10 Best Resources To Learn Data Science Online in 2022

Today, data science is more than a buzzword. To simply put it, data science is an interdisciplinary field of gathering data from various sources and channels such as databases, analysing and transforming them into visualization and graphs. This basically facilitates the readability and understanding of the data to aid in soft-skills like insightful decision-making for any organization or business. In short, data science is a combination of incorporating scientific methods, different technologies, algorithms, and more when it comes to data.

Apart from the certified courses, as a data scientist, it is expected to have experience in various domains of computer science, including knowledge of a few programming languages such as Python and R as well as statistics and mathematics. An individual should be able to comprehend the data provided and be able to transform it into graphs which help in extracting insight for a particular business.

Best Resources To Learn Data Science

For those pursuing a career in data science, it is not just technical skills that matter, in business settings an individual is tasked with communicating complex ideas and making data-driven insightful decisions. As a result, people in the field of data science are expected to be effective communicators, leaders, and team members as well as high-level analytical thinkers too.

If we talk about applications of data science, it is used in myriad fields, including image and speech recognition, the gaming world, logistics and supply chain, healthcare, and risk detection, among others. It remains a limitless world indeed. Data scientists will continue to remain in high demand, while at the same time there is a substantial skill gap that needs to be currently addressed in the industry.

Here’s the lowdown on a few of the online resources—in no particular order—which can be checked out to learn data science. While a few of these educational platforms have been launched a couple of years ago, they would continue to hold equal relevance when it comes to resources for seeking in-depth knowledge related to everything in the field of data science.

1. Udemy

Udemy is a site that offers hands-on exercises while extending comprehensive data courses. At last count, there were about 10,000 data courses and almost 500 of which are free of cost. An individual can discover specialisations, including Python, Tableau, R, and many more. While offering real-world examples, Udemy courses are quite well-defined when it comes to specific topics.
The courses are suitable for beginners as well as experts in the field of data science.

2. Coursera

Coursera is another online learning platform that offers massive open online courses (MOOC), specialisations, and degrees in a range of subjects, and this includes data science as well. Some of the courses hosted on the platform include top-notch names such as Harvard University, University of Toronto, Johns Hopkins University, University of Michigan, and MITx, among others. Coursera courses can be audited for free and certificates can be obtained by paying the mentioned amount. The courses from Coursera are part of a particular specialisation, which is a micro-credential offered by Coursera. These specialisations also include a capstone project.

3. Pluralsight

Pluralsight remains an educational platform for learners through insights from instructor-led courses or online courses, which lay stress on basics and some straightforward scenarios. Courses taken online will require you to exert more effort to gain detailed insights, thus helping you in the longer run. Pluralsight introduces one to several video training courses for Software developers and IT administrators.

By using the service of Pluralsight, an individual can look forward to learning a lot of solutions. An individual can even get the key business objectives and even close the skill gaps in critical areas like cloud, design, security, and mobile data.

4. FlowingData

The website, which is produced by Dr. Nathan Yau, Ph.D., offers insights from experts about how to present, analyse, and understand data. This comes with practical guides to illustrate the points with real-time examples. In addition, the site also offers book recommendations, as well as provides insights related to the field of data science.
There are also articles which an individual can browse related to gaining more in-depth insight into the correlation between data science and the world around.

5. edX

edX is an online platform, which has been created as a tie-up between Harvard University and the Massachusetts Institute of Technology. This website has been designed with the idea to highlight courses in a wide range of disciplines and deliver them to a larger audience across the world. edX extends courses that are offered by 140 top-notch universities at free or nominal charges to make learning easy. The website includes at least 3,000 courses and has programs available for learners to excel in the field of data science.

6. Kaggle

Kaggle is an online learning platform that would be quite beneficial for individuals who already have some knowledge related to data science. In addition, most of the micro-courses require the users to have some prior knowledge in data science languages such as Python or R and machine learning. It remains an ideal site for upgrading skills and enhancing the capabilities in the field of data science. It offers extensive insights related to the field from experts.

7. GitHub

GitHub remains a renowned platform that uses Git, which is a DevOps tool used for source code management, to apply version control to a code. With over 40 million developers on its users list, it also opens up a lot of opportunities for data scientists to collaborate and manage projects together, besides gaining insights about the industry that continues to remain high in demand at the moment.

 

 

8. Reddit

This is a platform that comprises sub-forums, or subreddits, each focused on a subject matter of interest. Under this, the R/datascience subreddit has been titled the data science community, which remains one of the larger subreddit pages related to data science. Various data science professionals discuss relevant topics in data science. The data science subreddit remains insightful for individuals seeking a community that can provide related technical advice in the field of data science.

9. Udacity

Udacity Data Science Nanodegree remains an ideal certification program for those who remain well-versed with languages such as Python, SQL, machine learning, and statistics. In terms of content, Udacity Data Science Nanodegree remains quite advanced and introduces hands-on practice in the form of real-world projects. While Udacity doesn’t offer an all-inclusive course, it introduces separate courses for becoming an expert in the field of data science. Professionals who aspire to become data scientists are advised to take Udacity’s three courses namely Intro to Data Analysis, Introduction to Inferential Statistics, and Data Scientist Nanodegree. These three courses extend real-world projects, which are provided by industry experts. In addition, technical mentor support, flexible learning program, and personal career coach and career services are also offered to aspirants in the domain.

10. KDnuggets

KDnuggets remains a resourceful site on business analytics, big data, data mining, data science, and machine learning. The site is edited by Gregory Piatetsky-Shapiro, a co-founder of Knowledge Discovery and Data Mining Conferences. KDnuggets boasts of more than 4,00,000 unique visitors and has about 1,90,000 subscribers. The site also provides information related to tutorials, certificates, webinars, courses, education, and curated news, among others.

 

Ending Note

Increasing technology and big data mean that organizations must leverage their data in order to deliver more powerful products and services to the world by analyzing that data and gaining insight, which is what the term “Data Science” means. You can jumpstart your career in Data Science by utilizing any of the resources listed above. Make sure you have the right resources and certifications. Now is the time to work in the data industry.

 

Mainframe Modernization: Making It Happen

In the fast-paced world of technology and business, it can be hard to keep up with what’s new. What’s new today can be obsolete in a few weeks, and adapting to this ever-changing landscape can become a challenge if an organization isn’t well prepared or equipped. Modernization of systems doesn’t necessarily mean transitioning to an entirely new system or platform; often, all it takes is actual modernization of existing tools to help them adapt to new business demands and requirements.

The mainframe is one system that has stood the test of time. A number of naysayers taut the system as “legacy” or obsolete, but the fact that mainframes handle 68% of the world’s production IT workloads indicate otherwise. Mainframes are proof that the latest isn’t always the greatest, standing firm as one of the foundations of business systems in today’s most successful businesses around the world. What some don’t realize is that the race toward digital transformation is not reliant on the system or platform an organization has in place; digital transformation initiatives rise and fall depending on how they approach data. Regardless of the platform used, data analysts who work with irrelevant or stale data are prone to achieve false or misleading results. Access to real-time data is key, and data gathered days or hours—even minutes—ago isn’t a current representation of the current situation. This can lead to an organization acting on miscalculations and opportunities that no longer exist. Actionable insights need to come from real-time data to ensure that your organization can make sound business decisions in a timely manner.

The Old vs. the New

Conventional methodologies have kept mainframe data and real-time data separate due to issues with accessibility. Most businesses traditionally use Extract, Transform, and Load (ETL) processes for data analysis, a logistically complex and time-consuming process that’s prone to errors and stale data because it’s performed only periodically. This can lead to hours or even weeks of delay that’s simply unacceptable in today’s always-connected, always-on digital business landscape. Today’s businesses depend largely on real-time business intelligence—and access to it—to get a competitive edge.

In light of this perceived separation between mainframes and real-time data analytics, data scientists have found that the creation of analytic models can be too slow at times due to the conventional process of offloading data from the mainframe to other platforms for analysis. Organizations should move away from ETL processes and find ways to make real-time data analytics from the mainframe quicker and more efficient for their business. Mainframe modernization is key in making mainframe systems work with modern solutions because it allows for data virtualization, integrating all disparate enterprise data into a logical data layer. This layer manages the unified data and provides centralized governance while delivering the required data in real-time to business users.

Depending on the industry, mainframe modernization can optimize key business processes like order processing, payment gateways, and internal business operations queries. Mainframes are known for performing high-volume transaction processing, and these transactions can make or break a business. Managed in real-time, it will help organizations battle fraud and manage business risks as they arise, or even before they do. The data gathered can also help paint a more accurate representation of who a company’s customers are, allowing them to better plan resources and come up with more personalized initiatives.

Making IT Happen

Mainframe modernization is a major undertaking that presents a host of options for every organization. These options will vary depending on a number of factors, including business size, tenure, and industry. The following, however, are a few of the key considerations in modernization.

  • Look for quick wins
    As all businesses know by now, time is of the essence in every undertaking, even mainframe modernization. Its success is dependent on how quickly it can deliver the desired results.
  • Automate migration to avoid disruption
    Accelerating modernization efforts means leveraging modern tools API’s. The platforms available today are designed to minimize the effects of the modernization process if not avoid disruption completely.
  • Focus on total cost of ownership (TCO)
    It’s a mistake to view the initial cost of modernization at face value. Amore accurate view of costs involves a focus on the total cost of ownership. Calculating the TCO, or the purchase costs plus operation costs, will help minimize it even before modernization initiatives commence.
  • Don’t just leave everything to IT
    The modern IT team is one that includes everyone in the organization. Mainframe modernization is more a business initiative than an IT concern, and as such, should involve decision makers and business leaders. System integrations and updates remain the responsibility of IT specialists, but choosing the appropriate modernization approach and ensuring that the initiative succeeds should be a responsibility shared by the entire organization.
  • Create business value
    Mainframe modernization isn’t simply the implementation of technology upgrades or migration to a new system; it should also be an opportunity to combine the old with the new. Improve existing business processes or create new ones accordingly while capturing institutional knowledge from mainframe systems to gain a competitive edge.

Options abound when it comes to mainframe modernization, but that doesn’t mean that you should apply them all or choose the latest and greatest. Choosing the right approach to modernization entails re-examining your business and its goals and deciding which solution will take you there—and take you there fast. There exists an “imaginary” gap between digital innovators and mainframes because of the challenges and costs in data accessibility and system availability. The goal of mainframe modernization is to bridge this gap in the best, and fastest, way possible.