Top 10 Tools for data scientists in 2022.

June 22, 2022

In 2022, the most enticing professional path is data science. And data is the gold that corporations mine every day to ensure that their marketing strategies, products, and brands are at the top of customers' minds.  And to deliver all of this, organizations need a data scientist, who on the other side needs a few tools to do their work more quickly and efficiently. 

These tools include programming languages, libraries, data storing software, visualization software, and much more. 

This blog will take you through 10 such tools that can make your life as a data scientist, not a bit but much easier in 2022. Please read on…

#1 Python

Python is a high-level, interpreted, open-source, programming language that offers an excellent approach to object-oriented programming. It is one of the most popular languages used by data scientists for a variety of projects and applications. Python has a lot of features for dealing with arithmetic, statistics, and scientific functions. It also has excellent libraries widely used by data scientists in various projects and applications, these libraries include:

  1. SciPy: Scipy is a prominent Python data science and scientific computing toolkit. It is a package full of a lot of functionalities for scientific, mathematics, and computer programming. Special functions,  Optimization, interpolation, linear algebra, integration, special functions, FFT, signal and image processing, ODE solvers, Statmodel, and other activities used in data science research are covered by SciPy sub-modules.

  1. Scikit Learn: Sklearn is a machine learning package for Python. Sklearn includes a number of machine-learning-related algorithms and utilities. Sklearn's data mining and data analysis tools are basic and straightforward. It offers consumers a standard interface with a collection of popular machine learning methods. Scikit-Learn aids in the rapid implementation of popular algorithms on datasets and the resolving of real-world challenges.

  1. Pandas: Pandas is one of the most widely used Python data manipulation and analysis libraries. Pandas have functions that may be used to manipulate vast amounts of structured data. It supports huge data structures as well as numerical tables and time series data manipulation. 

  1. Numpy: Numpy or Numerical Python is a Python package that contains mathematical functions for dealing with huge arrays. It has Array, Metrics, and Linear Algebra methods and functions. On the NumPy array type, the library supports vectorization of mathematical operations, which improves efficiency and speeds up the execution of data science problems. It also makes working with big multidimensional arrays or data with varied dimensionality and matrices simple.

#2 Apache Hadoop

Hadoop is open-source software built to grow from a single server to tens of thousands of computers and uses basic programming principles to process enormous data volumes across clusters of computers. It is a powerful tool and its distributed computing paradigm allows it to process massive volumes of data in terms of processing power and scalability. A data scientist can have more processing power by simply using more nodes. Hadoop saves information without the need for preprocessing, even unstructured data like text, photos, and video. It keeps several copies of every data automatically, and if one node fails while processing data, jobs are moved to other nodes, and distributed computing continues. Data is kept on commodity hardware, and the open-source framework is free. Furthermore, you may quickly expand your Hadoop system by simply adding extra nodes. 

#3 Tableau 

While tableau isn't required for certain hardcore coding, it may still be useful in your profession and day-to-day work as a data scientist. Tableau allows data scientists to do many EDA activities on a single platform with fewer resources and shorter timelines. Simply load the dataset from various sources and do various operations on it. It may be used to replace uninspiring charts with more useful and appealing numbers, such as bullet charts. The time that would have been spent creating programs may now be spent on other tasks by data scientists. SQL queries may be run on static Excel/CSV files in Tableau. Also, you may copy and paste queries to interact with databases that don't require you to be online.

#4 TensorFlow

TensorFlow is an open-source machine learning framework offering a robust ecosystem of tools and libraries that make it simple for developers to create and deploy machine learning-powered applications. It may be used for a variety of tasks, however, it is primarily focused on deep neural network training and inference. TensorFlow's most important feature for machine learning development is an abstraction. The data scientist may focus on the overarching logic of the program rather than the nitty-gritty details of developing algorithms or finding out how to hitch the result of one function to the input of another.

#5 Matlab

Data scientists may use MATLAB to integrate ML and statistics with application-specific techniques including signal and image processing, text analytics, optimization, and controls. The Deep Learning Toolbox in Matlab is a set of basic Matlab instructions for constructing and linking the layers of a deep neural network. It comes with a Parallel Computing Toolbox, which allows you to distribute training over multicore CPUs, graphics processing units (GPUs), and clusters of computers with multiple CPUs and GPUs. Matlab is an ML-rich programming language with a built-in library. As a result, the script is both tiny and effective when compared to other languages. Data scientists can develop a sophisticated program in just a few lines because of the language's architecture.

#6 Jupyter Notebook

Data scientists utilize Jupyter, a free, open-source interactive online application known as a computational notebook that mixes software code, computational output, explanatory text, and multimedia resources in a single document. Although computational notebooks have been around for decades, Jupyter has gained a lot of traction in recent years. An active community of user–developers, as well as a revised architecture that allows the notebook to speak dozens of programming languages — a fact mirrored in its name — have supported the notebook's quick adoption.

#7 DataRobot

Designed for the way data scientists operate and developed to help them give the most value to the company they work in. To help teams in this quickly expanding AI ecosystem, critical tools and solutions, best practices, and continuing education are combined. Allow data scientists to concentrate on important strategic efforts while avoiding tactical distractions. A wide variety of data engineering best practices—automated with custom-coded pipelines—increase the pace of important activities. Create realistic models in a fraction of the time with complete customization and experimentation flexibility.

#8 RapidMiner

RapidMiner is a comprehensive data science platform that automates and augments data preparation, model construction, and model operations. RapidMiner provides end-to-end augmentation and automation, from data exploration to modeling to production, to increase productivity and simplify the route to results. Because no one, including data scientists, requires excessive complexity. Data Scientists can swiftly put robust models into production, guaranteeing that they provide long-term value, and make results easy to consume using custom dashboards or from the BI platform utilizing RapidMiner's no-code deployment, automated monitoring, and insight delivery features.

#9 Knime

As a data scientist, you face a variety of challenges, one of which is lowering the time it takes to automate data collection and preparation so you can focus on tasks that really matter. The open-source software KNIME Analytics Platform helps you in doing exactly that. KNIME makes comprehending data and building data science processes and reusable components accessible to everyone by being intuitive, open, and constantly incorporating new innovations. All inside one platform, a vast array of data sources, tools, and approaches, many of which are based on prominent open source projects. Knime is free and open source. Methods, data, and operating systems are all unrestricted.

#10 BigML

Thousands of analysts, software engineers, and scientists across the world are using BigML to perform Machine Learning jobs "end-to-end," seamlessly translating data into actionable models that can be utilized as distant services or integrated locally in apps to generate predictions. The service is designed in such a way that you don't need a deep understanding of data science or machine learning techniques to get the most out of it. Sure, you have sophisticated choices on the service, but you won't need them as BigML's strong "1 Click" functionality makes it simple to develop predictive models.

Conclusion-

Few of the tools listed above are necessary for being a data scientist and without them, it's not even possible to begin while the others are just to make the process easy and can be considered optional. We hope you find this information useful.

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October 4, 2022

Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

Cloud services are essential for modern businesses, as they provide easy access to software, storage, and computing resources. Cloud services are delivered over the internet and are typically charged on a per-use basis. This makes them incredibly convenient for businesses, as they can pay for only the resources they actually use. Cloud computing allows businesses to scale their resources up or down, depending on their current business needs. This makes it suitable for startups, where demand is uncertain, or large enterprises with global operations. Cloud computing is also inherently scalable and allows businesses to quickly react to changing business needs. Cloud computing is a very competitive industry and providers offer attractive prices to attract customers. However, these prices have been impacted by the strong dollar. The dollar has strengthened by 15-20% against the Indian rupee in the last few years. As a result, the costs of services such as storage and bandwidth have increased for Indian companies. Vendors charge their Indian customers in Indian rupees, taking into account the exchange rate. This has resulted in a significant rise in the costs of these services for Indian companies.

Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.

Conclusion

The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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September 28, 2022

Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.

Conclusion

In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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September 21, 2022

Top 12 skills a CEO should demand in a data scientist to hire in 2022

Two decades ago, data scientists didn’t exist. Sure, some people cleaned, organized and analyzed information — but the data science professionals we admire today stand at the head of a relatively new (and vaunted) career path.

It is certainly one of the most popular careers because it is in great demand and highly paid. With data being the primary fuel of industry and organization, company executives must now determine how to drive their company in this rapidly changing environment. Not only is a growth blueprint essential, but so are individuals who can put the blueprint into action. When most senior executives or human resource professionals think of data-driven employment, a data scientist is the first position that comes to mind.

In this blog, we will discuss the top 12 skills a CEO should demand if hiring a data scientist in 2022. 

  1. Problem-Solving and Critical Thinking

Finding a needle in a haystack is the goal of data science. You'll need a candidate who has a sharp problem-solving mind to figure out what goes where and why, and how it all works together. Thinking critically implies making well-informed, suitable judgments based on evidence and facts. That means leaving your own ideas at the door and putting your faith - within reason - in the evidence. 

Being objective in the analysis is more difficult than it appears at first. One is not born with the ability to think critically. It's a talent that, like any other, can be learned and mastered with time. Always look for a candidate who is prepared to ask questions and change his/her opinion, even if it means starting over.

  1. Teamwork 

If you go through job listings on sites like Indeed or LinkedIn, you'll notice one phrase that appears repeatedly: must work well in a team. Contrary to popular belief, most scientific communities, including those in data science, do not rely on a single exceptional mind to drive forward development. A team's cohesiveness and collaboration power are typically more significant than any one member's brilliance or originality. Your potential candidate will not contribute to success if s/he does not play well with others or believes that s/he does not require assistance from your colleagues. If anything, candidates' poisonous attitudes may cause stress, decreased levels of accomplishment, and failure on the team.

Harvard researchers revealed in 2015 that even "moderate" amounts of toxic employee conduct might increase attrition, lower employee morale, and reduce team effectiveness. Eighty percent of employees polled said they wasted time worrying about coworker incivility. Seventy-eight per cent claimed toxicity had reduced their dedication to their work, and 66 per cent said their performance had suffered as a result. The fact is that being a team player is significantly more productive and fulfilling than being a solo act. Look for a candidate with good cooperation abilities, and both you and your team will profit!

  1. Communication 

Capable data scientists must be able to communicate the conclusions they get from data. If your candidate lacks the ability to convert technical jargon into plain English, no matter how significant the results are, your audience will not grasp them. Communication is one of the most important skills a data scientist can learn — and one that many pros struggle with. 

One 2017 poll that tried to uncover the most common impediments that data scientists encountered at work discovered that the majority of them were non-technical. Among the top seven barriers were "explaining data science to others," "lack of management/financial support," and "results not utilised by decision-makers."

You fail if you can't communicate - therefore look for a candidate who knows how to interpret! And can break down complicated topics into digestible explanations; rather than giving a dry report.

  1. Business Intelligence 

Sure, a candidate can’t start teaching abstruse mathematical theory whenever you want — but can they explain how that theory can be applied to advance business? True, data scientists must have a strong grasp of their field as well as a solid foundation of technical abilities. However, if a candidate is required to use those abilities to advance a corporate purpose, they must also have some level of business acumen. Taking a few business classes will not only help them bridge the gap between their data scientist peers and business-minded bosses, but it will also help them advance the company's growth and their career as well. It may also assist them in better applying their technical talents to create useful strategic insights for your firm.

  1. Statistics and mathematics 

When it comes to the role of arithmetic in machine learning, perspectives are mixed. There is no disputing that college-level comprehension is necessary. Linear algebra and calculus should not sound like other languages. However, if you're looking for a candidate for an internship or a junior position, then they don't need to be a math guru. But if you are looking for a candidate to work as a researcher, then the candidate must have more than just a strong math background. After all, research propels the business ahead, and you won't be able to accomplish anything until you have a candidate with a thorough grasp of how things function.

The fact is that just because data science libraries enable data scientists to perform complex arithmetic without breaking a sweat doesn't mean they shouldn't be aware of what's going on behind the surface. Get a candidate with the fundamentals right.

  1. AI and Machine Learning 

Machine learning is an essential ability for any data scientist. It is used to create prediction models ranging from simple linear regression to cutting-edge picture synthesis using generative adversarial networks. When it comes to machine learning, there is a lot to look for in a potential candidate. Regression, decision trees, SVM, Naive Bayes, clustering, and other classic machine learning techniques (supervised and unsupervised) are available. Then there are neural networks, which include feed-forward, convolutional, recurrent, LSTM, GRU, and GAN. There's also reinforcement learning, but you get the idea - machine learning is a vast subject. 

  1. Skills in cloud and MLOps

To remain relevant to the industry's current demands, more than three out of five (61.7%) companies say they need data scientists with updated knowledge in cloud technologies, followed by MLOps (56.1%) and transformers (55%). Three out of every four professionals with ten or more years of experience are learning MLOps to expand their skill sets. Cloud technologies (71.7%) are being learned as a fundamental new talent by mid-career professionals with 3-6 years of experience, followed by MLOps (62.3%), transformers (60.4%), and others.

Professionals in retail, CPG, and e-commerce are more likely (73.7%) to learn cloud technology as a new skill. As much as 70% of BFSI personnel upskill in MLOps. Another 70% and 60% of pharma and health workers are interested in acquiring transformers and computer vision as fundamental skills.

So make sure you don't miss out on such a talent who can bring cloud and MLOps skills into your company. 

  1. Storytelling and Data Visualization 

Data visualisation is enjoyable. Of course, it depends on who you ask, but many people consider it the most gratifying aspect of data science and machine learning. Look for a candidate who is a visualisation specialist and understands how to show data based on business requirements, and also how to integrate visualisations so that they tell a story. It might be as easy as integrating a few plots in a PDF report or as sophisticated as creating an interactive dashboard suited to the client's requirements.

The data visualisation tools utilised are determined by the language. Plotly, which works with R, Python, and JavaScript, may be the best option if you need a candidate for searching for a cross-platform interactive solution. Consider Tableau and PowerBI when you need a candidate for viewing data using a BI tool. 

Figure: Use of Data Visualization tools. 

  1. Programming 

Without programming, there is no data science. How else would you give the computer instructions? All data scientists must be familiar with writing code, most likely in Python, R, or SQL these days. The breadth of what a candidate will perform with programming languages differs from that of traditional programming professions in that they’ll lean toward specific libraries for data analysis, visualisation, and machine learning. 

Still, thinking like a coder entails more than just understanding how to solve issues. If there is one thing that data science sees a lot of, it is issues that need to be solved. But nothing is worse than understanding how to fix an issue but failing to transform it into long-lasting, production-ready code.

Out of the host of programming languages, 90% CEOs hire data science specialists who are specialists in Python as their preference for statistical modelling. Beyond that, the use of SQL (68.4%) is highest in retail, CPG, and ecommerce, followed by IT at 62.9%. R is the most widely used programming language if you operate in the pharma and healthcare business, with three in five (60%) data scientists reporting using it for statistical modelling.

  1. Mining Social Media 

The process of extracting data from social media sites such as Facebook, Twitter, and Instagram is referred to as social media mining. Skilled data scientists may utilise this data to uncover relevant trends and extract insights that a company can then use to gain a better knowledge of its target audience's preferences and social media actions. You need data scientists well versed with this type of study as it is essential for building a high-level social media marketing plan for businesses. Given the importance of social media in day-to-day business and its long-term viability, hiring data scientists with social media data mining abilities is an excellent strategy for company growth.

  1. Data manipulation 

After collecting data from various sources, a data scientist will almost surely come across some shoddy data that has to be cleaned up. You need to hire a candidate that knows what Data wrangling is. How to use it for the rectification of data faults such as missing information, string formatting, and date formatting. 

  1. Deployment of a Model 

What is the use of a ship if it cannot float? Non-technical users should not be expected to connect to specialised virtual machines or Jupyter notebooks only to check how your model operates. As a result, the ability to deploy a model is frequently required for data scientist employment.

The easiest solution is to establish an API around your model and deploy it as any other application — hosted on a virtual machine operating in the cloud. Things get harder if you wish to deploy models to mobile, as mobile devices are inferior when it comes to hardware. 

If speed is critical, sending an API call and depending on an Internet connection isn't the best option. Consider distributing the model directly to the mobile app. Machine learning developers may not know how to design mobile apps, but they may examine lighter network topologies that will have reduced inference time on lower-end hardware.

Consider hiring a candidate who is well versed with all the things discussed above related to deploying a model. 

Conclusion

And there you have it: the top twelve talents skills a CEO must look for while hiring a data scientist. Keep in mind that skill levels or talents themselves may differ from one firm to the next. Some data science jobs are more focused on databases and programming, while others are more focused on arithmetic. Nonetheless, we believe that these 12 data science skills are essential for your potential candidate in 2022.

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