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Author name: Rubiscape Team

Other

Power Bi Developer

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Application for – Power Bi Developer Job Description Job Overview We are seeking a talented and experienced Data Engineer to join our growing data team. The ideal candidate will have a strong background in data engineering, with 3-5 years of experience in designing and building data pipelines, managing data infrastructure, and supporting business intelligence solutions. Key Responsibilities Design, build, and maintain scalable and reliable data pipelines and data workflows. Collaborate with data scientists, analysts, and business stakeholders to provide data-driven insights. Develop ETL (Extract, Transform, Load) processes to integrate data from various sources. Ensure data quality and integrity by performing data validation and monitoring. Work with cloud platforms such as AWS, Google Cloud, or Azure to manage data storage and processing. Optimize and maintain existing data infrastructure and pipelines. Implement data governance and best practices for data security and privacy. Required Skills & Qualifications 3-5 years of experience as a Data Engineer or similar role. Proficiency in programming languages such as Python, Java, or Scala. Hands-on experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience working with big data technologies like Hadoop, Spark, or Kafka. Strong understanding of data modeling, ETL processes, and data warehousing concepts. Experience with cloud computing platforms (AWS, Google Cloud, Azure). Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes. Good communication skills and the ability to work well in a team-oriented environment. Preferred Qualifications Experience with data pipeline orchestration tools such as Apache Airflow or similar. Knowledge of data visualization tools (e.g., Tableau, Power BI). Experience with machine learning pipelines and model deployment. Benefits Competitive salary and performance-based bonuses. Comprehensive health and dental insurance. Opportunities for career growth and development. Work-from-home options and flexible working hours. Application Form Job Application Form First Name* Please enter your First Name. Last Name* Please enter your Last Name. Email* Please enter a valid email address. Gender* Select your GenderMaleFemaleOther Please select your Gender. Address* Please enter your address. Country* India Please select your Country. City* Please enter your City. Country Code* India (91) Please select your Country Code. Phone Number* Please enter your Phone No. Date of Birth* Please enter your DOB. Ready to Relocate?* Choose from the options belowYesNo Please select your response. Attach your CV* Please attach your CV. Submit

Other

Data Enginner

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Application for – Data Engineer Job Description Job Overview We are seeking a talented and experienced Data Engineer to join our growing data team. The ideal candidate will have a strong background in data engineering, with 3-5 years of experience in designing and building data pipelines, managing data infrastructure, and supporting business intelligence solutions. Key Responsibilities Design, build, and maintain scalable and reliable data pipelines and data workflows. Collaborate with data scientists, analysts, and business stakeholders to provide data-driven insights. Develop ETL (Extract, Transform, Load) processes to integrate data from various sources. Ensure data quality and integrity by performing data validation and monitoring. Work with cloud platforms such as AWS, Google Cloud, or Azure to manage data storage and processing. Optimize and maintain existing data infrastructure and pipelines. Implement data governance and best practices for data security and privacy. Required Skills & Qualifications 3-5 years of experience as a Data Engineer or similar role. Proficiency in programming languages such as Python, Java, or Scala. Hands-on experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience working with big data technologies like Hadoop, Spark, or Kafka. Strong understanding of data modeling, ETL processes, and data warehousing concepts. Experience with cloud computing platforms (AWS, Google Cloud, Azure). Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes. Good communication skills and the ability to work well in a team-oriented environment. Preferred Qualifications Experience with data pipeline orchestration tools such as Apache Airflow or similar. Knowledge of data visualization tools (e.g., Tableau, Power BI). Experience with machine learning pipelines and model deployment. Benefits Competitive salary and performance-based bonuses. Comprehensive health and dental insurance. Opportunities for career growth and development. Work-from-home options and flexible working hours. Application Form Job Application Form First Name* Please enter your First Name. Last Name* Please enter your Last Name. Email* Please enter a valid email address. Gender* Select your GenderMaleFemaleOther Please select your Gender. Address* Please enter your address. Country* India Please select your Country. City* Please enter your City. Country Code* India (91) Please select your Country Code. Phone Number* Please enter your Phone No. Date of Birth* Please enter your DOB. Ready to Relocate?* Choose from the options belowYesNo Please select your response. Attach your CV* Please attach your CV. Submit Application for – Data Engineer Job Description Application Form Job Overview We are seeking a talented and experienced Data Engineer to join our growing data team. The ideal candidate will have a strong background in data engineering, with 3-5 years of experience in designing and building data pipelines, managing data infrastructure, and supporting business intelligence solutions. Key Responsibilities Design, build, and maintain scalable and reliable data pipelines and data workflows. Collaborate with data scientists, analysts, and business stakeholders to provide data-driven insights. Develop ETL (Extract, Transform, Load) processes to integrate data from various sources. Ensure data quality and integrity by performing data validation and monitoring. Work with cloud platforms such as AWS, Google Cloud, or Azure to manage data storage and processing. Optimize and maintain existing data infrastructure and pipelines. Implement data governance and best practices for data security and privacy. Required Skills & Qualifications 3-5 years of experience as a Data Engineer or similar role. Proficiency in programming languages such as Python, Java, or Scala. Hands-on experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience working with big data technologies like Hadoop, Spark, or Kafka. Strong understanding of data modeling, ETL processes, and data warehousing concepts. Experience with cloud computing platforms (AWS, Google Cloud, Azure). Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes. Good communication skills and the ability to work well in a team-oriented environment. Preferred Qualifications Experience with data pipeline orchestration tools such as Apache Airflow or similar. Knowledge of data visualization tools (e.g., Tableau, Power BI). Experience with machine learning pipelines and model deployment. Benefits Competitive salary and performance-based bonuses. Comprehensive health and dental insurance. Opportunities for career growth and development. Work-from-home options and flexible working hours. Job Application Form First Name* Please enter your First Name. Last Name* Please enter your Last Name. Email* Please enter a valid email address. Gender* Select your GenderMaleFemaleOther Please select your Gender. Address* Please enter your address. Country* India Please select your Country. City* Please enter your City. Country Code* India (91) Please select your Country Code. Phone Number* Please enter your Phone No. Date of Birth* Please enter your DOB. Ready to Relocate?* Choose from the options belowYesNo Please select your response. Attach your CV* Please attach your CV. Submit Job Overview We are seeking a talented and experienced Data Engineer to join our growing data team. The ideal candidate will have a strong background in data engineering, with 3-5 years of experience in designing and building data pipelines, managing data infrastructure, and supporting business intelligence solutions. Key Responsibilities Design, build, and maintain scalable and reliable data pipelines and data workflows. Collaborate with data scientists, analysts, and business stakeholders to provide data-driven insights. Develop ETL (Extract, Transform, Load) processes to integrate data from various sources. Ensure data quality and integrity by performing data validation and monitoring. Work with cloud platforms such as AWS, Google Cloud, or Azure to manage data storage and processing. Optimize and maintain existing data infrastructure and pipelines. Implement data governance and best practices for data security and privacy. Required Skills & Qualifications 3-5 years of experience as a Data Engineer or similar role. Proficiency in programming languages such as Python, Java, or Scala. Hands-on experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB). Experience working with big data technologies like Hadoop, Spark, or Kafka. Strong understanding of data modeling, ETL processes, and data warehousing concepts. Experience with cloud computing platforms (AWS, Google Cloud, Azure). Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes. Good communication skills and the ability to work well in a team-oriented environment. Preferred Qualifications Experience with data pipeline orchestration tools such as Apache Airflow or similar. Knowledge of data visualization tools

Rubiscape and Protean Partnership
News

Rubiscape Partners with Protean Cloud to Revolutionize AI-ML and Data Science Solutions on the Intelligent Cloud

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Pune, October 2024 – Pune, October 2024 – As businesses continue to accelerate their digital transformation journeys, technologies like Artificial Intelligence (AI), Machine Learning (ML), and Data Science are no longer just competitive advantages—they are essential for survival in an increasingly data-driven world. According to recent market reports, the AI-ML market is projected to grow at a CAGR of 38.8%, reaching $309.6 billion by 2026. At the same time, cloud computing adoption continues to surge, with Gartner forecasting that global cloud services spending will reach $600 billion by 2025. The convergence of AI-ML and cloud technology is rapidly shaping the future of industries across the globe. Rubiscape and Protean Cloud have now entered a strategic partnership to leverage this powerful synergy. Together, they will bring state-of-the-art AI-ML and Data Science solutions to businesses via the Protean Cloud platform, unlocking unprecedented opportunities for innovation, scalability, and real-time data intelligence. “We are excited to partner with Rubiscape to deliver cutting-edge AI-ML and Data Science solutions on a cloud platform that is as intelligent as the technology it hosts. This partnership will empower businesses with scalable, secure, and seamless access to tools that drive smarter decision-making, improved workflows, and innovative business strategies”said Mr. Dharmesh Parekh, EVP & CIO at Protean Cloud. Rubiscape’s multi-persona Data Science & Machine Learning (DSML) platform will now be available on Protean Cloud’s powerful and secure infrastructure. “At Rubiscape, we are driven by the mission to ‘Democratising Data Science’, with a better accessibility, utilisation and impact of data. This partnership with Protean Cloud marks a significant step in that direction. Together, we will redefine how Data Science and AI solutions are build & scaled to disrupt industries and create new business models.” said Dr. Prashant Pansare, CEO, Rubiscape India By integrating AI and data-driven solutions with cloud technology, businesses can harness the true potential of their data for better decision-making, operational efficiency, and competitive advantage.  About Protean Protean eGov Technologies Ltd (previously NSDL e-Governance Infrastructure Ltd) is a leading Indian Technology company focuses on developing digital public infrastructure (DPI) and e-governance initiatives. Protean eGov provides Intelligent Cloud offerings for citizen services, e-governance solutions, system integration, business process re-engineering, data center co-location, and IT consulting services for citizens, corporates and the Government.  About Rubiscape Rubiscape, an award-winning, multi-persona Data Science & Machine Learning software product company, enabling people and enterprises to turn diverse data into insightful stories that drive business initiatives and actions. Rubiscape has emerged as a platform of choice to many forward-thinking enterprises; with 3X faster data pipelines, 5X lower TCO and a revolutionary user experience.

Ideas

ICC World Cup

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The countdown has begun for the T20 World Cup 2024! Get ready for an exhilarating tournament filled with world-class cricket action, as teams from around the globe compete for the ultimate prize in the shortest format of the game. Stay tuned for updates and join us in celebrating the spirit of cricket as we cheer on our favorite teams. Soon, you’ll be able to get live insights through the Rubisight Dashboard.

Ideas

Summer Heat

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India experiences significant heat waves, with regions like Rajasthan, Uttar Pradesh, and Delhi often recording temperatures exceeding 45°C (113°F). The frequency and intensity of these heat waves have increased over the past few decades due to climate change, impacting millions of people annually . Heat waves in India have led to substantial loss of life and health issues, with estimates of heat-related deaths varying. Some sources report thousands of fatalities during severe heat waves; for instance, over 2,500 people died due to an extreme heat wave in 2015 . Additionally, numerous cases of heat stroke and dehydration are reported each year. For a more detailed analysis of the impact of heat waves in India, including affected regions and demographic details, you can refer to the Indian Meteorological Department (IMD) . To stay updated on the latest information and advisories, resources such as the IMD and the National Disaster Management Authority (NDMA) are valuable . The frequency and intensity of heat waves in India are influenced by ongoing climate change. The provided statistics are based on the latest available information, and the situation may evolve as climate patterns shift.

future of data science - key trends - rubiscape blog
Data Science

Top Mistakes Made by Data Scientists

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Data Science helps businesses gain actionable insights from various sources of structured and unstructured data by applying scientific methods, processes, and systems. It requires a proper understanding of the different techniques used for preparing the data and knowledge about various data models that may be used to finally measure the outcome from the full process. In this entire cycle, there may be numerous factors that may be overlooked even by the most seasoned data scientists. Through this article, we share some of our insights on some of the most common mistakes made by data scientists.   Growing demand for data scientists and their role in the information age According to a recent survey made by KMPG on C-Level executives, 99% of them affirmed that big data would be a core part of their company’s strategy in the coming year. According to Accenture, the world will generate 463 exabytes of data per day in 2025. This is equivalent to 2.5 billion gigabytes or 2.5 quintillion bytes. This will create a greater demand for data scientists with key skills for extracting actionable insights from data. A striking example is that of the popular social networking site LinkedIn where data scientists have played a vital role in boosting business intelligence for the company. LinkedIn relies mainly on the data that is transferred by its 3,80,000 users who have built connections with each other. LinkedIn is utilizing the skills of such professionals to explore the world of Big data. Apart from LinkedIn, other big names such as Google and Facebook are utilizing the role of data scientists to give a better structure to large quantities of formless data to help them establish the significance of its value and bring a standard relationship between the variables. Most of the data architects extract information through large volumes of data and use SQL queries and data analytics for slicing these datasets. On the other hand, data scientists have a larger role to play as they need to have advanced knowledge of machine learning and software engineering to manipulate the data on their own to provide deeper insights. They mainly use advanced statistics along with complex data modeling techniques to come up with their future predictions.   What are the common mistakes made by data scientists? Failure to address the real questions The entire process of data science revolves around addressing the business questions and in most cases, it is the most neglected issue due to lack of communication between the sponsors, end-users, and the data science team. To get the most benefits from the data science initiatives, all the stakeholders need to stay connected and share information and knowledge which can help in defining the real business issues. Beginning with excessive data In many companies, the team members are involved in working on a huge chunk of data which is a waste of valuable time and effort. Instead, it can be more worthwhile for them to choose a subset of specific data to make the process much easier. For example – Is it possible to focus on just a single region or look for data from the last three months? To start with the prototype, random sampling may be taken into consideration. When the initial exploration, cleaning, and preparation are done, a bigger data set may be included in the process. Trying to complicate things Sometimes, even if the current project requires a simpler solution, data scientists often make the mistake of complicating matters by introducing more complicated models into the process. This can jeopardize the chances of completing the project on schedule and make it more difficult to achieve the main purpose. Not validating results The models that are created by the team of data scientists should enable the business to take suitable action. And once an action has been taken, it’s necessary to measure its effectiveness for which the team needs to have a validation plan ready, even before the actual implementation. Only this can help in making the process more efficient and give more meaningful results. More focus on tools than on business problems The major function of any data-driven role is to focus on solving problems through data extraction, but sometimes, the data scientists get overwhelmed and obsessed with using new tools than solving the real issues at hand. They need to understand the problem first and find out the requirements for finding the solution and finally decide on the best tools that may be used to solve the problem. Lack of proper communication There is plenty of communication involved in assessing the business problem and providing constant feedback to the stakeholders. The greatest risk comes when the data scientists do not ask enough questions and make their assumptions, which actually can result in providing a different solution than what is required.   The Key Ingredients of Data Science Data science requires knowledge of Statistics and Applied Maths Data science requires actual application of Statistics along with Applied Maths, which can provide guidance regarding uncertainty in data and allow companies to gather valuable insights from it Data Science involves solid communication A data scientist needs to be an effective team player who helps to initiate, iterate and drive some core decisions in the company. The role of a data scientist involves working along with product managers and the other team members to influence them to take vital business and product-related decisions. Data Science is about using creativity and dealing with people Data scientists need to have a creative approach as they need to understand the needs of the users in the system and convey their findings to the other core members of the team. At the same time, they need to be creative enough to derive insights from the system that generated the data in the first place. In this age of Big data, the biggest challenge will be on collecting data and extracting value from it which will get more demanding in the coming years. Data Scientists will have a key

Data Analytics

Banking Compliance is Becoming Harder – How Analytics Can Help

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With a new regulatory alert being issued every 7 minutes, growing compliance regulations are challenging banking institutions in a variety of ways. Changing customer behavior, and the constant evolution of technology is compelling them to change how compliance is approached. Ensuring compliance with a rising number of government and industry regulations can be hard-hitting and put a strain on the already drained resources. While traditional compliance models were effective for an era where simple enforcement was sufficient, today, they offer a limited understanding of business operations and underlying risk exposures. With the risk of regulatory sanction, reputation and financial loss, due to a failure to observe compliance obligations becoming extremely far-reaching, those who adapt best are the ones to enjoy a distinct competitive advantage. As each new industry regulation and its associated deadline causes a massive influx of new data that has to be stored and analyzed, garnering insights rapidly becomes vital for optimizing processes and pinpointing any potential problems areas. With compliance costing businesses $5.47 million annually and non-compliance $14 million, analytics is enabling organizations to keep pace and avoid the risk of costly non-compliance. It is helping banking organizations to stay ahead of compliance requirements, and better anticipate and respond to change. Here’s how analytics can help with banking compliance: Unearth reporting insights: Institutional banking clients, as well as regulatory auditors, constantly demand banks to reveal risk and possible exposure scenarios. Real-time analytics is a critical aspect here that allows banks to handle high volumes of data and unearth insights that meet the growing compliance needs. Using analytics, organizations can collect and distribute necessary compliance data to deliver reporting insights that are required throughout the enterprise, and meet regulatory requirements with ease. Improve risk control: Since non-compliance can result in substantial losses, analytics can help scale up the computational power of risk management. Decision-makers can ask more complex questions and get more accurate answers faster while developing new business strategies. Analytics-aided techniques can produce more accurate regulatory reports and deliver them more quickly. Since the need to pre-aggregate data is eliminated, risk managers are in a better position to understand the nuances in data, reduce fraud losses, and improve risk control across the enterprise. Enhance productivity: As banks need to be always ready to provide regulators with a quick response to regulatory stress tests, analytics plays a big role in making processes faster and more effective. Using advanced analytics, organizations can achieve faster and more accurate responses to regulatory requests and give teams analytics-driven decision support. Banks can use analytics to understand compliance levels across the enterprise, identify avenues that fare poorly, and take measures to enhance productivity and save money. Drive agility: With thousands of new regulatory requirements being ushered in every year, manually managing compliance activities is a fruitless undertaking. Manual compliance efforts are not only cumbersome and tedious, but they are also extremely prone to error. This increases the degree of risk and limits a company’s ability to meet growing regulatory requirements. Analytics allows organizations to better manage risk and compliance obligations; by aggregating data that’s needed from across the business, analytics paves the way for greater reporting accuracy and efficiency. Using analytics, organizations can respond quickly to the evolving regulatory landscape, and drive agility. Lower costs: With massive legacy and personnel costs going towards regulatory and financial reconciliation, firms have a pressing need to comply at a lower total cost of ownership. Since regulations and the market environment greatly hamper banks’ abilities to just throw money at the problem, analytics helps drive improved metrics and reporting through automation. Banks can transform raw data for cognitive and analytic processing, meet regulatory needs at a fraction of the costs, and drive higher efficiency. Effectively manage compliance Banking and other financial services companies have to contend with a variety of industry regulations and compliance requirements. As the time and cost of regulatory compliance and reporting vastly increases with every new regulation, keeping up is a great cause for additional stress – especially at a time when new competition and increasing customer demands is creeping from the sides. Advanced analytics is enabling the banking industry to become smarter in managing the myriad challenges it faces – by offering compliance officers enterprise-wide intelligence, analytics can help avoid financial non-compliance and stay a step ahead. Analytics-backed solutions are enabling banks to not only manage the increasing cost of compliance, but also the risk of non-compliance – both monetary and reputational.

python for data science - rubiscape blogs
Data Modelling, Data Science

Why Does Python Rule the World of Data Science?

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  As of 2020, GitHub and Google Trends rank Python as the most popular programming language, surpassing longstanding Java and JavaScript in popularity. Python is a general-purpose and high-level dynamic programming language that focuses on code readability. After being founded in the year 1991 by Guido Van Rossum, Python has only soared in popularity. Its syntax allows programmers to write codes in fewer steps as compared to Java or C++. Some of the other reasons behind Python’s popularity include its versatility, effectiveness, ease of understanding, and robust libraries. Python’s high-level data structures and dynamic binding make it a popular choice for rapid application development. Data scientists usually prefer Python over other programming languages. But what exactly makes Python suitable for data science? Why do data scientists prefer working with Python? Let’s find out – The Benefits of Python A big reason why Python is widely preferred is because of the benefits it offers. Some of the major benefits of Python are – Ease of learning: Python has always been known as a simple programming language in terms of syntax. It focuses on readability and offers uncluttered simple-to-learn syntax. Moreover, the style guide for Python, PEP 8, provides a set of rules to facilitate code formatting. Availability of support libraries: Python offers extensive support for libraries including those for web development, game development, or machine learning. It also provides a large standard library that includes areas like web services tools, internet protocols, and string operations. Moreover, many high-use programming tasks are pre-scripted into the standard library. This significantly reduces the length of the code that needs to be written. Free and open-source: Python can be downloaded for free and one can then start writing code in a matter of minutes. It has an OSI-approved open-source license. This makes Python free to use and distribute. Being open-source, Python can also be used for commercial purposes. A vibrant community: Another benefit of being an open-source language is the availability of a vibrant community that keeps actively working on making the language more user-friendly and stable. Its community is one of the best in the world and contributes extensively to the support forums. Productivity – The object-oriented design of Python provides improved process control capabilities. This, along with strong integration and text processing capabilities, contribute to increased productivity and speed. Python can be a great option for developing complex multi-protocol network applications. Easy integration – Python makes it easy to develop web services by invoking COM or COBRA components, thanks to enterprise application integration. It possesses XML and other markup languages that make Python capable of running on all modern operating systems through the same byte code. The presence of third-party modules also makes Python capable of interacting with other languages and platforms. Characteristic features – Python has created a mark for itself because of some characteristic features. It is interactive, interpretable, modular, dynamic, object-oriented, portable, high-level, and extensible in C++ & C. Why Python is Ideal for Data Science Functions Data science is about extrapolating useful information from large datasets. These large datasets are unsorted and difficult to correlate unless one uses machine learning to make connections between different data points. The process requires serious computation and power to make sense of this data. Python can very well fulfill this need. Being a general programming language, it allows one to create CSV output for easy data interpretation in a spreadsheet. Python is not only multi-functional but also lightweight and efficient at executing code. It can support object-oriented, structural, and functional programming styles and thus can be used anywhere. Python also offers many libraries specific to data science, for example, the pandas library. So, irrespective of the application, data scientists can use Python for a variety of powerful functions including casual analytics and predictive analytics. Popular Data Science Libraries in Python As discussed above, a key reason for using Python for data science is because Python offers access to numerous data science libraries. Some popular data science libraries are – Pandas – It is one of the most popular Python libraries and is ideal for data manipulation and analysis. It provides useful functions for manipulating large volumes of structured data. Pandas is also a perfect tool for data wrangling. Series and DataFrame are two data structures in the Pandas library. NumPy – Numpy or Numerical Python is a Python library that offers mathematical functions to handle large dimension arrays. NumPy offers vectorization of mathematical operations on the NumPy array type. This makes it ideal for working with large multi-dimensional arrays and matrices. SciPy – It is also a popular Python library for data science and scientific computing. It provides great functionality to scientific mathematics and computing programming. It contains submodules for integration, linear algebra, optimization, special functions, etc. Matplotlib – Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is a useful Python library for data visualization. Matplotib provides various ways of visualizing data in an effective way and enables quickly making line graphs, pie charts, and histograms, etc. scikit-learn – It is a Python library focused on machine learning. scikit-learn provides easy tools for data mining and data analysis. It provides common machine learning algorithms and helps to quickly implement popular algorithms on datasets and solve real-world problems. Conclusion Python is an important tool for data analysts. The reason for its huge popularity among data scientists is the slew of features it offers along with a wide range of data-science-specific libraries that it has. Moreover, Python is tailor-made for carrying out repetitive tasks and data manipulation. Anyone who has worked with a large amount of data would be aware of how often repetition happens. Python can be thus be used to quickly and easily automate the grunt work while freeing up the data scientists to work on more interesting parts of the job. If you’d like some help with leveraging the power of data, then you can get in touch with us at www.rubiscape.com

Data Modelling

Why Enterprises are More Interested in Startup Solutions

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Large enterprises have a lot working to their advantage – large budgets, a huge global workforce, global supply chains, and salesforce…the works. For a startup, these factors can look intimidating. But it pays to remember that every global giant was once a small fish in a big pond. It is by taking advantage of their strengths that they became the behemoths of today. The startup narrative has also undergone a sea of change over the years, with some very smart solutions coming up from this space. And large enterprises looking for solutions have noticed this. Here are some compelling reasons why some enterprises are taking a keen interest in working with startups to meet their needs. Agility Agility is perhaps one of the greatest advantages of a startup environment. Today businesses are evolving. Markets are changing and experiencing a state of constant disruption. Business models are in a state of constant evolution. Customer demands and expectations are becoming increasingly fluid. Technology changes and advancements are demanding organizations to come up with new solutions continuously. Given the smaller setup, startups gain an advantage here as it helps them remain more nimble and agile to change. What is becoming clear in today’s age is that success does not necessarily come from the position, scale, or first-order capabilities. Rather it comes from ‘second-order’ capabilities…capabilities that allow organizations to foster rapid adoption and the capacity to act on the signals of change. This is where startups score big as their internal structure allows them to become more responsive to change, come up with creative solutions to pressing problems faster and hence deliver what the customers want, when they want it. Greater risk-taking capabilities Startups have greater risk-taking capabilities than their larger counterparts. This is primarily because there is no bureaucratic red tape to navigate to implement change. Since the needs of the customer are at the heart of the startup culture, it is their needs that dictate the risks the startup needs to take. Initiating a change process, altering a roadmap, changing technology to meet the needs of a product, etc. are much easier and faster in a startup set-up because of the absence of slow-moving decision hierarchies. Access to the latest technologies and trends Technology startups usually work with the latest, and some of the most trending technologies. Their market positioning also demands them to stay updated on the latest technology trends. Want to know which direction UI and UX are heading? Ask a startup. The resource pool of technology experts working in startups is also adept at coming up with creative solutions to pressing problems using the latest and the most relevant technology stack. Large organizations can gain access to qualified and trained professionals without incurring the cost and expending the time and the effort to locate and hire a trained resource by simply working with a startup. This becomes even more relevant as technologies such as AI and Machine Learning start becoming more mainstream, and accessing top talent becomes harder. Since most startups work in a niche area, they work with niche technologists to develop robust and relevant solutions to suit market demands. Rapid prototyping The early stages of designing technology solutions demand the capability to build a working prototype to go-to-market faster. Rapid prototyping is much easier in a startup environment because of a short feedback loop. Along with this, startups don’t have complicated, interconnected, and rigid tech stacks. With clearer communication between stakeholders, access to the latest technologies, low technical debt, and a willingness to come up with a compelling solution, startups become more adept at addressing stakeholder engagement, client demands, retrospectives, and build smart alignments that contribute to rapid prototyping. It is these same capabilities that make it easier for startups to make greater customizations for their customers. Feedback-driven When change is the only constant, it becomes imperative to be open to feedback and have the velocity to implement it. Changing product requirements are a given today – stakeholders can rethink the requirements and features. End-users might demand new features and functionalities. The technology choice might need an overhaul keeping business evolution in mind. New elements might need to be introduced to make the product more attractive and useful. Startups are adept at incorporating all the feedback owing to the absence of bureaucracy and structural flexibility because of smaller, tighter-knit teams. This helps them make group decisions based on feedback faster and implement change without impacting the velocity of development. Today, collaboration has moved from becoming a buzzword to a business imperative. It has become essential for innovation. Organizations that enable collaboration are successful. Those who don’t have to implement it eventually. A similar collaboration between large enterprises and technology startups can be the key to foster innovation across geographies and benefit both sides – the large enterprises to create and enter new markets and the startups to develop their solutions and to scale. It’s a win-win for both.

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