When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them
When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them
We live in a data-driven world. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Now that Hadoop and other frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them.
In this article on Data Science vs Data Analytics vs Big Data, I will be covering the following topics in order to make you understand the similarities and differences between them.
Introduction to Data Science, Big Data & Data AnalyticsWhat does Data Scientist, Big Data Professional & Data Analyst do?Skill-set required to become Data Scientist, Big Data Professional & Data AnalystWhat is a Salary Prospect?Real time Use-case## Introduction to Data Science, Big Data, & Data Analytics
Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics.
It also involves solving a problem in various ways to arrive at the solution and on the other hand, it involves to design and construct new processes for data modeling and production using various prototypes, algorithms, predictive models, and custom analysis.
Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.
Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data.
[Source: ibm.com]What Does Data Scientist, Big Data Professional & Data Analyst Do?
Data Scientists perform an exploratory analysis to discover insights from the data. They also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.
Roles of Data Scientist
The responsibilities of big data professional lies around dealing with huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity.
Roles of Big Data Professiona
Data analysts translate numbers into plain English. Every business collects data, like sales figures, market research, logistics, or transportation costs. A data analyst’s job is to take that data and use it to help companies to make better business decisions.
Roles of Data AnalystSkill-Set Required To Become Data Scientist, Big Data Professional, & Data Analyst What Is The Salary Prospect?
The below figure shows the average salary structure of **Data Scientist, Big Data Specialist, **and Data Analyst.A Scenario Illustrating The Use Of Data Science vs Big Data vs Data Analytics.
Now, let’s try to understand how can we garner benefits by combining all three of them together.
Let’s take an example of Netflix and see how they join forces in achieving the goal.
First, let’s understand the role of* Big Data Professional* in Netflix example.
Netflix generates a huge amount of unstructured data in forms of text, audio, video files and many more. If we try to process this dark (unstructured) data using the traditional approach, it becomes a complicated task.
Approach in Netflix
Traditional Data Processing
Hence a Big Data Professional designs and creates an environment using Big Data tools to ease the processing of Netflix Data.
Big Data approach to process Netflix data
Now, let’s see how Data Scientist Optimizes the Netflix Streaming experience.
Role of Data Scientist in Optimizing the Netflix streaming experience
User behavior refers to the way how a user interacts with the Netflix service, and data scientists use the data to both understand and predict behavior. For example, how would a change to the Netflix product affect the number of hours that members watch? To improve the streaming experience, Data Scientists look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted. Another metric is bitrate, that refers to the quality of the picture that is served/seen — a very low bitrate corresponds to a fuzzy picture.
How do Data Scientists use data to provide the best user experience once a member hits “play” on Netflix?
One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.
For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.
By determining all these factors one can improve the streaming experience.
A set of big data problems also exists on the content delivery side.
The key idea here is to locate the content closer (in terms of network hops) to Netflix members to provide a great experience. By viewing the behavior of the members being served and the experience, one can optimize the decisions around content caching.
Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers.
In addition to the internal quality checks, Data scientists also receive feedback from our members when they discover issues while viewing.
By combining member feedback with intrinsic factors related to viewing behavior, they build the models to predict whether a particular piece of content has a quality issue. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by the Netflix users to close the loop on quality and replace content that does not meet the expectations of the users.
So this is how Data Scientist optimizes the Netflix streaming experience.
Now let’s understand how Data Analytics is used to drive the Netflix success.
Role of Data Analyst in Netflix
The above figure shows the different types of users who watch the video/play on Netflix. Each of them has their own choices and preferences.
So what does a Data Analyst do?
Data Analyst creates a user stream based on the preferences of users. For example, if user 1 and user 2 have the same preference or a choice of video, then data analyst creates a user stream for those choices. And also –
Orders the Netflix collection for each member profile in a personalized way.We know that the same genre row for each member has an entirely different selection of videos.Picks out the top personalized recommendations from the entire catalog, focusing on the titles that are top on ranking.By capturing all events and user activities on Netflix, data analyst pops out the trending video.Sorts the recently watched titles and estimates whether the member will continue to watch or rewatch or stop watching etc.
I hope you have *understood *the *differences *& *similarities *between Data Science vs Big Data vs Data Analytics.
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big DataDownloadable PDF of Best AI Cheat Sheets in Super High Definition
Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science in HD
Neural Networks Cheat Sheets
Neural Networks Basics Cheat Sheet
An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.
Neural Networks Graphs Cheat Sheet
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
Machine Learning Cheat Sheets
Machine Learning with Emojis Cheat Sheet
Scikit Learn Cheat Sheet
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines is a simple and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and matplotlib an open source, commercially usable — BSD license
Scikit-learn Algorithm Cheat Sheet
This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.
If you like these cheat sheets, you can let me know here.### Machine Learning: Scikit-Learn Algorythm for Azure Machine Learning Studios
Scikit-Learn Algorithm for Azure Machine Learning Studios Cheat Sheet
Data Science with Python Cheat Sheets
TensorFlow Cheat Sheet
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
If you like these cheat sheets, you can let me know here.### Data Science: Python Basics Cheat Sheet
Python Basics Cheat Sheet
Python is one of the most popular data science tool due to its low and gradual learning curve and the fact that it is a fully fledged programming language.
PySpark RDD Basics Cheat Sheet
“At a high level, every Spark application consists of a driver program that runs the user’s
main function and executes various parallel operations on a cluster. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.” via Spark.Aparche.Org
NumPy Basics Cheat Sheet
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
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Bokeh Cheat Sheet
“Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.” from Bokeh.Pydata.com
Karas Cheat Sheet
Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.
Padas Basics Cheat Sheet
Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.
If you like these cheat sheets, you can let me know here.### Pandas Cheat Sheet: Data Wrangling in Python
Pandas Cheat Sheet: Data Wrangling in Python
The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.
Data Wrangling with Pandas Cheat Sheet
Data Wrangling with ddyr and tidyr Cheat Sheet
If you like these cheat sheets, you can let me know here.### Data Science: Scipy Linear Algebra
Scipy Linear Algebra Cheat Sheet
SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.
Matplotlib Cheat Sheet
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented APIfor embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib.
Pyplot is a matplotlib module which provides a MATLAB-like interface matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.
Data Visualization with ggplot2 Cheat Sheet
Big-O Cheat Sheet
Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/
Data Science Cheat Sheet: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics
Data Wrangling Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf
Data Wrangling: https://en.wikipedia.org/wiki/Data_wrangling
Ggplot Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
Keras Cheat Sheet: https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs
Machine Learning Cheat Sheet: https://ai.icymi.email/new-machinelearning-cheat-sheet-by-emily-barry-abdsc/
Machine Learning Cheat Sheet: https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet
Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY
Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/
Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/
Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM
Pandas Cheat Sheet: https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc
Pyspark Cheat Sheet: https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ
Scikit Cheat Sheet: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet
Scikit-learn Cheat Sheet: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html
Scipy Cheat Sheet: https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI
TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html
Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning - Learn about each concept and relation between them for their ...
Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning - Learn about each concept and relation between them for their ...What is Data Science?
Data Science is an interdisciplinary field whose primary objective is the extraction of meaningful knowledge and insights from data. These insights are extracted with the help of various mathematical and Machine Learning-based algorithms. Hence, Machine Learning is a key element of Data Science.
Alongside Machine Learning, as the name suggests, “data” itself is the fuel for Data Science. Without the availability of appropriate data, key insights cannot be extracted from it. Both the volume and accuracy of data matters in this field, since the algorithms are designed to “learn” with “experience”, which comes through the data provided. Data Science involves the use of various types of data, from multiple sources. Some of the types of data are image data, text data, video data, time-dependent data, time-independent data, audio data, etc.
Data Science requires knowledge of multiple disciplines. As shown in the figure, it is a combination of Mathematics and Statistics, Computer Science skills and Domain Specific Knowledge. Without a mastery of all these sub-domains, the grasp on Data Science will be incomplete.What is Machine Learning?
Machine Learning is a subset or a part of Artificial Intelligence. It primarily involves the scientific study of algorithmic, mathematical, and statistical models which performs a specific task by analyzing data, without any explicit step-by-step instructions, by relying on patterns and inference, which is drawn from the data. This also contributes to its alias, Pattern Recognition.
Its objective is to recognize patterns in a given data and draw inferences, which allows it to perform a similar task on similar but unseen data. These two separate sets of data are known as the “Training Set” and “Testing Set” respectively.
Machine Learning primarily finds its applications in solving complex problems, which, a normal procedure oriented program cannot solve, or in places where there are too many variables that need to be explicitly programmed, which is not feasible.
As shown in the figure, Machine Learning is primarily of three types, namely: Supervised Learning, Unsupervised Learning and Reinforcement Learning.
Artificial Intelligence is a vast field made up of multidisciplinary subjects, which aims to artificially create “intelligence” to machines, similar to that displayed by humans and animals. The term is used to describe machines that mimic cognitive functions such as learning and problem-solving.
Artificial Intelligence can be broadly classified into three parts: Analytical AI, Human-Inspired AI, and Humanized AI.
From the above introductions, it may seem that these fields are not related to each other. However, that is not the case. Each of these three fields is quite closely related to each other than it may seem.
If we look at Venn Diagrams, Artificial Intelligence, Machine Learning and Data Science are overlapping sets, with Machine Learning being a subset or a part of Artificial Intelligence, and Data Science having a significant chunk of it under Artificial Intelligence and Machine Learning.
Artificial Intelligence is a much broader field and it incorporates most of the other intelligence-related fields of study. Machine Learning, being a part of AI, deals with the algorithmic learning and inference based on data, and finally, Data Science is primarily based on statistics, probability theory, and has significant contribution of Machine Learning to it; of course, AI also being a part of it, since Machine Learning is indeed a subset of Artificial Intelligence.
Similarities: All of the three fields have one thing in common, Machine Learning. Each of these is heavily dependent on Machine Learning Algorithms.
In Data Science, the statistical algorithms that are used are limited to certain applications. In most cases, Data Scientists rely on Machine Learning techniques to extract inferences from data.
The current technological advancement in Artificial Intelligence is heavily based on Machine Learning. The part of AI without Machine Learning is like a car without an engine. However, without the “learning” part, Artificial Intelligence is basically Expert Systems, Search and Optimization algorithms.
Difference between the three
Even though they are significantly similar to each other, there are still a few key differences that are to be noted.
Since all the three domains are interrelated, they have some common applications and some unique to each of them. Most applications involve the use of Machine Learning in some form or the other. Even then, there are certain applications of each domain, which are unique. A few of them are listed below:
Since the fields are interrelated by a significant degree, the skill-set required to master each of these fields is nearly the same and overlapping. However, there are a few skill-sets that are uniquely associated with each of them. The same has been discussed further.
The Job Market for each of these fields is in very high demand. As a direct quote from Andrew Ng says, “AI is the new Electricity”. This is quite true as the extended field of Artificial Intelligence is at the verge of revolutionizing every industry in ways that could not be anticipated earlier.
Hence, the demand for jobs in the field of Data Science and Machine Learning is quite high. There are more job openings worldwide than the number of qualified Engineers who are eligible to fill that position. Hence, due to supply-demand constraints, the amount of compensation offered by companies for such roles exceeds any other domain.
The job scenario for each of the different domains are discussed further:
Data Science, Machine Learning and Artificial Intelligence are like the different branches of the same tree. They are highly overlapping and there is no clear boundary amongst them. They have common skill set requirements and common applications as well. They are just different names given to slightly different versions of AI.
Finally, it is worth mentioning that since there is high overlap in required skill-set, an optimally skilled Engineer is eligible to work in either of the three domains and switch domains without any major changes.
Learn the Difference between the most popular Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning
In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning.
Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning.
Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.
But still, there is a big misconception among many people about the meaning of these terms.
In the worst case, one may think that these terms describe the same thing — which is simply false.
A large number of companies claim nowadays to incorporate some kind of “ Artificial Intelligence” (AI) in their applications or services.
But artificial intelligence is only a broader term that describes applications when a machine mimics “ cognitive “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.
On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of if-else statements.
An if-else statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:
Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: Machine Learning and Deep Learning. Both are subfields of Artificial IntelligenceMachine Learning vs Deep Learning
Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.
Machine Learning incorporates “ classical” algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets.
The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.
For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.
When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?
At this point, you may ask: How do we minimize the error?
One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.
Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.
In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.
Machine Learning is a pretty old field and incorporates methods and algorithms that have been around for dozens of years, some of them since as early as the sixties.
Some known methods of classification and prediction are the Naive Bayes Classifier and the Support Vector Machines. In addition to the classification, there are also clustering algorithms such as the well-known K-Means and tree-based clustering. To reduce the dimensionality of data to gain more insights about it’ nature methods such as Principal component analysis and tSNE are used.Deep Learning — The next big Thing
Deep Learning, on the other hand, is a very young field of Artificial Intelligence that is powered by artificial neural networks.
It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.
Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve.
All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.
We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and Deep Learning. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.Why is Deep Learning better than Machine Learning?
The first advantage is the needlessness of Feature Extraction. What do I mean by this?
Well if you want to use a Machine Learning model to determine whether a given picture shows a car or not, we as humans, must first program the unique features of a car (shape, size, windows, wheels etc.) into the algorithm. This way the algorithm would know what to look after in the given pictures.
In the case of a Deep Learning model, is step is completely unnecessary. The model would recognize all the unique characteristics of a car by itself and make correct predictions.
In fact, the needlessness of feature extraction applies to any other task for a deep learning model. You simply give the neural network the raw data, the rest is done by the model. While for a machine learning model, you would need to perform additional steps, such as the already mentioned extraction of the features of the given data.
The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:
“ The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms. “
Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.