What is Data Science?

Well, Data Science can be defined as a large set of data that can either be structured or unstructured, which are then processed to gather information from it. You should understand that there is a massive amount of data that is always generated by the companies within each second and also needs to be processed. So, here, Data Science will bring together, store and even organize the data, which are then further analyzed by the data analysts to provide information. In a simple way, we can define Data Science as the study of massive data that are gathered and then processed to provide important information.

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What is Cloud Computing?

In Cloud Computing, a virtual environment is provided in which data or even information can be gathered through the internet. This technique allows us to get rid of the use of a physical server since most of the data can always be stored in the cloud separately through the help of virtualizations. Cloud can also provide a given platform that is used by a computer system or even a facility to run their programs.

There are a number of benefits that cloud computing offers since it’s flexible so that any business can easily move the workload to and from the cloud to ensure that the business strategies are executed.

Data Science Vs. Cloud Computing (Major Differences)

Here, we are going to discuss a few of the major differences between Data Science and Cloud Computing. Let’s have a look!

1.    Concept

In Cloud Computing, you can store and even retrieve data from anywhere at any given time you wish. But, Data Science that is related to big data is a massive set of data which will process a given set of data to provide necessary information when needed.

2.    Characteristics

In Cloud Computing, the services are provided over the internet, which is not limited to:

· Software as a Service (SaaS)

· Platform as a Service (PaaS)

· Infrastructure as a Service (IaaS)

Back to Data Science, some of the most important characteristics that can lead to strategic business plans are not limited to Velocity, Variety and even Volume.

3.    Accessibility

Cloud Computing offers universal access to all services. But, Data Science tries to solve a given technical problem and then offers better results.

4.    When to use

When a customer’s key objective is to find a rapid deployment and scaling of the applications, they will have to shift to Cloud Computing. The application works with a set of highly sensitive data and needs strict compliance and one should store things on the cloud.

Again, Data Science will be the traditional method, and its frameworks are even ineffective. Data Science is not a replacement for a relational database system, and it solves a given problem that is related to massive data sets, and most of the massive data sets always do not deal with small data.

5.    Cost

Well, in every comparison, the cost will never miss. And this was even to be our first point, right? All not lost still; we can still talk about it. Between the two options, which one do you think is cheaper? Well, Cloud Computing is considered to be cheaper since it has low maintenance costs, and also it has a centralized platform with no upfront cost and even disaster safe implementation. Data Science is a highly scalable, big ecosystem and is also cost-effective.

6.    Job roles and responsibility

Here, the users of the cloud are the developers or even an office worker in a given organization. However, in Data Science, there are several data analysts in big data that are responsible for analyzing and providing useful information such as dates, things that are interesting, sites and possible future trends in a given area.

7.    Types and trends

In Cloud Computing there are three trends that are not limited to:

· Public Cloud

· Private Cloud

· Hybrid Cloud

· Community Cloud

Back to Data Science, there are some important trends such as:

· Hadoop

· MapReduce

· HDFS

8.    Vendors

Do you know some of the vendors in Cloud Computing? Well, some of the vendors and solution providers of Cloud Computing are not limited to:

· Google

· Amazon Web Service

· Microsoft

· Dell

· Apple

· IBM

How about some of the vendors and solution providers available in Data Science? They are not limited to:

· Cloudera

· Hortonworks

· Apache

· MapR

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Additional Differences between Data Science and Cloud Computing

There are some other differences that you should also know about these two options to enhance your understanding. These differences include:

1. The main goal of Cloud Computing is to offer computer resources and services mainly through the help of network connections. Whereas, when talking about Data Science, it’s all about solving problems that are related to massive data sets by generating and processing data to provide valuable information.

2. In Cloud Computing, you should know that data is kept in servers that are also maintained by different service providers. You can always have access to these data with the help of the internet. While, in Data Science, massive data is broken and distributed across several different computer systems where the data is then later analyzed and processed.

3. Data Science solutions can always be deployed with the aid of a platform as a service or even software as service in software as a service with various components or even applications which do run majorly on Hadoop and are accessible. Back again to the platform, it’s a service that is Hadoop which is then offered to the customers.

4. Data Science uses the data which can be created before you buy an organization and helps with insights which can be of help to the business in the future. Cloud computing, as well as a flexible and fast service with respect to its deployments and ensures the key operations of the organization are successful.

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SUMMARY FOR DATA SCIENCE VS CLOUD COMPUTING

Data science and cloud computing are both advanced technologies, in fact, leading and very significantly playing key roles in the IT companies as well as the non –IT companies too. Their presence ensures the growth of the company. All of these are equally important to a company.

UNDERSTANDING DATA SCIENCES AND CLOUD COMPUTING

When both data science and cloud computing software technologies are used in a company, there is the likelihood of an increase in revenue of the company and reduction in the cost of investment in that, cloud computing helps in sorting the local software while data science helps in making business decisions. Data science uses big data; therefore, there is a need for cleaning, preparing and analyzing the data that is being worked on. The purposes for analyzing the data involves five aspects, that is, Volume, Variety, Velocity, and Veracity.

Cloud computing involves using network servers to store, manage, and control data. It is a migration from the traditional way of working and has a number of public, private, and hybrid nature. It is also good to note that not all types of clouds are suitable for everyone. They vary since they are different in terms of models and services; therefore, it is all about finding the right one based on your needs. It is therefore advisable that the type of cloud chosen or rather preferred to be first of all analyzed. All these clouds differ in the mode in which they work though they provide a user-friendly dashboard to the professionals. It, therefore, requires a constant polishing of skills through constantly reading and practicing the new evolutions in the field.

IN TERMS OF CAREER, WHICH ONE IS BETTER? DATA SCIENCE OR CLOUD COMPUTING?

Well, are you wondering which one you should opt for in terms of career? If I may ask, where does it taste like? Data Science or Cloud Computing? Well, just keep the answer for yourself. Here we are going to discuss these two options in terms of career.

It’s worth saying that both of these two options have a number of advantages, and it’s a perfect place you need to get into currently. No matter where your dice falls or which side of the coin you feel is better, all these two options are a go. Data Science boasts of a better scope while its rival the Cloud Computing, also boasts of a better market, and another important thing that you should know is that the salaries of these two options are escalating day by day.

Amazon, Microsoft, and Google are all good companies that are pushing for better data Scientists and Cloud Computing; hence you should be expecting a bigger industry day by day.

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Importance of Data Science with Cloud Computing

Do you think these two options can go hand in hand? Well, Data Science and Cloud Computing go hand in hand. A Data Scientist usually analyzes several types of data that are stored in the cloud. With the increase in Big Data, companies are beginning to keep massive sets of data online, and this needs the assistance of Data Scientists. Are you thinking about finding more about Data Science? If so, you can enroll for a live Data Science Certification Training that is offered by Edureka with a 24/7 support and lifetime access.

Well, can Data Scientists work in the cloud? Yes, he can. Let’s us have a look at the types of data that a data scientist can work on in the cloud world:

· Check on the structured, semi-structured, and even unstructured data

· Check on varied sets of data, irrespective of the format or even size

· Analyze a given data to draw insights or even future trends

Again, you should know that the problem with such data is always that they do sit in disparate silos. As the storage is currently cheaper and even open to source platforms and tools are also readily available for data scientists, the cloud is everything.

Cloud Computing and Data Scientist?

· Cloud computing usually aids a data scientist using the platforms that are not limited to Windows Azure, which can also offer access to programming languages, tools, and even frameworks. These can be free, as well as there are some that are paid.

· Data scientists are usually more comfortable when using MapReduce tools such as Hadoop to keep data and even retrieval tools that are not limited to Pig and Hive. They also use other languages, such as Python and Java, to write their programs.

· Usually, data scientists will use two types of tools that are open-source ones such as R, Python, Hadoop frameworks and even more scalable machine learning tools and even other commercially available ones like MS SQL, Tableau, Oracle RDB, and Business Objects.

· Provided the size of the data sets and the presence of tools and platforms, understanding the cloud is not only pertinent but also very critical for a data scientist.

Salaries for Data Scientists

Data Scientist is among some of the most important people that are needed when it comes to working on Big Data and Analytics. Just a simple comparison with other different professions will provide you with a clear picture of how things are changing.

Provided that the skill and expertise needed for data scientists are extensive and there is a shortage of such skills, this makes the compensation to be so competitive. As found by a survey done by Burtch Works, the average salary for a data scientist that has barely been working for 3 years could be up to $90,000, while when you move upwards to a managerial level, it can go up to $160,000 and even more. In this case, the compensation at the managerial level for a data scientist is considered to be more competitive than that for a Mid-level Big Data Professional as per the chat, which reiterates the face that makes data scientists have a more bright future.

Future of Data Science and Cloud Computing

Currently, organizations are investing so many resources in two aspects, and that is to stay profitable and go hand in hand with Big Data and ensuring that the data keeps staying in the cloud. Processing data and moving it to the cloud organizations provide several benefits amid tackling massive sets of data that are used in decision making and even reducing overall costs of infrastructure. In this huge demand for these two options, there are a number of dollars in here.

Other things that you should know about Data Science and Cloud Computing

What are some other things that you need to know about these two fields? Well, do you know that the US retail giant Walmart generates about 2.5 petabytes of data from about 1 million customers every single hour? Are you exactly wondering what a petabyte is? A petabyte is equal to 1 million gigabytes. You can also understand this by a 13 3 years HD video.

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If you consider that Walmart locations are open for business for more than 10 hours a day, we can approximate that around 130 years of HD video and 25 petabytes of data are collected every single day. Well, there are not many companies such as Walmart.

Let’s drop Walmart and think about smaller enterprises. Do you know that even smaller enterprises currently are also generating massive amounts of data? This makes it increasingly more challenging to take advantage of that much information.

All that said, data science is the key, but before we can proceed with data science, we must also acknowledge an important player, and that is the cloud and cloud computing.

Cloud Computing remains essential for Data Science in today’s world

Are you having difficulty understanding the advantages of cloud computing when it comes to data science? Well, let’s imagine a world full of massive data such as today, but there are no servers. If that were to happen, the alternative could be that firms would need databases that run locally, isn’t that so?

If this was to happen, we could experience several drawbacks that are not limited to:

· Manual intervention would be needed to retrieve data

· Your computer becomes a single point of failure to the analyses that you have worked on locally

· The processing speed could be the same as that of your processing computer

· High chances of working on limited data due to the limited computing resources that are available at your disposal

· Again, in this type of setup, you would not have been able to leverage real-time data to create recommender systems or even any type of machine learning algorithms that can’t work without live data.

You can’t imagine such a world, right?

Well, that’s why servers were invented. Do the servers have drawbacks too? Well, there are some drawbacks when it comes to using servers. These drawbacks are not limited to:

· Well, can you think of even one major drawback when we choose to use servers? Well, there is one common point that you may think about. The server needs space to be stored. A cloud is also basically someone else’s server; this doesn’t need any space.

· When you decide to set up a server infrastructure, it will be so expensive to purchase and set up. How about clouds? The cloud infrastructure already exists and is only waiting for your server consumption.

· As we have it, in-house data storage requires one to have a backup and more so in different locations, right? As opposed to servers, the cloud provides data everywhere, anytime, and are usually backed up across several servers all over the world

· Servers will need so much planning. For even fast-expanding companies, server needs could remain unpredictable for even up to the current quarter. If you choose to go for in-house servers, you will have to end up purchasing more servers that you really need at a given time. What about clouds? Here, you pay as much as you will consume.

Thank God we currently boast of clouds!

When comparing clouds with local servers, clouds take an early lead in all the areas and aspects that you may choose to look at them. Data scientists should decide and focus on creating better algorithms, testing hypotheses, and even taking advantage of all the available data without thinking about the small memory present in their computers.

Usually, data scientists have to wait for long hours for some of their algorithms to train, but if any of them chooses to use the cloud, he or she will have the option to pay more and have their job completed within a short period.

Do you want to take a step towards data science?

Are you thinking about starting your journey to becoming a data scientist? Well, that’s a good thought, and you need to begin by registering for a program today. You need to begin with the pillars with the available Statistics, Maths, and Excel courses. Again, you should proceed by building your experience with SQL, Python, R, and even Tableau. You should then later upgrade your expertise with Machine Learning, Deep Learning, Credit Risk Modeling, Time Series, and lastly, Customer Analysis in Python.

Conclusion

Both Cloud Computing and Data Science have truly altered how organizations and even humans operate today. All these two options need each other, and they go hand in hand, making it difficult to decide on which one is better than the other. All these two options are equally important.