Author name: Rise Institute

Any software project’s main goal is to produce high-quality work while spending less money and taking less time to complete. Regular software testing helps achieve this. Any IT project must include software testing as a crucial component. The software is examined for errors and problems that might have a negative impact on the user experience. Testing is primarily divided into two categories: Manual Testing and Automated Testing. This Tutorial on Automation Testing will teach us everything there is to know about test automation. Let’s look at the subjects this tutorial on automation testing will cover: What is testing by automation? What separates manual testing from automated testing How are test cases chosen for automation? How are automated tests carried out? Various methods for automation testing Tools for Automation Testing You can watch this lecture on automation testing where our automation test engineer course expert goes over every last technical detail. What is the definition of Automation Testing? Automation testing is the practise of running test cases by repeatedly carrying out pre-defined actions with the aid of tools, scripts, and software. The goal of test automation is to replace manual human labour with more efficient systems or tools. Any software product’s success depends on testing. The likelihood is that most people won’t even buy or use your software, or if they do, it won’t be for very long, if it doesn’t operate properly. However, manual testing to find flaws or bugs is costly, time-consuming, frequently repetitive, and prone to human error. Automation enters the picture at this point. For software development teams to keep up with the increasing demands for higher-quality software at breakneck speeds, automation is essential. One of the key choices you’ll need to make when you start testing is whether to use automated testing or manual testing. As a result, you need to be aware of how manual testing and automated testing differ greatly. Difference between Manual Testing and Automation Testin Accuracy & Reliability Automation Testing High, as tools and scripts are used Manual Testing Low, as manual tests are more prone tohuman error Time required Automation Testing Relatively Low Manual Testing Relatively High Investment Cost Automation Testing High, Return of Investment(ROI) is high  Manual Testing Low, Return of Investment(ROI) is low Usage Automation Testing Suitable for  Regression Testing, Performance Testing, Load Testing  Manual Testing Suitable for Exploratory, Usability and Ad hoc Testing Human Element Automation Testing No human observation involved Manual Testing Allows for human observation to find out any glitches Customer Experience Automation Testing No guarantee of positive customer experience Manual Testing Helps in improving the customer experience Technically, neither of these choices is superior to the other. However, project size, budget, and time constraints will undoubtedly be deciding factors that influence which approach will work best in your testing procedure. An automated testing programme might miss insights from a test that a human mind can gain from manual testing. While large projects that call for repeatedly testing the same areas are better suited for automated testing. Can we automate each test case, though? What tests should be automated? Since it is impossible to automate all testing, it is crucial to choose the test cases that should be done so. There are a few test cases where automation can be used: Automating repetitive tasks is a good idea. These tasks not only bore you, but you also frequently make mistakes on them. Invest in a tool or automation strategy that will handle data export, number crunching, and complex graph creation for you rather than doing these things manually. For tests that call for multiple data sets, automation is an option. Automate the process so that data is entered into forms rather than by hand. Load testing is another excellent scenario in which automation can be used. For test cases that execute on a variety of hardware or software platforms and configurations, automation is an option. You now understand when to conduct automation testing. In the following section of this tutorial on automation testing, we’ll go over a step-by-step process for creating an automation plan. Visit Automation Testing Certification by Rise Institute if you want to learn more about automation after this blog tutorial. What is Automation Testing and how is it done? Test automation success requires thorough planning and design. When automating a process, the following steps are taken: Choosing a Testing Tool Any process begins with a definition, so you should define the purpose of automation before applying it to test automation. Once you are certain of the tests you are running, you must choose your tool. Although there are many different types of testing tools available, it’s crucial for automation to pick the one that best fits your test requirements. When choosing an automation tool, bear the following essential advice in mind: Is it simple to create and maintain the tool’s scripts? Does it function on platforms such as desktop, mobile, and the web? Does the tool have a feature for reporting test results? What kinds of testing can this tool support? How many languages can you use the tool with? Define the reach of Automation The scope of automation is then defined, meaning you must choose which test cases to automate. You can adhere to the following advice: Scenarios with a lot of information Tests for functions that are common across applications Technology readiness How much of a business’s components are recycled The difficulty of the test cases Development, Design, and Planning You should choose the tasks your automated tests will carry out after deciding your goal and the types of tests to automate. Included in planning, design, and development are: Creating Test Cases: Create the test cases you want. Make sure to test more than just one aspect of the behaviour of the application when making test cases. Editing and debugging large, complex automated tests is always very challenging. It is best to break up your tests into a number of smaller, more straightforward tests. Creating Test Suites: Create test suites that

Programming and coding are fascinating topics. It makes it possible for people to come together every day to produce something original, exchange ideas, grow, resolve problems, and learn new things. Programming and software development will be among the most in-demand professions in 2023, claims Business Insider. We’ll walk you through the most frequently asked programming interview questions for 2023 in this blog. This blog post on programming interview questions will go over every question you might come across, from entry-level to expert roles, in almost any interview. Therefore, without further ado, let’s look at the questions. So without further ado, let’s get started with the top programming interview questions for 2023! Let’s get started with some basic programming questions in this blog post on programming interview questions. Questions for Beginner Programming Interviews What exactly is computer programming? The process of carrying out a specific computation through the design or construction of an executable computer programme is known as computer programming. Analysis, algorithm generation, resource consumption profiling, and algorithm implementation are some of the tasks involved in programming. What is the process of programming? A set of instructions called a programme allows the computer to carry out various tasks. Each of those commands can be carried out and serves a particular purpose. Describe an algorithm. An algorithm is a set of guidelines and rules that tell computer programmes how to carry out tasks. Algorithms work by following predetermined steps with input data to produce an output. Algorithms are essential when designing automation and are used in many different programmes, including search engines. What exactly are constants? Data values known as constants stay the same no matter when a computer programme is launched. Examples include name constants, literal constants with shortened names, and constants that are embedded in the source code. Describe a data structure. Data structures are systems designed specifically to simplify functions and organise data. Stacks, linked lists, and arrays are a few examples of data structures. What Is Debugging? The act of debugging involves locating and fixing programmatic errors. The programme is thoroughly examined for errors during this process. Errors are then identified and investigated. When does a syntax mistake happen? When a programmer uses the incorrect syntax when writing an instruction, a syntax error happens. When a programmer enters an incorrect line of code, they have made a syntax error. Missing punctuation or misspelt names are the two most common syntax mistakes. In either a compiled or an interpreted programming language, a syntax error prevents the code from running. Let’s get started with some intermediate programming interview questions in this blog post on programming questions. When does a runtime error happen? When a programme is syntactically sound but has a bug that is only discovered during programme execution, it is said to have a runtime error. The Java compiler is unable to catch these errors during compilation; instead, the Java Virtual Machine (JVM) only notices them when the application is running. When does a logical mistake happen? A logical error occurs when the incorrect logic is used by a programme. For a logical error, the translator (compiler or interpreter) does not report any error message. The most challenging errors to find are these ones. Describe a flowchart. The flowchart is a visual representation of a computer programme that aids in comprehending how data and control flow through an algorithm. What was your first programming language? In the interview, it would be beneficial to be sincere. What programming language did you learn first? Was there a JavaScript mistake? Python? This question is intended to test your programming language proficiency, among other things. No matter where you started, be sure to brush up on the specifics of the language you intend to discuss. What exactly are loops? A structure known as a loop allows statements to be repeated up to a predetermined number of times or until a predetermined criterion is met. Data Abstraction: What Is It? A body of information is reduced to its core components through the process of data abstraction. This method, also known as “modelling,” makes the process of developing programmes simpler. The functionality of a programme depends on only deciding to reveal data that pertains to specific user audiences because databases are complicated structures. Let’s get started with some challenging programming interview questions in this blog post on programming interview questions. Questions for Advanced Programming Interviews How does a compiler operate? A compiler is a piece of software that converts source code written in a high-level language into a set of instructions that the CPU of a digital computer can understand. A compiler transforms source code into machine-readable object code. Since it’s in binary form, this machine-readable code typically consists of zeros and ones. Now that the programme is an executable file, the compiler can run it. What Do Variables Mean? A variable, or value, is a container for a specific set of bits or type of data in computer programming. A variable is an abstract storage location with an associated symbolic name that holds some known or unknown quantity of information. What is sorting? List a few types of sorting. Sorting involves arranging data in descending or ascending order in accordance with particular relationship components between different information types. Insertion Sort involves gradually adding values to an already-existing list. Merge Sort – To order the components of each partition, elements are divided into separate groups and merged together. Quick Sort: In this sorting method, a pivot element is placed, and smaller elements are arranged to the left and larger elements to the right. Radix Sort: This method uses place values for numbers to order the data. Data is sorted into a sequence as part of a process called a heap sort. What is documentation? Documentation is a thorough explanation of the algorithm, design, coding approach, testing, and appropriate usage of a programme. Both the users who depend on the programme on a daily basis and the programmer who might be asked to update

Let’s continue this ongoing series on Python with one more article. We will examine the benefits and drawbacks of Python in this article.  Python applications Benefits of Python The drawbacks of Python Final observations on the subject Let’s start! Applications of Python: Python can be used to build desktop applets with a GUI. Developers can create desktop applets with GUI support using libraries like Tkinter, pyQt, and pyGame. Web servers programming. programming for web servers. Backend development for web servers can be done by developers using libraries like Django and Flask. Prototyping Python prototyping enables quick and efficient prototyping of any software models so that clients can see them. Game Creation Games with GUIs can be created by developers using libraries like PyGame. Data science and Artificial Intelligence Python is by far most frequently used for data science and machine learning. Python has made data processing incredibly simple with the help of its vibrant community and extensive library. Python has its own libraries for data manipulation, data visualisation, and data cleaning. If you have data, you must be familiar with Python. Having learned about Python’s applications so far, let’s move on to this article’s discussion of Python’s benefits and drawbacks. Benefits of Python Python has developed into a full-featured software development toolkit thanks to the growing python community and open source libraries. Powered by a Large and Active Community Python has one of the most well-known and active communities, which aids in the language’s ongoing improvement. It makes sense that it ranked first on stack overflow. The open source license under which the Python language is distributed makes it simple for others to contribute to its development. Learning Circle Most programming languages have learning curves that grow parabolic over time, making them difficult to grasp at first but simple to learn once you become accustomed to them. However, learning Python is simple due to its simple syntax and brief handwriting. Moving on from this article on Python’s benefits and drawbacks, External Libraries You can install a variety of modules that make Python interactive using the standard Python package installer (PIP). These libraries and modules can communicate using a variety of methods, including operating system calls and internet protocols. You can use the OS module for system calls that run user-level threads and socket programming for networking. Compatibility with Other Languages For cross-platform development, integration libraries like Cython and Jython enable Python to integrate with C/C++ and Java. Given that no language is complete and it is recommended to use a single language for all development, this gives Python even more power. With the help of these libraries, you can take advantage of the strong features that each language has to offer. Productivity The developers get a head start with Python’s “batteries included” philosophy because they don’t have to download additional modules and packages. Python’s simple syntax and quick prototyping increase productivity by about 40% when compared to traditional programming languages. Conclusion Python is currently the programming language that developers use the most, compared to other programming languages. The crucial advantages of the Python language include its simplicity in learning, checking, and reading. Installing packages and creating software in Python is simpler than in C or C++. Another advantage of Python programming is that since it doesn’t use the concepts of pointers or references, no computer virus can cause a segmentation fault. The widespread acceptance and use of Python among scientists, engineers, and mathematicians is a significant advantage it has over more established programming languages. Python is so useful for experimentation of all kinds and prototyping because of this. Python is so useful for experimentation of all kinds and prototyping because of this. Along with being used to create animation for movies, it is also utilised in deep learning and machine learning. Moving on from this article on Python’s benefits and drawbacks, Python Disadvantages The main benefits of Python, a well-liked programming language, have already been discussed. But everyone is aware that a coin has two sides! Indeed, Python has a number of shortcomings as well, which discourages developers from using it. Let’s examine each one in turn: Slow footwork Python uses an interpreter, which loads the file line by line, as opposed to a compiler, which runs the entire file at once. Compilation becomes slower as a result, and it frequently runs slowly. Python is primarily avoided by competitive programmers due to this. Compared to Python, C++ offers more computation per second. Python is not widely used in the development of applications, which is another reason. Code error detection Python uses an interpreter, so when a programme is being compiled, errors do not surface. Because Python uses dynamically typed variables, testing is more difficult. All of these errors turned out to be run-time errors, which programmers never want to have. Python is now less useful for developing GUI applets as a result. Mobile Device Weakness: Python is used in desktop programmes, web servers, and scripts, among other things. However, because it uses more memory and processes information more slowly than other languages, it is not regarded as being suitable for mobile devices. Moving on from this article on Python’s benefits and drawbacks, Large Memory Requirements When processing, Python uses a lot more memory than other languages like C/C++ because of the way it is designed. Because of this, Python is an absolute no-no for memory-constrained development. Conclusion: The language is viewed as a less effective platform for game and mobile app development. Python is frequently used on desktop and servers, but only a small number of mobile packages have seen significant usage. Runtime errors are Python’s only other flaw. The language has numerous design restrictions. Python uses an interpreter rather than a compiler to run, which slows down performance. Let’s examine the concluding remarks. Final Observations Python has a lot of drawbacks, but it is clear that overall, the benefits outweigh the drawbacks by a good margin. Every programming language has a unique strength and niche that attracts developers’

Around 92% of organizations worldwide gain useful marketing insights from data analysis, and there will be around 1,00,000 job openings that require data science skills in the world in 2023! Data scientists are in increasingly high demand as companies and organizations undergo a digital transformation. The fact that Data Science was described as “the sexiest job of the 21st century” by Harvard Business Review was not unexpected. A data scientist makes it seem as though they are always on the go. Not only do they earn more money on average than the other occupations, but they are also a company’s best bet for problem-solving. A data scientist can assist in making important decisions for a company or institution. Can anyone, though, become a data scientist? Do data scientists really make as much money as they say they do? What makes it a well-liked option among students? How does one plan their Data Science career? In this blog, we’ll take you step-by-step through the key factors that will direct your path through the vast and fascinating world of Data science. We must first acknowledge how broad and dynamic the field of data science is before we can move forward. Depending on your educational background, the discipline offers a variety of specializations and a wealth of opportunities! If you want to work in the field of data science and analytics, you should be a student or recent graduate. Here are some simple tips, techniques, and insider information to help you succeed as a data scientist. 1) The field of Data Science is fascinating. Data Science has become the most popular career path for three years in a row, according to LinkedIn US Jobs Data. It has a wide range of roles in this field, including Data Engineer, Data Analyst, Data Scientist, Business Intelligence Analyst, Database Administrator, Data Architect, Machine Learning Engineer, and many more, in addition to a high average pay scale and promising career opportunities. Understand the various roles that data science plays before diving in, and then focus on an area that particularly interests you. Depending on your educational background, you might find it easier to fit into some roles. 2) Extend beyond abstract ideas Utilize hands-on instruction to get your hands dirty. You must educate yourself thoroughly on data science. A common but serious error made when creating a data science course is packing it full of information and making it heavily theoretical without offering a follow-up practical experience to put what is learned into practice. When theory and practice are integrated, it helps students apply their knowledge and become experts in the field. Working on real-world cases and projects on the job is a key component of being a successful data scientist. To prepare you for employment, it is crucial that the courses you decide to enroll in offer a special fusion of theory and practical. 3) Look for an internship opening This needs to be read as an extension of the earlier paragraph’s point. Searching for Data Science internship opportunities is one way to acquire the experience you need to land a job. An internship will make it easier and faster for you to advance in this field. It is a surefire way to put to the test, practices, and apply the knowledge you learned while enrolled in the course. An internship will give you hands-on experience solving a problem, as well as guidance and support from experts in the field. Seek out the chance to work with public data sets and put what you’ve learned to the test. A good way to learn by doing is through an internship. 4) Acknowledging the enterprise issue One must understand the business problem that organizations are trying to solve before delving into the data. As a result, management and the data scientist will be more likely to communicate, which will help them reach the right decision and solve pressing issues. A data scientist must be able to comprehend and communicate clearly with other teams in order to ensure that all misunderstandings, confusion, and doubts are eliminated. 5) Don’t undervalue the importance of soft skills Your ability to stand out from the crowd will be aided by your soft skills. A good data scientist must be inquisitive and eager to get their hands dirty solving the issues at hand. Understanding and conversing with non-technical people depend heavily on communication. For effective communication and to increase productivity at work, one must also foster a sense of teamwork. A Data Scientist can advance into important decision-making roles within an organization with the help of analytical skills, critical thinking, and strong business acumen in addition to the qualities mentioned above. 6) Find a trustworthy mentor and keep a strong peer group. The field of data science is dynamic, as was already mentioned. Even though industries like BFSI and healthcare have long used data science, we are just now becoming aware of its full potential. A talented mentor who can guide you through your career in the best direction is essential for aspiring data scientists. A strong mentor-ship system can be helpful in guiding an aspiring data scientist towards the specialization within the field that is most appropriate for them. Similar to this, a strong peer group can support you when you run into difficulties. Maintaining this peer group is crucial for keeping you up to date on industry trends and innovations as well as for occasional self-motivation. Both of these elements will help you develop a successful career path and make your experience in the field wholesome. 7) Study, apply, and repeat The study of data is dynamic. We discover new applications for artificial intelligence and Machine learning every day. As a society, we use technology to support a faster-paced, more efficient way of life, but we are also constantly creating new innovations to improve our quality of life. Therefore, it’s critical for anyone pursuing a career in data science to stay up to date on recent advancements in the industry. A data scientist is constantly learning new

Top Data Science Courses in Mumbai: Syllabus, Durations, and Scope It’s been said that the digital world runs on data. Data science is all about analysing data to identify patterns in behavior, trends, and business interpretation. The IT industry in India is expanding rapidly. So, Rise Institute Mumbai offering Data Science Courses in Mumbai. According to Harvard, data science is the most in-demand profession for every business hub. IT businesses require qualified experts to extract important, business-critical information from the data. In conclusion, there is a huge need for Certified Data Scientists. Leading universities and ed-tech providers offer data science courses. Data is a valuable resource and will outlive the systems themselves. Tim Berners-Lee, the man who created the World Wide Web. The following are the highlights of Data Science Courses in Mumbai: To get started get enrolled in Data Science Course by Rise Institute Mumbai. It includes free brushup sessions for the duration of course as well as access to recorded videos, games, projects. Additionally, you will receive 50 hours of online learning access in addition to 32 hours of two-way live interactive classroom sessions. Following that, the course’s study materials will be made available on the LMS. Obtain the prestigious Rise Institute Alumni status, and Certification which is more significant. Get seasoned industry mentors with 10+ years of experience. Get a successful internship with Rise Institute or one of its partner companies, too. Nevertheless, you will receive more than 10 job offers per week. During the training, you can participate in Industry Projects. Benefits of Rise Institute Data Science Course You can expressly select from a variety of career options with Rise Institute. Use a variety of Data Science methods and skills in general. After that, test your knowledge using the various tools that data scientists use. In any case, you will be knowledgeable about the critical procedures for solving a data science problem. Learn Python as well, as it supports data science right away. You will indeed gain knowledge of how data scientists think and work. You will specifically learn how to create SQL queries for databases and examine relational database concepts. You will soon import and clean data sets, conduct data analysis, and create and assess data models. Nevertheless, make use of the tools, methods, and libraries for data visualisation. Rise Institute also offers the following courses: Course on machine learning Course on artificial intelligence Cyber security Training RPA with UiPath Training Foundation Course in ITIL What is data science and how can you join it? An interdisciplinary field called data science uses scientific procedures, processes, and algorithms to glean valuable information from vast amounts of data. Although the field dates back to the 1900s, its popularity has only recently increased. There are tons of valuable insights kept in databases and data lakes. The current industry can be transformed by using these data to create more cutting-edge goods and services. These data are largely unexplored, though, due to a lack of interpretation. Here is where data science is useful. Businesses all over the world are now able to act on data in an effective and efficient manner thanks to data science, which spots trends and produces insightful insights. Machine learning is a subset of AI that employs techniques to deliver AL applications, whereas data science is a subset of AI that enables data scientists to extract valuable insights from massive amounts of data using statistics, analysis, and other scientific methods. A part of machine learning called deep learning handles more challenging problems. Predictive analytics, among other things, is a subset of data science that sheds light on potential future trends based on historical data or data that has crossed multiple data lakes and sources. These terms have an interdependence pattern and are associated with the field of data science. It is important to distinguish between their areas of expertise and functions in data science even though they are frequently used interchangeably. Instead of relying on business analysts to extract insights from the data, data science promotes the use of machine learning to train models to learn from the data that is available. Data science is crucial at this point in technological development because it affects every industry in the world. Data science will have a wider scope in the near future as a dynamic field. Data science offers a wide range of career options. Information Science Foundations Applying statistics makes it easier to interpret data. Programming: To automate data manipulation, programming languages like Python and R are crucial. Data modeling: The process of formatting particular data into a database is known as data modeling. The use of graphical representations of data to highlight trends and insights is known as data visualisation. Machine learning: Machine learning is basically a group of methods for forecasting and predicting data. Big Data: The open-source distributed systems Apache Hadoop and Apache Spark enable data scientists to manage sizable amounts of data. Collaboration: Teams of data engineers, software developers, business analysts, and others frequently work together on data science projects. Lifecycle of Data Science Data analysis and decision-making are typically iterative processes in data science. Understanding the data science lifecycle is essential to maximising the benefits of each process phase. The data science lifecycle for projects involving data modeling looks something like this. Planning entails figuring out a project’s scope and potential outcomes. Making a data model: When making machine learning models, data scientists are expected to use the right resources and information. They frequently use a variety of open-source libraries and in-data tools as a result. Data model evaluation: Before deploying a model, it must first be evaluated. In order to evaluate the model’s effectiveness in light of fresh data, it generates evaluation metrics and visualisations. Explaining a model: Curating an automated explanation on significant factors, such as producing predictions and model-specific explanatory details on model predictions, is becoming more and more important for data scientists. To explain technical results to non-technical colleagues, this calls for excellent communication skills. Deploying