Data Science: Visualizing the data at global level

We have always relied on the powers of oracles in order to find out what happens next. That is because we want to make the right decision and do not want to miss anything as the future is always uncertain. It is soothing to know that we can depend on technologies, knowledge, and insights that allow us to take wise decisions and secure our future. Business relies on these entities to make decisions in order to secure its future and thrive. But not every business is able to make sense out of the enormous data it has. Nokia, for instance, had millions of data points collecting data from its customers and funneling it into its business intelligence. Yet, it was not able to predict the rise of smartphones and remained biased towards its traditional business model. The once unchallengeable company is now struggling to gain grounds over its competitors who took the right decision at the right time.

Making sense out of data is as crucial as collecting it. Why companies like Nokia fail to utilize their data is that the two sides involved in the whole decision making process are polar opposites. On one hand are the business people who know what data they need and can define requirements, but do not possess skills to design a data architecture that gives them the data they need. Technology people, those who provide data, don’t understand the business requirements, but can design the data architecture. Thus when these two sets of experts fail to find common ground, business misses insights that are crucial for business intelligence.

Data Science has been a trending word in the industry for a long time. It is the middle path of the business aspect and the technology aspect of decision making. Data science analyses data to provide actionable insights. At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them by incorporating computer science, data modeling, statistics, analytics, and mathematics. With data points such as mobile apps, web apps, websites, point of sales, IoT increasing geometrically, the role and impact of data science can only grow in the future.

Linkedin, in its initial days, was growing fast but its users were not making connections with people already on the site. The traditional analysis was not helping it. Then one executive employed Data Science in order to create more engagement. The process saw unprecedented increase in use connections. Uber, the unicorn start-up, runs detailed predictive analysis of data to check when the demand for cabs is bound to rise and uses surge pricing. It uses similar data science to promote driver loyalty by providing them incentives. In short, Data Science is becoming a crucial discipline and a reliable system for making business decisions across domains.

One of the biggest misconceptions is that you need a sciences or math Ph. D to become a legitimate data scientist. Data Scientists use many technologies such as Hadoop, Spark, and Python. These technologies do not warrant a Ph. D.

Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. In simple words, Hadoop is a framework that allows you to store Big Data in a distributed environment so that you can process it parallel.

Apache Spark is an open-source engine built around speed, ease of use, and sophisticated analytics and developed specifically for handling large-scale data processing and analytics. It allows users to access data in across sources, such as Hadoop Distributed File System (HDFS), Amazon S3 etc.  Internet behemoths such as Netflix, Yahoo, and eBay have deployed Spark massively, collectively processing multiple petabytes of data on clusters of thousands of nodes.

Python or Monty Python is a general purpose programming language which has overtaken R as the primary language of Data analytics, Data Science owing to its capabilities such as easier learning curve, wide reach, bigger user base and support groups, flexibility and better app integration.

Mastering these technologies can open the avenues for an aspiring Data Scientist. There aren’t enough Data scientists to cater to the growing needs of the industry.

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Top 10 Open Source Big Data Tools

Data has become a powerful tool in today’s society, where it translates into direct knowledge and tons of money. Companies are paying through the nose to get their hands on data, so that they can modify their strategies, based on the wants and needs of their customers. But, it doesn’t stop there! Big Data is also important for governments, which helps run countries – such as calculating the census.

Data is often in a state of mess, with bucket loads of information coming through multiple channels. Here’s a simple analogy to understand how big data works. Search a common term on Google, can you see the number of results on the top of the search page? Well, now imagine having that many results thrown at you at the same time, but not in a systematic manner. Well, this is big data. Let’s look at the more formal definition of the term.

What is Big Data?
The term ‘Big Data’ refers to extremely large data sets, structured or unstructured, that are so complex that they need more sophisticated processing systems than the traditional data processing application software.

It can also refer to the process of using predictive analytics, user behavior analytics or other advanced data analysis technology to extract value from a data set. Big Data is often used in businesses or government agencies to find trends and patterns, that can help them strategic decisions or spot a certain pattern or trend among the masses.

Here are some open source tools to help you sort through big data:

1. Apache Hadoop
Hadoop has become synonymous with big data and is currently the most popular distributed data processing software. This powerful system is known for its ease of use and its ability to process extremely large data in both, structured and unstructured formats, as well as replicating chunks of data to nodes and making it available on the local processing machine. Apache has also introduced other technologies that accentuate Hadoop’s capabilities such as Apache Cassandra, Apache Pig, Apache Spark and even ZooKeeper. You can learn this amazing technology using real world examples here.

2. Lumify
Lumify is a relatively new open source project to create a Big Data fusion and is a great alternative to Hadoop. It has the ability to rapidly sort through numerous quantities of data in different sizes, sources and format. What helps stand out is it’s web-based interface allows users to explore relationships between the data via 2D and 3D graph visualizations, full-text faceted search, dynamic histograms, interactive geospatial views, and collaborative workspaces shared in real-time. It also works out of the box on Amazon’s AWS environment.

3. Apache Storm
Apache Storm can be used with or without Hadoop, and is an open source distributed realtime computation system. It makes it easier to process unbounded streams of data, especially for real-time processing. It is extremely simple and easy to use and can be configured with any programming language that the user is comfortable with. Storm is great for using in cases such as realtime analytics, continuous computation, online machine learning, etc. Storm is scalable and fast, making it perfect for companies that want fast and efficient results.

4. HPCC Systems Big Data
This is a brilliant platform for manipulating, transforming, querying and data warehousing. A great alternative to Hadoop, HPCC delivers superior performance, agility, and scalability. This technology has been used effectively in production environments longer than Hadoop, and offers features such as built-in distributed file system, scalability thousands of nodes, powerful development IDE, fault resilient, etc.

5. Apache Samoa
Samoa, an acronym for Scalable Advanced Massive Online Analysis, is a platform for mining Big Data streams, especially for Machine Learning. It contains a programming abstraction for distributed streaming ML algorithms. This platform eliminates the complexity of underlying distributed stream processing engines, making it easier to develop new ML algorithms.

6. Elasticsearch
A reliable and secure open source platform that allows users to take any data from any source, in any format and search, analyze it and visualize it real time. Elasticsearch has been designed for horizontal scalability, reliability and easy management, all the while combining speed of search with the power of analytics. It uses a developer-friendly, query language that covers structured, unstructured and time-series data.

7. MongoDB
MongoDB is also a great tool to help store and analyze big data, as well as help make applications. It was originally designed to support humongous databases, with its name MongoDB, actually derived from the word humongous. MongoDB is a no SQL database that is written in C++ with document-oriented storage, full index support, replication and high availability, etc. You can learn how to get started with MongoDB here.

8. Talend Open Studio for Big Data
This is more of an addition to Hadoop and other NOSQL databases, but is a powerful addition non-the-less. This open studio offers multiple products to help you learn everything you can do with Big Data. From integration to cloud management, it can help you simplify the job of processing big data. It also provides graphical tools and wizards to help write native code for Hadoop.

9. RapidMiner
Formerly known as YALE, RapidMiner tool offers advanced analytics through template-based frameworks. It barely requires users to write any code and is offered as a service, rather than a local software. RapidMiner has quickly risen to the top position as a data mining tool and also offers functionality such as data preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment.

10. R-Programming
R isn’t just a software, but also a programming language. Project R is the software that has been designed as a data mining tool, while R programming language is a high-level statistical language that is used for analysis. An open source language and tool, Project R is written is R language and is widely used among data miners for developing statistical software and data analysis. In addition to data mining it provides statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.

Big Data mining and analysis are definitely going to continue to grow in the future, with many companies and agencies spending lots of time and money, for acquiring and analyzing data, making data more powerful. If you have used any of these tools or have any other favorite tools for big data, please let us know in the comments below!