Data Science – A Revelation of the Modern Digital Era

Data is transpiring today in large quantities because of the modern equipment, so what is happening with the old data? Why is the old data so important? What can the old data tell us? Globalization is the means of connecting and desegregating people, businesses, and government authorities all across the globe. Owing to the internet and social media the world has become complicated with the overload of information that means more and more piles of data are added to the large volumes of already existing data.

New technologies continue to present a new amount of uncertainties and possibilities for the world. The possibility of billions of people connected by the internet and their mobile devices, with an exceptional path to knowledge is infinite. These possibilities are aggregated by developing technology inventions in fields such as artificial intelligence, business analytics, data analytics, data science, robotics, autonomous transportation, 3-D printing, nanotechnology, and quantum computing.

Every time we use our search engines or our social media platforms to interact or to share pictures digitally, we add more to the pile of data which already exists in a large volume. As the amount of data generation grew, the need to monitor or collect or prepare this data also grew. These became some of the main difficulties and concerns for enterprise industries until the year 2010. Since there’s been a lot of hype about data storing and processing, a new door was opened to a broad field which came into existence as “Data Science”.

What is Data Science?

Data science does not mean the invention of complicated models or making awesome visualizations or writing data codes. Data Science means the usage of data to generate as much influence as plausible for an organization. This influence can be created in the form of multiple things – it could be in the form of insights, in the form of data merchandise, or the form of the product references for an organization. To perform such tasks, you require complicated models, data visualizations or code generators.

Before data science, the term data mining was quite popular. It was then defined as the overall method of identifying valuable information from data. Data science was brought to another level in the 2000s, it was done by combining computer science with data mining. This led to the birth of social media platforms and websites, which additionally contributed to Big data. Technologies and tools like MapReduce, Hadoop, and Spark were launched to extract valuable information from big data.

So the rise in big data spiked the rise in data science to support the requirements of businesses to extract insights from their huge unorganized data sets. By this time, the journal of data science defined data as everything that is related to gathering, analyzing and modeling all types of applications. With the abundance of data and the birth of data science, it became possible to instruct machines with a data-driven approach. Let us look at various positions that are required to be filled in the field of administration, processing, and fulfilling of big data science. These positions include:

  • Data Engineers – these are the people who build and manage data gathering processes. They should be well aware of things such as Aforge.net, Java, and Scikit-learn.
  • Software Engineers – these are the people who understand what kind of data requires to be examined and develop the software to suit the industry. They should be well aware of things such as Java, SQL, and Python.
  • Artificial Intelligence Hardware Specialists – these are the people who understand the AI and what knowledge should be searched for in the pile of data sets. They should be well aware of things like Python, Java, and machine learning.

Role of a Data Scientist

Typically, as a data scientist, it’s your job to solve real company issues by gathering data. It doesn’t matter to the organization which kind of tools you use to complete your tasks. Their main goal is to understand their customers well and to deliver excellent results. Being a good data scientist doesn’t mean how advanced your tools are, it means the amount of impact that you can produce with your work.

A data scientist is not a data cruncher, he/she is a problem solver and strategist who are hired by companies to solve their most enigmatic and troublesome problems. They are expected to guide the company in the right direction.

The Need for Data Science

The first question that comes to the mind is what is the need to collect so much raw data and then extract information from it. So, let us learn why we need Data Science and why it is important. There is a cumbersome burst of raw data happening around us and all of it is not inappropriate. We can organize this raw data to make decision making easier. Businesses can use this raw data to derive meaningful insights. It can help them to know their customers better and to enhance their overall performance in the market. As the saying goes “Data is to products what electricity is to gadgets.”

Data Science VS Big Data VS Data Analytics

Data Science means using various tools and algorithms to extract valuable information from raw data. This raw data is drawn from different channels and platforms like cell phones, search engines, surveys, e-commerce sites, and social media. This field is related to the cleansing of data and then preparing and analyzing it. Data science is used in industries like internet searches, digital advertisements and search recommendations.

A huge amount of data is available to the world in the form of structured and unstructured sets, which is termed as Big Data. It means analyzing large sets of extreme data computationally to understand the patterns and trends of today’s civilization. This helps organizations to extract essential information from their data to discover and develop, which makes data a valuable asset for organizations. This helps in understanding human behavior which in turn helps businesses to understand their customers better. Big Data is applied in industries with financial services, communication and retail services or outlets. 

Data Analytics courses is the process of revealing hidden patterns and associations, latest business trends, customer preferences, and structured and unstructured data forms. Data analytics can be utilized in healthcare, travel and gaming industries. It helps in boosting the complete performance of the business by refining the financial processes. This increases the visibility of a brand and provides insights into it.

Data Science Courses and Eligibility

More and more companies are now realizing the importance of having an expert on board to analyze their company’s data. So, what are the requirements to become a data scientist? A career in data science requires an understanding of mathematics and statistics. There are many ways to become a data scientist. The most common way is doing a bachelor’s degree in IT, Computer Science, Math, Physics or another related field and then applying for a Data Science certification program.

If you want to specialize in a particular branch or domain then you can also opt for Data Science online training programs. Students gain practical skills and are given hands-on experiences with the help of assignments and programs. Students in data science courses are taught how to collect, interpret and analyze the data, which could help organizations all over the world to make intelligent decision making. They should have effective communication skills as well as high-level analytical thinking.

Why you should become a Data Scientist?

According to studies, there will be a requirement of more than 2.7 million data scientist job openings by the year 2020. In today’s job market, there is a lot of excitement and hype around the role of data scientists and why shouldn’t it be? It is a profitable job that ensures your security in terms of money. Apart from its economic benefits, it is also a discipline that affects our everyday lives in some way or the other.

Data science plays a unique role in improving the lives of people. It has the ability to set a course of an entire business just by analyzing the data that the company has been producing for years. It helps in detecting frauds in the banking sector by analyzing the data of financial institutions. The objective of data science is based more on scientific analysis rather than the notions and experience of just one individual.

Believe it or not, Data Science also helps in solving social problems. It will help you to bring a positive revolution in the country. Big companies like Google, LinkedIn and Amazon to small retail stores – all are looking for experts to analyze their raw data to useful information. The job of a Data Scientist has grown into the most demanding and flexible profession now.

Summary

Despite a growing interest in the field of data science, there is an acute shortage of skilled professionals with good professional skills. Organizations are in dire need of data scientists who can use the collected data to choose the most intelligent business path, dynamic processes, advanced product marketing, a better understanding of the opponent and all this turns into satisfying customer assistance.

The 8 Challenges to Networking a Factory

The age of the smart factory is here! More and more industrial processing facilities are hooking everything together, creating internal networks, to reap the benefits that these bring. The data-gathering and analysis-related functions of a networked factory can do wonders for long-term success and production efficiency. However, there are more than a few challenges.

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Whether you are acquiring supplies from otscable for a new facility or you’re looking to upgrade an existing one, there are issues you have to consider. Some of these might be obvious at the outset, such as the logistics of all this networking gear. Others might catch you off-guard. Here are the seven most critical challenges that occur if you intend to make a smarter factory.

Legacy Equipment

One concern that is highly practical is legacy equipment. Older machinery could come from an era when networking wasn’t important. These could be crucial to your operations, but also too old for a simple “plug and play” approach. This means that you are in desperate need to figure out how to blend the old and new, and that’s not always the easiest thing to achieve.

Unfortunately, there are instances when there is no solution. If you’re upgrading from an existing factory, you will have to settle for mixing the old and new and working as best you can. You can look into using third-party integration equipment and adaptors, which are your best option if a strict update and upgrade are out of the cards.

Physical Logistics

Another huge concern is simple logistics. Where do you lay out the cables? Where are the routers or switches installed? How far between support hardware are the cables running? This is something that you need to understand before you start placing equipment on the ground. Consider the layout and where your heavy machinery as you plan the placement of your networking infrastructure.

Security

Security is a concern. A smart factory collects a great deal of data about your operations, which might be highly sensitive. Protecting it and any insights gained from it is important for most factories and companies. One way to protect the data is to go for a wired network, which traditionally is much harder to infiltrate from the outside.

In general, you do not want to go with a smart factory until you have the security in place. You want layers of protection and authorization for your data. How you achieve that is up to you, though keeping the more sensitive data in a closed network, inaccessible from the outside without the right credentials, is a good first step.

Data Storage

Data storage is also an ongoing concern for smart factories. As operations are recorded down to their minutia, all that information has to be kept somewhere. Preferably, the storage occurs on-site so you don’t have to stretch the network too far and risk security issues. This means you need to account for the storage and the conditions that prevent the hardware from being damaged.

Factory Visibility

Visibility is also a concern. In the old days, you might have observers present but practically no real active monitoring. Most things were probably done passively. In a smart factory, you’re going from zero monitoring to thousands of devices and points collecting data all the time. This can be a staggering amount of information to process and may require a learning curve.

A related challenge to this is if multiple factories are interconnected. Even if you maintained visibility in one, being suddenly thrust into seeing all of your facilities in such detail can be staggering. This is something that usually takes a bit of time to get used to.

Outages

You’ll want back-up systems in place in the event of outages. Never assume that you will never have an outage, and set the network up so that it functions on its own even without internet access. Make sure that the most crucial parts of it can work and record data independently, even under outage conditions.

Edge Networking

Going closer to the “edge” might also be a challenge for you. Edge networks are when a single task is processed by multiple terminals across a network. This can be a serious challenge because it means that your internal network has to connect to a much broader one. This will require serious cooperation between multiple departments, facilities, and personnel.

The Right Tools

Finally, you have to look at the tools you intend to use. The market for devices and tools for smart factories is increasing, which is both good and bad. It’s good because you have more options available, so there are higher odds something that suits your needs is out there. It’s bad because there’s more chaff to wade through, more time needed to get the right ones.

Conclusion

Yes, it is challenging to hook a factory to a network and engage in the “Internet of Things.” There are challenges that must be overcome, logistics to consider, and costs to factor in. However, there are many benefits to gain, both from the network itself and by keeping up to date on the march of technology.