The problem with batch data processing is that it doesn’t allow for real-time processing for systems like an ATM, online booking systems, credit card applications, or e-commerce platforms. Many modern methods and applications require far faster processing times to deliver the user experience needed.

And with stream processing, you’ll get better NoSQL real-time analytics to make informed decisions and power impressive application features and processes.

1. What is a NoSQL Real-time Database?

A real-time NoSQL database allows you to process data in motion or as you receive or produce data within an application. It’s far simpler and faster than the old way of querying and computing data once you store it within a database.

With stream processing, you can continuously pull analytics and query the data. Plus, you’ll be able to react to the data immediately with actions or machine learning that will remember the event and train the system on what to do if that event occurs again.

For developers, a NoSQL real-time database provides the following opportunities.

• React to events instantly: all actions and analytics update automatically to provide the most meaningful and valuable experience.
• Ability to process more data at faster rates: You can process higher data volumes at faster speeds because you don’t have to wait for batch data processing.
• Decentralized and decoupled infrastructure: you no longer need massive shared databases since each stream processing application can maintain its data.

2. Use Cases for Real-time Data Processing

To better understand the need for stream processing, here’s a look at a few valuable use cases where real-time data can have a significant impact on your application and processes.

• Recognizing fraudulent credit card charges at the event and blocking them immediately requires real-time data processing and intelligence.
• They are triggering push notifications to users based on their behaviors and actions within an application.
• I am making application adjustments based on real-time analysis or sensor data.

3. NoSQL Open Source and Commercial Platforms

If you’re looking for a database that powers real-time processing, the best NoSQL options for your applications are here.

a. BangDB

BangDB is a real-time NoSQL database that features native AI and stream processing with a multi-model approach to provide a robust database for modern applications. Stream data in real-time while training models and completing predictions based on the new data.

b. DynamoDB

DynamoDB allows you to modify records as you ingest them while triggering events. As an Amazon product, DynamoDB is a powerful tool to help you be more agile and deliver a seamless user experience, just like the online retail giant.

c. MongoDB

MongoDB has powerful real-time analytics capabilities so that you can act on your data and information quickly. It’s a popular option for financial services, government, high-tech, and retail businesses. It helps companies to scale rapidly while building a structure for real-time insights and actions.

d. Cassandra

Cassandra came from Facebook and its need for real-time processing to power the social media giant. It is the ideal real-time NoSQL database for applications where you write more data than you read. It is a column-based database with high performance and strong data consistency.

e. HBase

HBase provides real-time access to data using multi-structured approaches to storing it. Process the data in real-time and call upon stored data quickly for responsive applications. This database is frequently used in medical, sports, e-commerce, and the oil industries.

The post NoSQL Databases Overview: Open Source And Commercial Platforms appeared first on Technology News & Trends.

This is where DevOps professionals have created value for businesses. Utilizing the philosophy of DevOps, organizations can massively boost their productivity. This article will learn what DevOps is all about and how to get the necessary certification.

1. What is DevOps?

DevOps is a collective group of concepts that have been gathered and compiled through various best practices regarding the technical community and field. The basis of DevOps is the Agile Framework for managing businesses and organizations.

DevOps aims to bring together a spirit of collaboration and cooperation while developing the skills of the entire staff. This ideology needs to be followed through all the stages of the life cycle of the business.

Under DevOps, the Development and Operations are not separated and continue working as a united team, working across the entire life cycle. DevOps sometimes also integrates the Security and Quality Control pillars into the mix.

Together, the couples find new and ingenious ways to automate the age-old manual tasks to utilize the powers of technology and boost the productivity of the business.

2. Benefits of DevOps:

• Improving Speed: DevOp aims to improve the lag and downtime between the operations of the business. This is achieved by shaving away wasted time wherever possible.
• Smooth Delivery: Just developing the service isn’t enough. The delivery of the service in time to the customer is equally important to be properly deployed and implemented.
• Reliability: DevOps as a framework creates an environment of reliability. By creating a continuous feedback loop of integration and answerability, the system becomes more and more reliable.
• Scalable: Scalability is something that many different organizations end up struggling with. Beginning from a small company to a big business has many other challenges. DevOps philosophy is helpful for all, irrespective of their size, which automatically makes it a scalable and universal framework.
• Secure: By integrating the Security departments of businesses, you can ensure there is no leakage of securities. Similarly, the clients’ data privacy is equally essential because it can cause considerable losses to the systems in place.

3. How to get DevOps Certification:

DevOps training can be done through both online and offline means.

To get the certification, all you need to do is to get the required training. Once you get the training, you can then receive the necessary certificate. These certification courses are usually a few months long and will give you a detailed explanation of the entire subject.

Getting a DevOps Certification is a great deal for business professionals interested in IT that they can use to boost their credentials and bag that fantastic job!

The post Should you get a DevOps Certification? appeared first on Technology News & Trends.

Although it puts you in a great position and helps you get noticed by potential employers, something precisely does yourself plan on making with that data science degree?

Here we will look at any of the numerous popular career pathways to embark on once you have your data science degree. Each job will use your education and skills and provide a unique work environment, challenges, and rewards.

1. Data Scientist

That is possibly one of the common ways to take with your data science degree, and it could very well be precisely what you have in mind. According to the Indeed Career Guide, data scientists average $122,338/year, which will vary based on the employer, skill level, industry, and job location.

Doing a data scientist will be your job to compile, make sense of, and analyze all kinds of data – often in vast quantities at a time. This takes a strategic and trained mind, and you need to have a robust eye for detail.

2. Machine Learning Engineer

A machine learning engineer’s career may sound quite high-tech and even a bit futuristic, but this isn’t a new role. In this job, it will be up to you to put together data funnels and then give your client or employer software solutions that address their needs.

This means that you need to understand big data and have that analytical mind; you also need to have software engineering and programming skills. In other words, this is a very advanced job.

Unfortunately, the US Bureau of Labor Statistics doesn’t give machine learning engineers their category. Still, due to the skills, job requirements, and the educational backing, you can expect a mean salary of just over $111,000 (as per PayScale).

The fact that this combines information research and computer research makes it all the more in-demand and is a field that can expect some real growth in the next few years.

3. Applications Architect

Here’s an exciting career that will have you working with a variety of applications. It will be up to you to design the actual application’s architecture, right down to the infrastructure and interface, and then track how it is used.

It’s not enough to build it; you also need to analyze how it works, how people are using it, and how it interacts with other critical applications. This is a highly technical position, and because of the reliance on various applications by companies of all sizes, you can expect this career to grow in demand.

4. Data Architect

A data architect may not be the job you are used to hearing about, but this can prove to be a fascinating path to follow in reality.

Once again, it makes use of your degree as it will be up to you to design analytics applications that are meant to be used in a variety of ways, and across a variety of platforms. In other words, you are designing and creating a database system for your company or client.

This also means you can work for a private company, in a large corporation, or even open up your consultancy.

5. Data Analyst

Just as the job title implies, as a data analyst, it will be your job to practice data analysis tools to create knowledge of raw data. This data is then used to help shape the company’s direction and the decisions it needs to make.

Some of the things you may be working on in this job include removing corrupted data, using various computerized tools and models to extra data, putting together reports, and doing a complete analysis of data.

Data analysts are employed by manufacturers, banks, colleges and universities, consultancies, software development companies, and so much more.

6. Data Engineer

A data engineer is probably one of the most well-known jobs that use your data science degree. Most people have probably heard of this career path, but that doesn’t mean they fully understand what it involves.

A data engineer is responsible for real-time processing, or what’s called batch processing, to be conducted on data that has been stored. Among other things, it will be up to you to make sure that the data is readily available for those that need it when they need it.

7. Statistician

Here’s a growing field at the moment; in fact, the US Bureau of Labor predicts statisticians’ domain to grow by 33.8% by the year 2026. It’s always good news to hear that the field you have your eye on is growing, thereby offering employment opportunities. As for the median pay, it was listed at $92,030 as of 2019.

In this position, it would be your responsibility to analyze data and then use mathematical practices and techniques to make sense of the data and solve questions.

If you’re wondering where you could work, the research and development fields are the biggest employers of statisticians. This R&D spans across the life sciences, engineering, and physical industries.

8. Business Intelligence Developer

Should you always wonder what goes into the business-making decision in various companies? Where do they get their information from, who decides, what is that decision based on? A business intelligence developer can undoubtedly help to answer these questions.

As a business intelligence officer, it will be up to you to put together the ideal strategy for your client to find any data or information they want quickly and easily. This means designing and creating the system.

9. Information Officer

Here’s a fancy title that sums up that of a data officer. You will be maintaining, managing, and organizing a database with digital tools and resources in this particular role. Depending on the corporation you work for, and the business, this could be substantial data quantities.

At the same time, it will be up to you as the information officer to ensure that the data is always easily accessible to those who need it and have clearance and that it is highly protected. In today’s world, where cyber threats are in abundance, security, and protection are becoming critical and frankly tough to achieve.

10. The Field Spans Across Many Industries

As you can see by these top career paths, a data science degree is one that can set you up to work in a variety of industries. As you start to think about it, pretty much every company needs to collect and study data at some point, as it is required to make those big decisions and set goals.

With that being the case, it shows just how important this field is, and that demand won’t be fading any time soon.

With average salaries that range from about $60,000 to $135,000/year, there is plenty of opportunities to make a good living. And, of course, the longer you are in the field, the more experience you gain, and the higher the salary you can start to demand.

There is also an opportunity to work your way up the career ladder, which means you don’t have to worry about your career becoming stagnant or boring.

11. Do You Have What It Takes?

As you look through this list of career pathways that open up with a data science degree, you may also ask yourself if you have what it takes to succeed in the long run. You can click here for an in-depth infographic that discusses this exact question among others.

It can help you to determine if data science makes sense for you if you have the right skills, how to succeed, and some of the entry-level jobs that you can get into right out of university.

The careers listed above tend to be the next step up and won’t necessarily be the job you land straight out of university. Just like with so many industries, it can take time.

12. A Robust, Exciting, and Growing Industry to Break Into

A data science qualification can unlock the door to a very robust, exciting, and growing industry that offers real promise not just for right now but heading into the future. You’ve got lots of different paths to look into that appeal to various skills and talents, giving you all the control.

The post The Top Career Pathways for a Data Science Degree appeared first on Technology News & Trends.

Over the years, big data have increased so much that the traditional methods of data analysis and extraction are not producing significant results.

In different terms, there is also to it that is missing. So data science here comes into play which promises advanced techniques and concepts of using data. Data science consulting services have seen growing importance over the years because it has helped make significant business decisions.

Also, it has automated repetitive, time-consuming tasks with the help of machine learning and AI. All these methods result in better operations of the business, which overall improves the wealth of the organization.

Curious to know why Data Science has proven beneficial to organizations? This article explains data science and why taking up a Data Science online course is a worthy option.

1. Who is a Data Scientist?

So far, we have known why data science has seen growing importance. Therefore data science is the application of advanced methods and concepts of extracting meaningful information from data and putting it to the user for business decisions.

Consequently, the data scientist is the professional who practices the tools and techniques of data science and applies them to resolve business challenges. The skills required to become a data scientist are listed below:

• Statistical methods and tools: Analyzing the data is the critical initial step.
• Machine learning: building algorithms and implementing them so that the computer/software automatically learns from them.
• Computer science: database systems, artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
• Programming: computer programming languages such as Java, R, Python, and SQL are the languages used popularly for data science. The algorithms are constructed with its help of it.
• Data storytelling: communicating the process involved in simplified terms to the non-technical audience.
• Business intuition: connecting with all the involved stakeholders to dig deeper into the business challenges and find long-term solutions.
• Analytical thinking: finding analytical solutions to abstract business problems.
• Critical thinking: before concluding, make sure that the objective analysis is performed.
• Inquisitiveness: digging deeper into the resources to discover more about the solutions.
• Interpersonal skills: communicating the work with all the levels of the organization.

2. Roles and Responsibilities of Data Scientist

The roles and responsibilities that a data scientist performs are listed below:

Data Analysis

It is a crucial initial step that data scientists take. It involves analyzing data with the help of statistical tools and techniques. The more analysis is done, the better decisions are taken. Some of the data mining techniques, such as pattern detection, graph analysis, decision trees, clustering, or statistical analysis, are important.

Work with Stakeholders

The data scientist observes the techniques and resources used to take out data and what system is being followed for its use. After that, he collaborates with the people involved in these processes.

This is beneficial because the data scientist gets to know detailed information about where the data is being utilized and in what ways.

After getting to know the details of the data, the next step is the creation of data models databases, big data, etc. Here also, the different stakeholders should be made aware of all such work. Involving them will lead to better solutions in the future.

They advise companies on their data potential. Searching for new insights and then transforming them into business goals. They are performing advanced statistical analysis, data mining, and visualization technologies.

Collaboration with stakeholders requires you to have some essential communication skills. Make sure that the information you are trying to find out is available in the sources that you believe. Ask questions and try to extract more information about the data so that the problems are solved effectively.

A data scientist is different from a data analyst. While data scientists also analyze data with the help of statistics, their work has more to it. They apply a creative and artistic approach to their work processes. They believe in getting data from multiple resources and believe in acquiring ideas from out of the box also.

3. Conclusion | Role of a Data Scientist

We know that the work of data scientists is spread across vast areas; after all, it’s quite a challenging and demanded role across companies.

According to payscale, the standard payroll of a data scientist is ₹698,413 in India. The top companies that hire data scientists are Microsoft, Facebook, IBM, Amazon, Google, Apple, and Oracle.

This shows how much data science is essential, and organizations are willing to pay a reasonable amount for it. That’s why, if you are a computer science graduate or a statistician, or even a management student, you can start a career in data science.

All it requires is learning the right amount of skills from industry experts and participating in real-time projects. You can enroll in an online course which will be cost-effective as well as you need not worry about the location, which becomes an obstacle sometimes.

The post The Role of a Data Scientist appeared first on Technology News & Trends.

Undoubtedly, Big Data has closely integrated with the financial services industry, and it’s very likely to keep on driving future innovation. Stay with us to learn what is the role of Big Data in finance, and which significant challenges it might present.

1. Big Data in Finance: a Brief Overview

Being a real buzzword these days, the term ‘Big Data’ entails large volumes of data that carry on increasing exponentially, as well as approaches to its collection, processing and analytics. According to IBM data scientists, Big Data is based upon ‘4V’s’ volume, variety, velocity and veracity.

Work means the scale of data; combination implies its diverse forms, the rate stands for the acceleration at which data is the store and analyzed, and finally comes integrity, which might be questionable.

It comes without saying that the financial services industry is one of the most data-intensive sectors of the global economy, so the role of Big Data in this area is enormous.

2. How Big Data Is Disrupting Finance

Big Data gives a competitive advantage to companies, enabling them to work faster and more efficiently. In this section, we’ll walk you through the most common application of Big Data in the financial sector.

a. Customer Acquisition and Retention

Let’s be honest: the consumer has always been the critical point of any business. Therefore, banks and financial markets leverage Big Data to collect information that helps identify trends and get insights for predicting customer behaviour and providing better service.

By understanding customer needs, companies get clues on how they can generate more leads, improve their products and services, and increase customer satisfaction. BankMobile, a digital U.S. bank, which partners with universities to get new clients, uses Big Data to easily monitor the effectiveness of these partnerships, its customer acquisition costs, fraud, and also process whopping amounts of customer feedback.

b. Smart Stock Market Insights

Big Data has changed how stock markets across the globe used to operate, as well as the approach to making investment decisions.

Machine learning provides accurate forecasts at lightning speed, enabling analysts to make the best decisions. In essence, paired with algorithmic trading, Big Data looks incredibly promising for the trading sector.

c. Enhanced Analytics

Big Data analytics is here to enhance predictive modelling for more efficient assessment of the outcomes on investments. Big Data, coupled with improved algorithmic understanding, gives way to more accurate predictions and the ability to alleviate issues related to trading.

d. Identification of Potential Risks

As finance industry players operate in high-risk environments, they are in constant need of risk management solutions.

Big Data is a fundamental mechanism in risk management as it copes with evaluating data from multiple sources. This approach allows for tracking, detecting, and alleviating risks that might exert adverse effects on the operations.

e. Fraud Management

With predictive analysis, Big Data allows for detecting fraudulent activities, and many pioneering organizations have already adopted this approach.

For instance, Alibaba Group developed a fraud risk management system that is based upon real-time Big Data processing. The system analyses large volumes of consumer data in real-time and detects fraudulent transactions.

3. Big Data Challenges

a. Data Growth

The most significant challenge related to Big Data is its enormous volumes that might be difficult to store and analyze. Also, a great deal of data is unstructured, like documents, audio or photos, e.i.

It doesn’t reside in a database and can be challenging to search and process. Therefore, banks have to invest in the Big Data technologies to support the new use cases, for instance, in NoSQL.

It includes an array of different database technologies that provide a mechanism for accumulation and retrieval of data. A NoSQL database can store unstructured data and give a faster performance, offering flexibility while handling multiple types of data at a massive scale.

b. Big Data Consolidation

Next, data typically exists in separate systems, which makes it difficult to centralize. To extract data from those systems, Big Data requires a near real-time delivery, presenting a challenge for Fintech.

c. Potential Security Issues

Security is another concern related to Big Data. Because of data centralization out of the protected systems, data security might become questionable. Plus, Big Data stores can become sensitive to hacker attacks. All in all, the financial industry needs reliable solutions to ensure maximum data security.

d. Managing Data from Diverse Sources

Also, Fintech has to face the challenge of data integration as the data you need to interpret usually originates from various sources and in multiple formats. As an example, an eCommerce company needs to analyze data from website logs, social media and call-centres. These data formats differ, and it can be challenging to integrate them.

e. Maintaining Regulatory Compliance

Financial institutions have to keep an eye on the legal restrictions that apply to data collection and processing. For instance, the European Global Data Protection Regulation requires that organizations can store personal data only when there is a direct use for it, the customer being in full control of the data.

4. Summary

In essence, Big Data is genuinely revolutionizing the financial services industry, allowing for improved analytics, anticipating risks and fighting fraud. Still, some challenges need to be tackled to expand efficiency and provide better solutions.

The post Big Data in Finance services – Overview, Applications, Challenges appeared first on Technology News & Trends.

Find out more about four ways that donor relationship management software and a Classy Salesforce integration or other platform-specific solutions from Blackbaud integration partners can help your organization get the most out of donor data.

1. Make Sure Your Donor Database Is Updated

The first approach to data analysis your organization should take before a fundraiser involves checking donor records for completion.

If you have a list of attendees or donors, start by checking these records for missing information. It may be possible to improve the quality of data by correcting erroneous entries or supplying missing information.

The second approach toward analyzing current data in advance of another round of fundraising pertains to giving records. An organization that uses donor or customer relationship management software or other nonprofit management software should be able to track the frequency of providing for each donor, average donation amounts, and donors’ preferred giving channels.

An updated database is essential for accurate analytics that can significantly increase the effectiveness of charitable appeals. High-quality data empowers an organization to communicate and solicit donations more effectively while gaining more data that can then be used to inform targeted approaches to raising funds.

2. How To Maximize Your Data Analytics

Once an organization is working with the most current donation dataset, a more pragmatic fundraising approach is possible. The third way to maximize the benefit of donor data through analysis involves using donor management software to segment a full donor list into subgroups.

Targeted tasks can then be directed to each group or set of groups. Depending on the fundraiser type, an organization may want to take a specialized approach to target supporters, such as fundraising event attendees, members, or major donors.

The fourth approach to fundraising data analysis pertains to the event platform. Ensure that you are fundraising on the right channel to reach and obtain data from as many participants as possible.

Depending on the cause, a peer-to-peer or social media approach may be most effective for getting a broad audience and obtaining useful data.

Segmentation and platform-based considerations can improve donor response rates and drive up contribution amounts. Robust nonprofit software and integration tools make these and many other data-driven approaches to fundraising planning possible.

As you reach more supporters, it is essential to scale the same level of attention to records’ completeness to maintain the value of these insights.

These four measures can help your organization maintain and benefit from a more accurate database of donors. Clean data is more effective for a wide variety of uses, from donor communication to pre-fundraising analysis.

The ability to continuously generate donor data and track participation over time is one of the most potent donor relationships and nonprofit management software capabilities.

Taking a more analytical and data-driven approach to fundraise can help your organization maximize its operational budget.

The post 4 Ways You Should Be Analyzing Your Donor Data Before a Fundraiser appeared first on Technology News & Trends.

Unsurprisingly, research shows that a third of modern businesses’ IT expenditure is used to create cloud infrastructure. Further, with businesses struggling for competitiveness and a demand to reduce costs and improve efficiency, numerous cloud solutions are now available.

With such applications becoming a necessity in everyday tasks, most organizations move toward custom cloud apps development for efficiency and productivity.

While adopting such apps is inevitable in your enterprise, you need to understand the diverse apps and computing services before you settle on any. Here is a brief overview of how cloud applications and services started.

1. What Is a Cloud Application?

A cloud application is any computer application accessible through programming interfaces (APIs) and web browsers operated from the cloud. Usually, remote servers are used to store data and offer the functionality and interface of these apps.

Google Docs is an example of a common cloud application. Your team members can access any Google Docs files remotely provided they have an internet connection and a web browser compliant device.

2. History of Cloud

When we talk about cloud apps and related services, most people think it is a 21st-century concept. However, while the technology boomed starting the 2000s, cloud systems were already available in the sixties.

a) Computer Applications Premise

The concept of cloud apps, storage, and sharing can be traced back to mainframe computers. The processors used during this period were costly and required expensive maintenance. They were also huge. Businesses would adopt a time-sharing approach to the main computer using several connected stations.

This idea of computationally sharing power formed the starting point of the technological concept used nowadays. Presently, an unlimited number of people can share and access a cloud seamlessly.

b) Computer System Interconnections

The 1960s’ innovation saw an advancement in the cloud concept to include the development of communications networks. For instance, the Advanced Research Projects Agency Network (ARPANET), a brain-child idea of J.C.R Licklider, launched in 1969. To date, Licklider’s innovation remains a key part of the growth of the internet. The network ensured the sharing of digital sources across distant computers.

c) IBM’s Operating System

The evolution of cloud applications continued in the 1970s. This period saw the development of virtual machines. A perfect example of this was IBM’s VM OS operating system that was introduced in 1972.

This evolution gave way for organizations to use private networks. Subsequently, in the 1990s, technological advancement led to the introduction of cloud infrastructure.

Despite such advancement, cloud technology faced significant limitations. For instance, the internet speed was quite slow up until the nineties.

d) Cloud Apps and Services in the 2000s

With the cloud technology now in the public domain, various developments were seen. For example, Amazon launched its web-based retail services on a cloud infrastructure model in 2002.

After around four years, they launched Amazon Web Services, including the Amazon Mechanical Turk and the Elastic Compute Cloud (E2). These applications offered individuals a chance to host their applications and programs.

Before the end of 2006, Google Docs, a commonly used platform today, was launched by Google. Users could type, edit, save, as well as share files across team members remotely.

Besides, several universities collaborated with tech giants – the likes of IBM and Google – in 2007 to develop a research projects’ server farm. The same year, Netflix introduced its video streaming service, which allowed people to stream movies and videos.

e) Modern-day Cloud Applications and Services

Today, businesses and consumers alike have adopted cloud technology at an alarming rate. Unlimited companies are now using cloud modules to ease daily tasks and improve efficiency.

Cloud-native applications such as the Oracle Cloud, which offers XaaS services, have gone a long way in making cloud computing efficient today.

3. What’s the Benefit of Cloud Applications Advancement for Businesses?

Cloud software has developed at high speed, which is beneficial to any modern business.

So, can enterprises and regular customers get benefits?

Advancements in this technology have made it effortless for any individual to save and access information from any location.

The unavailability of cloud technology and storage, both mobile and desktop, would be running out of space fast.
Businesses would incur high costs to buy, install, and transfer programs to new devices.

There is no doubt that cloud technology has evolved significantly in recent years. Today, enterprises can rent cloud applications and develop their private cloud systems, which act as data centers.

Most experts project that cloud computing will be instrumental in developing hybrid IT solutions such as the Internet of Things. This will go a long way in making businesses more efficient and productive.

The post It’s Cloudy: A Short History of Cloud Apps and Services appeared first on Technology News & Trends.

1. What is Data Science?

Traditionally, the data that we used to have was mostly in a structured form and were small in size and could be easily analyzed and handled manually or simple devices. But as we have seen the growth of technology, we have also noticed that the data that is used today is unstructured or semi-structured.

As a massive amount of information is produced daily, simple tools have failed to be sufficient for tackling this data. This is why the need for more advanced analytical tools and algorithms for processing data and drawing insights from data after data analysis. The importance and purpose of data science technology are discussed below to help you understand what science is.

2. The Purpose Of Data Science

The ultimate purpose of data science is to find patterns in a set of data. To interpret and draw insights from the data, data science technology uses various statistical techniques. From data extraction to handling or manipulating data to pre-process it, the data scientist jobs involve all of them and inspection of data at the same time.

From having the responsibility of making predictions to concluding analyzing the given set of data, the data scientist jobs involve these activities to make the companies take important and smarter business decisions.

3. Importance of data science

As stated earlier, various industries produce a large amount of data regularly, and data science technology is needed for making careful decisions. Data science transforms raw data into meaningful information, and this is where the importance of data science lies.

While the importance of data science is based mainly on the analysis and management of data, it solely depends on the area of expertise of a particular company where an individual data scientist is working.

This automatically means the data scientist should have domain knowledge of that specific area. Companies need data science technology for creating data-driven decision models and also for creating a better customer experience. The data science applications are way too many. There are some sections where it is used majorly by these companies. Some of them are mentioned below.

4. For Marketing:

Nowadays, it’s all about marketing, and companies take the help of data science technology to plan better marketing schemes and create better advertisements that help them sell their products.

There are many times when advertising in the traditional way or using the same old marketing projects has led to a considerable amount of expenses and ultimately have failed to produce the results the way it was expected.

This does not happen when data science comes into the discussion. One of the primary jobs of data science is data analysis. The same thing is done here by analyzing the feedback from the customers and figuring out the customer’s behavior.

This helps them in changing or modifying their techniques for advertising. Monitoring the recent trends also helps recognize the needs of the market and thus create plans for advertising and marketing campaigns accordingly.

5. Understanding the customers:

Data science allows these companies to gain customers by analyzing their needs and allowing them to make only those available products suitable for potential customers. Data science is the key for these companies to understand their clients and therefore help them understand the needs and deliver products keeping that in mind.

6. For Innovation:

If the products can be delivered according to the customer’s needs, those needs can also help the data scientist modify or bring in essential changes in the products. The importance of data science is seen here as data scientists analyze and create insights from the conventional designs but looking at it in a whole new way. This helps the company in making decisions and head in the right direction.

7. For Enrichment of lives:

As have been stated repeatedly, the customers’ data in the form of their feedback are needed for serving them properly. In the healthcare industry, the importance of data science lies in assisting patients with their needs.

Data scientists analyze a particular patient’s historical data or disease and create products that help them tackle the problems they are currently facing.

So, we see many sectors or industries where the importance of data science is much more than what is known to us, depending on the needs or ways in which a particular industry works.

We can now say that data science technology forms the backbone of the industries that deal with massive amounts of data as the data science applications are many.

Data science plays this vital role in making the business grow for the better and show the profit that is very much required of the company.

Given the data analysis and insights provided, the data scientist helps the companies adopt appropriate strategies and make them suitable for the customers.

Covid-19, a word that is enough to define the present situation of our world. The COVID effects are massive, and there has been no area that is spared from it. Therefore, it is quite natural that the thoughts of the future of our career have often crossed our minds.

Whether we like it or not, the situations are getting from bad to worse, and the financial status, given our present situation, is a matter of concern for all of us. It doesn’t matter whether you are a working professional or a job aspirant, it is time to sit back and think about making your situation better, and the solution is quite simple.

It is time to choose a career (a different one if you are already associated with a field) that is secure and stable than other career options. Data science technology is that option that will secure your financial future. We are about to discuss the reasons for just that.

8. How is data science helpful for a career post the pandemic

As the demand for data science has increased in recent years, so has data scientists’ value. The data science jobs are one of the highest paid jobs in the technology industry right now. Data scientists demand top-notch benefits and high salaries because of the unique skills that they possess. Let’s see why it is considered so.

9. What does a data scientist job offer?

According to a statistics report, data science jobs are likely to grow up to thirty percent by 2026. A famous software company has declared data science technology to be one of the best career options to endure recessions.

These high-paid and efficiently trained professionals become a useful resource for optimizing data, reducing waste, and looking for appropriate solutions to complex problems. In a crisis like this pandemic, many believe that stability is what we seek, and a data scientist’s job might just be that stability.

10. To have a new beginning

As said earlier, if you are a working professional and this current turn of events has not worked in your favor, then a change in the direction that your career take might make or break your future financial status, and there is no other way to do this than to learn data science. Moreover, there is an easy way to do this that does not even involve leaving your house.

Nowadays, there are multiple online data science programs, and these data science courses are worth it. These data science programs are rewarding as these are competitive and challenging ways to learn data science.

These data science programs make you have a career or lead your way towards one even when the economy is facing a rapid downfall. These online courses are also helpful in making you learn data science, which comes under learning new skills that will be beneficial for your career.

11. Need for the remote areas

The mode or ways of working are changing with this rapidly changing world. From the leading companies to the small scale industries or even in the traditional companies, people prefer to work from home rather than in their previous offices to stop the virus from spreading given the situation that we are currently facing.

This new working mode might be a permanent solution, and the working professionals would most likely continue working like this even after the pandemic subsides. If that is the case, then data science jobs can be quickly done in remote areas. Many big companies are looking for data scientists who are flexible or are comfortable doing data science jobs in any part of the world and are hiring them always.

There is no guarantee of how long this Covid-19 situation will last. Still, it can be assumed rightfully that the technology industry will be affected less when compared to other sectors, thus securing data science jobs more than other jobs as data science jobs can be done remotely.

Not only this, but the companies which already exist will now heavily depend on data scientists for new and innovative ideas and strategies for their products that will help in fighting this pandemic situation or something related to that.

12. Conclusion

As shown in the above sections, data continues to prove itself as promising and successful career paths, especially when it comes to the technology industry. There is a consistent rise in demand in the market for professionals who are skilled in this field or anyone of its subjects.

At present, successful professionals understand the need to know new technologies beyond their traditional ones and widen their spectrum of these skills after all data scientists must sharpen their skills to analyze large amounts of data, data mining, programming skills, and many such things. Data scientists have proved to be valuable assets in the past years and are present in almost all industries.

These data scientists are highly data-driven individuals with skills capable of building complicated quantitative algorithms to manipulate and analyze a vast amount of data used to give solutions to problems and plan new schemes.

Data scientists must possess knowledge in specific technical and non-technical areas that includes R, Python, Machine learning, and many such domains. Therefore, it only becomes necessary for you to learn data science or these skills to make a data science career.

There are many institutions available that can be excellent learning sources when it comes to data science. So, choosing a data science course is the option that becomes most suitable to help you get a job as these data science programs make sure of that.

However, it becomes necessary to choose an institution that lets you pursue a data science course that helps you learn data science and secures your career in data science. The institution that comes right into mind is Great Learning. Great Learning data science courses are recommended by one and all.

In a short period, Great Learning has managed to gain quite a good reputation of its own. So, if you want to learn data science in the best way possible, Great Learning is the institution to go to as it has several data science programs that will help you grow and shape an outstanding career in the future.

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Python is a programming language widely used in many fields. It is considered as the king of the coding world. Data scientists extensively use this language, and even beginners find it easy to learn the Python language. There are many Python data science courses that guide and effectively train you to determine the python language.

1. What is Python?

Python is an interpreted and object-oriented programming language. It is an easily understandable language whose syntaxes can be grasped by a beginner quickly. Guido found it in 1991.

It is supported in operating systems like Linux, Windows, macOS, and a lot more. The Python is developed and managed by the Python software foundation.

The second version of Python was released in 2000. It features list comprehension and reference counting. This version was officially stopped functioning in 2020. Currently, only the Python version 3.5x and later versions are supported.

2. Why is Python used in data science?

Python is the most preferred programming language by the data scientists as it effectively resolves tasks.

It is one of the top data science tools used in various industries. It is an ideal language to implement algorithms. Python’s sci-kit-learn is a vital tool that the data scientist finds useful while solving many machine learning tasks. Python data science courses uses Python libraries to solve a task.

Python is perfect when it comes to scalability. It gives you flexibility and multiple solutions for different problems. It is faster than Matlab. The main reason why YouTube started working in Python is because of its exceptional scalability.

a) Features of Python language

• Python has a syntax that can be understood easily.
• It has a vast library and community support.
• We can quickly test codes as it has interactive modes.
• The errors that arise can be easily understood and cleared quickly.
• It is free software, and it can be downloaded online. Even there are free online Python compilers available.
• The code can be extended by adding modules. These modules can also be implemented in other languages like C, C++, etc.
• It offers a programmable interface as it is expressive.
• We can code Python anywhere.
• The access to this language is simple. So we can easily make the program working.

3. The different types of Python libraries used for data science

a) Matplotlib

Matplotlib is used for effective data visualization. It is used to develop line graphs, pie charts, histograms efficiently. It has interactive features like zooming and planning the data in graphics format. The analysis and visualization of data are vital for a company. This library helps to complete the work efficiently.

b) NumPy

NumPy is a library that stands for Numerical Python. As the name suggests, it does statistical and mathematical functions that effectively handles a large n-array. This helps in improving the data and execution rate.

c) Scikit-learn

Scikit- learn is a data science tool used for machine learning. It provides many algorithms and functions that help the user through a consistent interface. Therefore, it offers active data sets and capable of solving real-time problems more efficiently.

d) Pandas

Pandas is a library that is used for data analysis and manipulation. Even though the data to be manipulated is large, it does the manipulation job easily and quickly. It is an absolute best tool for data wrangling.

It has two types of data structures .i.e. series, and data frame. Series takes care of one-dimensional data, and the data frame takes care of two-dimensional data.

e) Scipy

Scipy is a popular library majorly used in the data science field. It does scientific computation. It contains many sub-modules used primarily in science and engineering fields for FFT, signal, image processing, optimization, integration, interpolation, linear algebra, ODE solvers, etc.

4. Importance of data science

Data scientists are becoming more critical for a company in the 21st century. They are becoming a significant factor in public agencies, private companies, trades, products, and non-profit organizations. A data scientist plays as a curator, software programmer, computer scientist, etc.

They are the central part of managing the collection of digital data. According to our analysis, we have listed below the significant reasons why data science is essential in developing the world’s economy.

• • Data science helps to create a relationship between the company and the client. This connection helps to know the customer’s requirements and work accordingly.
  • Data scientists are the base for the functioning and the growth of any product. Thus they become an essential part of doing significant tasks .i.e. data analysis and problem-solving.
  • There is a vast amount of data traveling around the world, and if it is used efficiently, it results in the successful growth of the product.
  • The resulting products have a storytelling capability that creates a reliable connection among the customers.
  • This is one of the reasons why data science is popular.

It can be applied to various industries like health-care, travel, software companies, etc.

• Big data analytics is majorly used to solve the complexities and find a solution for IT companies’ problems, resource management, and human resource.
• It greatly influences the retail or local sellers. Currently, due to the emergence of many supermarkets and shops, the customers approaching the retail sellers are drastically decreased. Thus data analytics helps to build a connection between the customers and local sellers.

Are you finding it difficult to answer the questions in an interview? Here are some frequently asked data science interview questions on basic concepts

a) Q. How to maintain a deployed model?

To maintain a deployed model, we have to

• Monitor
• Evaluate
• Compare
• Rebuild

b) Q. What is the random forest model?

The random forest model consists of several decision trees. If you split the data into different sections and assign each group of data a decision tree, the random forest models combine all the trees.

c) Q. What are recommendation systems?

A recommendation system recommends the products to the users based on their previous purchases or preferences. There are mainly two areas .i.e. collaborative filtering and content-based filtering.

d) Q. Explain the significance of p-value?

• P-value <= 0.5 : rejects the null-hypothesis
• P-value > 0.5 : accepts null-hypothesis
• P-value = 0.5 : it will either except or deny the null-hypothesis

Q. What is logistic regression?

Logistic regression is a method to obtain a binary result from a linear combination of predictor variables.

Q. What are the steps in building a decision tree?

1. Take the full data as the input.
2. Split the dataset in such a way that the separation of the class is maximum.
3. Split the input.
4. Follow steps 1 and 2 to the separated data again.
5. Stop this process after the complete data is separated.

5. Best Python data science courses

Many websites provide Data Science online courses. Here are the best sites that offer data science training based on Python.

• GreatLearning
• Coursera
• EdX
• Alison
• Udacity
• Skillathon
• Konvinity
• Simplilearn

6. How do data science courses help in a successful career, post-COVID-19 pandemic?

The economic downfall due to COVID-19 impacts has led to upskill oneself as the world scenarios are changing drastically. Adding skills to your resume gives an added advantage of getting a job quickly.

The businesses will invest mainly in two domains .i.e. data analysis of customer’s demand and understanding the business numbers. It is nearly impossible to master data science, but this lockdown may help you become a professional by indulging in data science programs.

Firstly, start searching for the best data science course on the internet. Secondly, make a master plan in such a way that you complete all the courses successfully. Many short-term courses are there online that are similar to the regular courses, but you can complete it within a few days.

For example, Analytics Labs are providing these kinds of courses to upskill yourself. So this is the right time where you are free without any work and passing time. You can use this time efficiently by enrolling in these courses and become more skilled in data science than before.

These course providers also give a data science certification for the course you did; this will help build your resume.

Data science is a versatile field that has a broad scope in the current world. These data scientists are the ones who are the pillars of businesses.

They use various factors like programming languages, machine learning, and statistics in solving a real-world problem. When it comes to programming languages, it is best to learn Python as it is easy to understand and has an interactive interface. Make efficient use of time in COVID-19 lockdown to upskill and build yourself.

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