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data.
Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is a spam, or recognizing speech accurately enough to generate captions for a YouTube video.
The key difference from traditional computer software is that a human developer hasn’t written code that instructs the system how to tell the difference between the banana and the apple.
Instead, a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data, in this instance likely a huge number of images labelled as containing a banana or an apple.
You can read more on how to be an expert in AI from here
The relationship between Data Science, Machine learning and Big Data
Data science is a complete journey of solving a problem using data at hand wheres Big data and machine learning are tools for the data scientists. It helps them to perform some specific tasks. While, Machine learning is more around making predictions using data present at hand whereas Big data emphasis on all the techniques that can be used to analyze a large set of data(thousands of petabytes may be, to begin with)
Let us understand in detail the difference between machine learning and Big Data
Big Data Analytics vs Machine Learning
You will find both similarities and differences when you compare between big data analytics and machine learning. However, the major differences lie in their application.
Big data analytics as the name suggest is the analysis of patterns or extraction of information from big data. So, in big data analytics, the analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs but still produce desirable outputs.
Most data analysis activities which do not involve expert task can be done through big data analytics without the involvement of machine learning. However, if the computational power required is beyond human expertise, then machine learning will be required.
Normal big data analytics is all about cleaning and transforming data to extract information, which then can be fed to a machine learning system in order to enable further analysis or predict outcomes without the requirement of human involvement.
Big data analytics and machine learning can go hand-in-hand and it would benefit a lot to learn both. Both fields offer good job opportunities as the demand is high for professionals across industries. When it comes to salary, both profiles enjoy similar packages. If you have skills in both of them, you are a hot property in the field of analytics.
However, if you do not have the time to learn both, you can go for whichever you are interested in.
So what to choose?
After understanding the 3 key phrases i.e Data science, Big data and machine learning, we are now in a better position to understand their selection and usage in business. We now know that data science is a complete process of using the power of data to boost business growth. So any decision-making process involving data has to involve data science.
There are few factors which may determine whether you should go for machine learning or Big data way for your organisation. Let us have a look at these factors and understand them in more detail
Factors affecting the selection
Selection of Big Data or Machine learning depends upon the end-goal of the business. If you are looking forward to generating predictions say based on customer behaviour or you want to build recommender systems then machine learning is the way to go. On the other hand, if you are looking for data handling and manipulation support where you can extract, load and transform data then Big Data will be the right choice for you.
2. Scale of operations
The scale of operation is one deciding factor between Big data and machine learning. If you have lots and lots of data like thousands of TB’s etc then employing Big data capabilities is the only choice. Traditional systems are not built to handle this much amount of data. Various businesses these days are sitting over huge chunks of data collected but lack the ability to meaningfully process them. Big Data systems allow handling of such amounts of data. Big data employs the concept of parallel computing which eases enables the systems to process and manipulate data in bulk quantities
3. Available resources
Employing Big data or machine learning capabilities requires a lot of investment both in terms of human resource and capital. If an organisation has resources trained for big data capabilities, then only they can manage such big infrastructure and leverage its benefits
Applications of Machine Learning
1. Image Recognition
It is one of the most common machine learning applications. There are many situations where you can classify the object as a digital image. For digital images, the measurements describe the outputs of each pixel in the image.
2. Speech Recognition
Speech recognition (SR) is the translation of spoken words into text. It is also known as “automatic speech recognition” (ASR), “computer speech recognition”, or “speech to text” (STT).
3. Learning Associations
Learning association is the process of developing insights into various associations between products. A good example is how seemingly unrelated products may reveal an association with one another. When analyzed in relation to buying behaviours of customers.
4. Recommendation systems
These applications have been the bread and butter for many companies. When we talk about recommendation systems, we are referring to the targeted advertising on your Facebook page, the recommended products to buy on Amazon, and even the recommended movies or shows to watch on Netflix.
Applications of Big Data
Big data analytics has proven to be very useful in the government sector. Big data analysis played a large role in Barack Obama’s successful 2012 re-election campaign. The Indian Government utilizes numerous techniques to ascertain how the Indian electorate is responding to government action, as well as ideas for policy augmentation.
2. Social Media Analytics
The advent of social media has led to an outburst of big data. Various solutions have been built in order to analyze social media activity like IBM’s Cognos Consumer Insights, a point solution running on IBM’s BigInsights Big Data platform, can make sense of the chatter. Social media can provide valuable real-time insights into how the market is responding to products and campaigns. With the help of these insights, the companies can adjust their pricing, promotion, and campaign placements accordingly.
The technological applications of big data comprise of the following companies which deal with huge amounts of data every day and put them to use for business decisions as well. For example, eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising. Inside eBay‟s 90PB data warehouse. Amazon.com handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers.
4. Fraud detection
For businesses whose operations involve any type of claims or transaction processing, fraud detection is one of the most compelling Big Data application examples. Big Data platforms that can analyze claims and transactions in real time, identifying large-scale patterns across many transactions or detecting anomalous behaviour from an individual user, can change the fraud detection game.
Amazon employs both machine learning and big data capabilities to serve its customers. It uses ML in form of recommender systems to suggest new products to its customers. They use big data to maintain and serve all the products data they have. Right from processing all the images and the content, to displaying them over the website, it is handled by the employed big data systems.
Facebook similarly like Amazon has loads and loads of user data available with it. It uses machine learning to segment all the users based on their activity. Then, Facebook finds the best advertisements for its users in order to increase the clicks on the ads. All this is done through machine learning. With large user data at disposal, traditional systems can not process this data and make it ready for machine learning purposes. Facebook has employed big data systems so that they can process and transform this huge data and actually can derive insights out of it. Big data is required to make all this huge data processable.
In this blog, we learned how data science, machine learning and Big data link with each other. Whenever you want to solve any problem by using data at hand, data science is the process to solve it. If the data is too large and traditional systems or small-scale machines cannot handle it then BIG data techniques are the option to analyze such large chunks of data set. Machine learning covers the part when you want to make predictions of some kind, based on data you have at your end. These predictions will help you in validating your hypothesis around data and will enable smarter decision making.
Follow this link, if you are looking to learn more about data science online!