Data Lake & Hadoop : How can they power your Analytics?

Data Lake & Hadoop : How can they power your Analytics?

Powering analytics through a data lake and Hadoop is one of the most effective ways to increase ROI. It’s also an effective way to ensure that the analytics team has all the right information moving forward.

Powering analytics through a data lake and Hadoop is one of the most effective ways to increase ROI. It’s also an effective way to ensure that the analytics team has all the right information moving forward.

There are many challenges that research teams have to face regularly, and Hadoop can aid in effective data management.

From storage to analysis, Hadoop can provide the necessary framework to enable research teams to do their work. Hadoop is also not confined to any single model of working or any only language. That's why it's a useful tool when it comes to scaling up. Since companies can perform greater research, there is more data generated. The data can be fed back into the system to create unique results for the final objective.

Data lakes are essential to maintaining as well. Since the core data lake enables your organization to scale, it's necessary to have a single repository of all enterprise data. Over 90% of the world’s data has been generated over the last few years, and data lakes have been a positive force in the space.

Why Hadoop is effective?

From a research stand-point, Hadoop is useful in more ways than one. It runs on a cluster of commodity servers and can scale up support thousands of nodes. This means that the quantity of data being handled is massive, and many data sources can be treated at the same time. This increases the effectiveness of Big Data, especially in the cases of IoT, Artificial intelligence, Machine Learning, and other new technologies.

It also provides rapid data access across the nodes in the cluster. Users can get an authorized access to a large subset of the data or the entire database. This makes the job of the researcher and the admin that much easier. Hadoop can also be scaled up as the requirement increases over time.

If an individual node fails, the entire cluster can take over. That’s the best part about Hadoop and why companies across the world use it for their research activities. Hadoop is being redefined year over year and has been an industry standard for decades now. Its full potential can be discovered best in the research and analytics space with data lakes.

HDFS – The Hadoop Distributed File System (HDFS) is the primary storage system that Hadoop employs, using a NameNode and DataNode architecture. It provides higher performance across the board and acts as a data distribution system for the enterprise.

YARN – YARN is the cluster resource manager that allocates system resources to apps and jobs. This simplifies the process of mapping out the adequate resources necessary. It’s one of the core components within the Hadoop infrastructure and schedules tasks around the nodes.

MapReduce – It’s a highly effective framework that converts data into a more simple set with individual elements broken down into tuples. From here, the data is translated into games and efficiencies are created within the data network. This is an excellent component for making sense of large data sets within the research space.

Hadoop Common - The common is a collection of standard utilities and libraries that support other modules. It’s a core component of the Hadoop framework and ensures that the resources are allocated correctly. They also provide a framework for the processing of data and information.

Hadoop and Big Data Research

Hadoop is highly effective when it comes to Big Data. This is because there are greater advantages associated with using the technology to it's fullest potential. Researchers can access a higher tier of information and leverage insights based on Hadoop resources. Hadoop can also enable better processing of data, across various systems and platforms.

Anytime there are complex calculations to be done and difficult simulations to execute, Hadoop needs to be put in place. Hadoop can help parallel computation across various coding environments to enable Big Data to create novel insights. Otherwise, there may be overlaps in processing, and the architecture could fail to produce ideas.

From a BI perspective, Hadoop is crucial. This is because while researchers can produce raw data over a significant period, it's essential to have streamlined access to it. Additionally, from a business perspective, it's necessary to have strengths in Big Data processing and storage. The availability of data is as important as access to it. This increases the load on the server, and a comprehensive architecture is required to process the information.

That's where Hadoop comes in. Hadoop can enable better processing and handling of the data being produced. It can also integrate different systems into a single data lake foundation. Added to that, Hadoop can enable better configuration across the enterprise architecture. Hadoop can take raw data and convert it into more useful insights. Anytime there is complexities and challenges, Hadoop can provide more clarity.

Hadoop is also a more enhanced version of simple data management tools. Hadoop can take raw data and insight and present it in a more consumable format. From here, researchers can make their conclusions and prepare intelligence reports that signify results. They can also accumulate on-going research data and feed it back into the central system. This makes for greater on-going analysis, while Hadoop becomes the framework to accomplish it on.

Security on Hadoop and Implementing Data Lakes

There are a significant number of attacks on big data warehouses and data lakes on an on-going basis. It’s essential to have an infrastructure that has a steady security feature built-in. This is where Hadoop comes in. Hadoop can provide those necessary security tools and allow for more secure data transitions.

In the healthcare space, data is critical to preserving. If patient data leaks out, it could lead to complications and health scares. Additionally, in the financial services domain, if data on credit card information and customer SSN leaks out, then there is a legal and PR problem on the rise. That’s why companies opt for greater control using the Hadoop infrastructure. Hadoop is also beneficial regarding providing a better framework for cybersecurity and interoperability. Data integrity is preserved throughout the network, and there is increased control via dashboards provided.

From issuing Kerberos to introducing physical authentication, the Hadoop cluster is increasingly useful in its operations. There is an additional layer of security built into the group, giving rise to a more consistent database environment. Individual tickets can be granted on the Kerberos framework and users can get authenticated using the module.

Security can be enhanced by working with third-party developers to improve your overall Hadoop and Data lake security. You can also increase the security parameters around the infrastructure by creating a stricter authentication and user-management portal and policy. From a cyber-compliance perspective, it’s a better mechanism to work through at scale.

Apache Ranger is also a useful tool to monitor the flow of data as well. This is increasingly important when performing research on proprietary data in the company. Healthcare companies know all too well the value of data, which is why the Ranger can monitor the flow of data throughout the organization. Apache YARN has enabled an exact Data lake approach when it comes to information architecture.

That's why the Ranger is effective in maintaining security. The protocol can be set at the admin level, and companies can design the right tool to take their research ahead. The Ranger can also serve as the end-point management system for when different devices connect onto the cluster.

The Apache Ranger is also a handy centralized interface. This gives greater control to researchers, and all stakeholders in the research and analytics space are empowered. Use-cases emerge much cleaner when there is smooth handling of all data. There is also a more systematic approach to analytics, as there is an access terminal of all authorized personnel. Certain tiers of researchers can gain access to certain types of data and others can get a broader data overview. This can help streamline the data management process and make the analytics process that much more effective.

The Ranger serves as a visa processing system that gives access based on the required authorization. This means that junior researchers don't get access to highly classified information. Senior level researchers can gain the right amount of insight into the matter at hand and dig deep into core research data. Additionally, analysts can gain access to the data they've been authorized to use.

This enables researchers to use Hadoop as an authorization management portal as well. Data can be back-tracked to figure out who used the data portal last. The entire cluster can become unavailable to increase security against outsiders. However, when researchers want a second opinion, they can turn towards consultants who can gain tertiary access to the portal.

Recognizing the analytics needs of researchers and data scientists

It’s important for researchers to understand the need for analytics and vice versa. Hadoop provides that critical interface connect disconnected points in the research ecosystem. Additionally, it creates a more collaborative environment within the data research framework.

Healthcare, Fintech, Consumer Goods and Media & Research companies need to have a more analytical approach when conducting research. That’s why Hadoop becomes critical to leverage, as it creates a more robust environment. The analytics needs are fully recognized by Hadoop, providing more tools for greater analytics.

For forming the right data lake, there needs to be a search engine in place. This helps in streamlining the data and adding a layer of analysis to the raw information. Additionally, researchers can retrieve specific information through the portal. They’re able to perform more excellent analysis of core data that is readily available to them.

Data scientists can uncover accurate insights when they’re able to analyze larger data sets. With emerging technologies like Spark and HBase, Hadoop becomes that much more advanced as an analytics tool. There are more significant advantages to operating with Hadoop and data scientists can see more meaningful results. Over time, there is more convergence with unique data management platforms providing a more coherent approach to data.

The analysis is fully recognized over Hadoop, owing to its scale and scope of work. Hadoop can become the first Data lake ecosystem, as it has a broad range within multiple applications. There is also greater emphasis given to the integrity of data, which is what all researchers need. From core principles to new technology additions, there are components within Hadoop that make it that much more reliable.

Democratizing data for Researchers & Scientists

Having free access to data within the framework is essential. Hadoop helps in developing that democratic data structure within the network. Forecasting to trend analysis can be made that much more straightforward, with a more democratic approach to data. Data can be indeed sorted and retrieved based on the access provided.

Data can also be shared with resources to enable a more collaborative environment in the research process. Otherwise, data sets may get muddied as more inputs stream into the data lake. The lake needs to have a robust democratized approach so that researchers can gain access to that when needed. Additionally, it's essential to have more streamlined access to the data, which is another advantage of using Hadoop. Researchers need to deploy the technology at scale to obtain benefits that come along with it.

Data scientists can also acquire cleaner data that is error-free. This is increasingly important when researchers want to present their findings to stakeholders as there is no problem with integrity. The democratization of data ensures that everyone has access to the data sets that they're authorized to understand. Outsiders may not gain access and can be removed from the overall architecture.

Scientists can also study some aspects of the data lake and acquire unique insights that come with it. From a healthcare perspective, a single outbreak or an exceptional case can bring in new ideas that weren’t previously there before. This also adds immense value to distributed instances wherein there is no single source identified. Unique participants can explore the data like and uncover what is required from it.

It’s essential to have a more democratic approach when it comes to data integrity and data lake development. When the data lake is well maintained, it creates more opportunities for analysis within the research space. Researchers can be assured that their data is being presented in the best light possible. They can also uncover hidden trends and new insights based on that initial connection. The information is also sorted and classified better, using Hadoop’s extensive line of solutions and tools built-in.

Researchers have an affinity for using Hadoop, owing to its scale-readiness and great solution base. They can also be used to present information via other platforms, providing it with a more democratic outlook. The data can also be transmitted and shared via compatible platforms across the board. The researchers present within the ecosystem can even compile data that is based on your initial findings. This helps in maintaining a clean record and a leaner model of data exploration.

Benefits & Challenges of Hadoop for enterprise analysis

Hadoop is one of the most excellent solutions in the marketplace for extensive research and enterprise adoption. This is because of its scale and tools available to accomplish complex tasks. Researchers can also leverage the core technology to avail its benefits across a wide range of solution models.

The data can also be shared from one platform to another, creating a community data lake wherein different participants can emerge. However, overall integrity is maintained throughout the ecosystem. This enables better communication within the system, giving rise to an enhanced approach to systems management.

Hadoop benefits the research community in the four main data formats –

Core research information – This is data produced during trials, research tests and any algorithms that may be running on Machine Learning or Artificial Intelligence. This also includes raw information that is shared with another resource. It also provides information that can be presented across the board.

Manufacturing and batches – This data is essential to maintain as it aids in proper verification of any tools or products being implemented. It also helps in the check of process owners and supplies chain leads.

Customer care – For enterprise-level adoption, customer care information must be presented and stored effectively.

Public records – Security is vital when it comes to handling public records. This is why Hadoop is used to provide security measures adequately.

One of the main problems with Hadoop is leveraging massive data sets to one detailed insight. Since Hadoop requires the right talent to uncover insights, it becomes a complicated procedure. Owing to its complexities it also needs better compliance frameworks that can define specific rules based on instances.

Additionally, as Hadoop is scaled up, there are challenges with storage and space management. That’s why cloud computing is emerging as a viable solution to prevent data loss due to storage errors. Hadoop is also facing complexities regarding data synchronization. That's why researchers need to ensure that all systems are compliant with Hadoop and can leverage the scope of the core platform. For best results, it's ideal to have a more holistic approach to Hadoop.

Conclusion

With the vast quantities of data being generated every day, there is a need for greater analytics and insight in the research space. While every industry, from Healthcare to Automobile, relies on data in some form, it’s essential to have a logical research architecture built into the system. Otherwise, there may be data inefficiencies and chances of the data lake getting contaminated. It’s best to opt for a hybrid Hadoop model with proper security and networking capabilities. When it comes to performing accurate research, it’s essential to have all the right tools with you.

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Downloadable PDF of Best AI Cheat Sheets in Super High Definition

Let’s begin.

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science in HD

Part 1: Neural Networks Cheat Sheets

Neural Networks Cheat Sheets

Neural Networks Basics

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.

Basically, there are 3 different layers in a neural network :

  1. Input Layer (All the inputs are fed in the model through this layer)
  2. Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
  3. Output Layer (The data after processing is made available at the output layer)

Neural Networks Graphs

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.

Part 2: Machine Learning Cheat Sheets

Machine Learning Cheat Sheets

>>> If you like these cheat sheets, you can let me know here.<<<

Machine Learning with Emojis

Machine Learning with Emojis Cheat Sheet

Machine Learning: Scikit Learn 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

Scikit-learn algorithm

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

Part 3: Data Science with Python

Data Science with Python Cheat Sheets

Data Science: TensorFlow Cheat Sheet

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.

Data Science: PySpark RDD Basics Cheat Sheet

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

Data Science: NumPy Basics Cheat Sheet

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.

***If you like these cheat sheets, you can let me know ***here.

Data Science: Bokeh Cheat Sheet

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

Data Science: Karas Cheat Sheet

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.

Data Science: Padas Basics Cheat Sheet

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

Data Wrangling

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 Science: Data Wrangling with Pandas Cheat Sheet

Data Wrangling with Pandas Cheat Sheet

“Why Use tidyr & dplyr

  • Although many fundamental data processing functions exist in R, they have been a bit convoluted to date and have lacked consistent coding and the ability to easily flow together → leads to difficult-to-read nested functions and/or choppy code.
  • R Studio is driving a lot of new packages to collate data management tasks and better integrate them with other analysis activities → led by Hadley Wickham & the R Studio teamGarrett Grolemund, Winston Chang, Yihui Xie among others.
  • As a result, a lot of data processing tasks are becoming packaged in more cohesive and consistent ways → leads to:
  • More efficient code
  • Easier to remember syntax
  • Easier to read syntax” via Rstudios

Data Science: Data Wrangling with ddyr and tidyr

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.[3]

Data Science: Matplotlib Cheat Sheet

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 Science: Data Visualization with ggplot2 Cheat Sheet

Data Visualization with ggplot2 Cheat Sheet

>>> If you like these cheat sheets, you can let me know here. <<<

Data Science: Big-O Cheat Sheet

Big-O Cheat Sheet

Resources

Special thanks to DataCamp, Asimov Institute, RStudios and the open source community for their content contributions. You can see originals here:

Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/

Bokeh Cheat Sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Bokeh_Cheat_Sheet.pdf

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

Keras: https://en.wikipedia.org/wiki/Keras

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

ML Cheat Sheet:: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY

Matpotlib: https://en.wikipedia.org/wiki/Matplotlib

Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/

Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/

Neural Networks: https://www.quora.com/Where-can-find-a-cheat-sheet-for-neural-network

Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE

NumPy: https://en.wikipedia.org/wiki/NumPy

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM

Pandas: https://en.wikipedia.org/wiki/Pandas_(software)

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: https://en.wikipedia.org/wiki/Scikit-learn

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

SciPy: https://en.wikipedia.org/wiki/SciPy

TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html

Tensor Flow: https://en.wikipedia.org/wiki/TensorFlow

Data Science vs Data Analytics vs Big Data

Data Science vs Data Analytics vs Big Data

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

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

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

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

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

What Is Data Science?

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

[Source: gfycat.com]

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

What is Big Data?

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

[Source: gfycat.com]

What is Data Analytics?

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

[Source: ibm.com]

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

What does a Data Scientist do?

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

Roles of Data Scientist

What do Big Data Professionals do?

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

Roles of Big Data Professiona

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

What does a Data Analyst do?

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

Roles of Data Analyst

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

What Is The Salary Prospect?

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

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

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

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

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

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

Approach in Netflix

Traditional Data Processing

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

Big Data approach to process Netflix data

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

Role of Data Scientist in Optimizing the Netflix streaming experience

1. Understanding the impact of QoE on user behavior

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

2. Improving the streaming experience

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

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

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

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

3. Optimize content caching

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

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

4. Improving content quality

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

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

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

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

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

Role of Data Analyst in Netflix

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

So what does a Data Analyst do?

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

Learn Data Science | How to Learn Data Science for Free

Learn Data Science | How to Learn Data Science for Free

Learn Data Science | How to Learn Data Science for Free. In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free.

The average cost of obtaining a masters degree at traditional bricks and mortar institutions will set you back anywhere between $30,000 and $120,000. Even online data science degree programs don’t come cheap costing a minimum of $9,000. So what do you do if you want to learn data science but can’t afford to pay this?

I trained into a career as a data scientist without taking any formal education in the subject. In this article, I am going to share with you my own personal curriculum for learning data science if you can’t or don’t want to pay thousands of dollars for more formal study.

The curriculum will consist of 3 main parts, technical skills, theory and practical experience. I will include links to free resources for every element of the learning path and will also be including some links to additional ‘low cost’ options. So if you want to spend a little money to accelerate your learning you can add these resources to the curriculum. I will include the estimated costs for each of these.

Technical skills

The first part of the curriculum will focus on technical skills. I recommend learning these first so that you can take a practical first approach rather than say learning the mathematical theory first. Python is by far the most widely used programming language used for data science. In the Kaggle Machine Learning and Data Science survey carried out in 2018 83% of respondents said that they used Python on a daily basis. I would, therefore, recommend focusing on this language but also spending a little time on other languages such as R.

Python Fundamentals

Before you can start to use Python for data science you need a basic grasp of the fundamentals behind the language. So you will want to take a Python introductory course. There are lots of free ones out there but I like the Codeacademy ones best as they include hands-on in-browser coding throughout.

I would suggest taking the introductory course to learn Python. This covers basic syntax, functions, control flow, loops, modules and classes.

Data analysis with python

Next, you will want to get a good understanding of using Python for data analysis. There are a number of good resources for this.

To start with I suggest taking at least the free parts of the data analyst learning path on dataquest.io. Dataquest offers complete learning paths for data analyst, data scientist and data engineer. Quite a lot of the content, particularly on the data analyst path is available for free. If you do have some money to put towards learning then I strongly suggest putting it towards paying for a few months of the premium subscription. I took this course and it provided a fantastic grounding in the fundamentals of data science. It took me 6 months to complete the data scientist path. The price varies from $24.50 to $49 per month depending on whether you pay annually or not. It is better value to purchase the annual subscription if you can afford it.

The Dataquest platform

Python for machine learning

If you have chosen to pay for the full data science course on Dataquest then you will have a good grasp of the fundamentals of machine learning with Python. If not then there are plenty of other free resources. I would focus to start with on scikit-learn which is by far the most commonly used Python library for machine learning.

When I was learning I was lucky enough to attend a two-day workshop run by Andreas Mueller one of the core developers of scikit-learn. He has however published all the material from this course, and others, on this Github repo. These consist of slides, course notes and notebooks that you can work through. I would definitely recommend working through this material.

Then I would suggest taking some of the tutorials in the scikit-learn documentation. After that, I would suggest building some practical machine learning applications and learning the theory behind how the models work — which I will cover a bit later on.

SQL

SQL is a vital skill to learn if you want to become a data scientist as one of the fundamental processes in data modelling is extracting data in the first place. This will more often than not involve running SQL queries against a database. Again if you haven’t opted to take the full Dataquest course then here are a few free resources to learn this skill.

Codeacamdemy has a free introduction to SQL course. Again this is very practical with in-browser coding all the way through. If you also want to learn about cloud-based database querying then Google Cloud BigQuery is very accessible. There is a free tier so you can try queries for free, an extensive range of public datasets to try and very good documentation.

Codeacademy SQL course

R

To be a well-rounded data scientist it is a good idea to diversify a little from just Python. I would, therefore, suggest also taking an introductory course in R. Codeacademy have an introductory course on their free plan. It is probably worth noting here that similar to Dataquest Codeacademy also offers a complete data science learning plan as part of their pro account (this costs from $31.99 to $15.99 per month depending on how many months you pay for up front). I personally found the Dataquest course to be much more comprehensive but this may work out a little cheaper if you are looking to follow a learning path on a single platform.

Software engineering

It is a good idea to get a grasp of software engineering skills and best practices. This will help your code to be more readable and extensible both for yourself and others. Additionally, when you start to put models into production you will need to be able to write good quality well-tested code and work with tools like version control.

There are two great free resources for this. Python like you mean it covers things like the PEP8 style guide, documentation and also covers object-oriented programming really well.

The scikit-learn contribution guidelines, although written to facilitate contributions to the library, actually cover the best practices really well. This covers topics such as Github, unit testing and debugging and is all written in the context of a data science application.

Deep learning

For a comprehensive introduction to deep learning, I don’t think that you can get any better than the totally free and totally ad-free fast.ai. This course includes an introduction to machine learning, practical deep learning, computational linear algebra and a code-first introduction to natural language processing. All their courses have a practical first approach and I highly recommend them.

Fast.ai platform

Theory

Whilst you are learning the technical elements of the curriculum you will encounter some of the theory behind the code you are implementing. I recommend that you learn the theoretical elements alongside the practical. The way that I do this is that I learn the code to be able to implement a technique, let’s take KMeans as an example, once I have something working I will then look deeper into concepts such as inertia. Again the scikit-learn documentation contains all the mathematical concepts behind the algorithms.

In this section, I will introduce the key foundational elements of theory that you should learn alongside the more practical elements.

The khan academy covers almost all the concepts I have listed below for free. You can tailor the subjects you would like to study when you sign up and you then have a nice tailored curriculum for this part of the learning path. Checking all of the boxes below will give you an overview of most elements I have listed below.

Maths

Calculus

Calculus is defined by Wikipedia as “the mathematical study of continuous change.” In other words calculus can find patterns between functions, for example, in the case of derivatives, it can help you to understand how a function changes over time.

Many machine learning algorithms utilise calculus to optimise the performance of models. If you have studied even a little machine learning you will probably have heard of Gradient descent. This functions by iteratively adjusting the parameter values of a model to find the optimum values to minimise the cost function. Gradient descent is a good example of how calculus is used in machine learning.

What you need to know:

Derivatives

  • Geometric definition
  • Calculating the derivative of a function
  • Nonlinear functions

Chain rule

  • Composite functions
  • Composite function derivatives
  • Multiple functions

Gradients

  • Partial derivatives
  • Directional derivatives
  • Integrals

Linear Algebra

Many popular machine learning methods, including XGBOOST, use matrices to store inputs and process data. Matrices alongside vector spaces and linear equations form the mathematical branch known as Linear Algebra. In order to understand how many machine learning methods work it is essential to get a good understanding of this field.

What you need to learn:

Vectors and spaces

  • Vectors
  • Linear combinations
  • Linear dependence and independence
  • Vector dot and cross products

Matrix transformations

  • Functions and linear transformations
  • Matrix multiplication
  • Inverse functions
  • Transpose of a matrix

Statistics

Here is a list of the key concepts you need to know:

Descriptive/Summary statistics

  • How to summarise a sample of data
  • Different types of distributions
  • Skewness, kurtosis, central tendency (e.g. mean, median, mode)
  • Measures of dependence, and relationships between variables such as correlation and covariance

Experiment design

  • Hypothesis testing
  • Sampling
  • Significance tests
  • Randomness
  • Probability
  • Confidence intervals and two-sample inference

Machine learning

  • Inference about slope
  • Linear and non-linear regression
  • Classification

Practical experience

The third section of the curriculum is all about practice. In order to truly master the concepts above you will need to use the skills in some projects that ideally closely resemble a real-world application. By doing this you will encounter problems to work through such as missing and erroneous data and develop a deep level of expertise in the subject. In this last section, I will list some good places you can get this practical experience from for free.

“With deliberate practice, however, the goal is not just to reach your potential but to build it, to make things possible that were not possible before. This requires challenging homeostasis — getting out of your comfort zone — and forcing your brain or your body to adapt.”, Anders Ericsson, Peak: Secrets from the New Science of Expertise

Kaggle, et al

Machine learning competitions are a good place to get practice with building machine learning models. They give access to a wide range of data sets, each with a specific problem to solve and have a leaderboard. The leaderboard is a good way to benchmark how good your knowledge at developing a good model actually is and where you may need to improve further.

In addition to Kaggle, there are other platforms for machine learning competitions including Analytics Vidhya and DrivenData.

Driven data competitions page

UCI Machine Learning Repository

The UCI machine learning repository is a large source of publically available data sets. You can use these data sets to put together your own data projects this could include data analysis and machine learning models, you could even try building a deployed model with a web front end. It is a good idea to store your projects somewhere publically such as Github as this can create a portfolio showcasing your skills to use for future job applications.


UCI repository

Contributions to open source

One other option to consider is contributing to open source projects. There are many Python libraries that rely on the community to maintain them and there are often hackathons held at meetups and conferences where even beginners can join in. Attending one of these events would certainly give you some practical experience and an environment where you can learn from others whilst giving something back at the same time. Numfocus is a good example of a project like this.

In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free. Showcasing what you are able to do in the form of a portfolio is a great tool for future job applications in lieu of formal qualifications and certificates. I really believe that education should be accessible to everyone and, certainly, for data science at least, the internet provides that opportunity. In addition to the resources listed here, I have previously published a recommended reading list for learning data science available here. These are also all freely available online and are a great way to complement the more practical resources covered above.

Thanks for reading!