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Python is one of the top choices in programming languages among professionals worldwide. Its straightforward syntax allows software developers and data scientists to pick up new skills with ease. You can also find many Python projects on GitHub to practice and learn while doing.  If you are looking for Python projects with source code from GitHub, this article is for you. Whether you have been searching for “Python projects for students GitHub”or “advanced Python projects GitHub” on Google, we have got you covered. We will cover multiple popular Python projects on GitHub that you can try out for yourself.  Why Python Projects? The job market has a high demand for professionals with Python skills, but not many candidates pay attention to the advantages of using it. It has extensive support libraries and user-friendly data structures. And over the years, it has emerged as an excellent tool for building command-line applications. Learning python is an integral part of a good data science course.  You will find various open-source examples if you take a look at the Python projects on GitHub. The repository has something for everyone – from creating a simple password generator to automating routine tasks and mining Twitter Data. For beginners, an activity-based learning approach can do wonders. It can help you understand the ins and outs of the language, such as the Pandas and Django web frameworks and the multiprocess architecture. So, let’s dive in.  Learn Data Science Courses online at upGrad Python Projects on GitHub 1. Magenta This Python research project approaches to machine learning through artistic expression. Started by the team at Google Brain, Magenta is centered on deep learning and reinforcement learning algorithms that can create drawings, music, and such. Its collaborative notebooks will introduce you to the technical details of this smart tool that aims to amplify the works of original creators.  Modiply is another example of an extensible music server that you can find freely on GitHub.  Also read: Excel online course free! 2. Photon It is a fast crawler designed for open-source intelligence (OSINT) tools. The OSINT concept involves collecting data from publicly available sources to be used in an intelligence context. With Photon, you can perform several data crawling functions, including the extraction of the following: In-scope and out-of-scope URLs URLs with parameters Emails and social media accounts XML, pdf, png, and other files Amazon buckets, etc.  This is one of the easiest beginner python projects GitHub. upGrad’s Exclusive Data Science Webinar for you – How upGrad helps for your Data Science Career? document.createElement('video'); https://cdn.upgrad.com/blog/alumni-talk-on-ds.mp4 3. Mailpile This GitHub project is known for its state-of-the-art encryption functionality. It is a privacy tool backed by a large community. Primarily, it allows you to send and receive PGP encrypted electronic mails.  Mailpile’s speedy search engine can handle huge volumes of email data and organize it in a clean web interface. It uses static rules or bayesian classifiers for automatic tagging. Go through the free software and live demos on its website to find out more! Read about: Git vs Github: Difference Between Git and Github 4. XSStrike Cross-site scripting or XSS is a security vulnerability found in web applications. XSS attacks inject client-side, often malicious, scripts into otherwise benign web pages. So, the XSStrike suite was developed to detect and exploit such attacks. This open-source tool is equipped with the following features:  Four handwritten parsers An intelligent payload generator An effective fuzzing engine A fast crawler With the above parts, it analyzes the response and crafts payloads. It can also perform efficient context analysis with integrated fuzzers.  Our learners also read – python free courses! 5. Google Images Download This command-line python program can search and download hundreds of Google images. The script can look for keywords and phrases and optionally download the image files. Google Images Download is compatible with the 2.x and 3.x versions of Python. You can replicate the source code of this project to hone your programming skills and to understand its real-world applicability.  Check out all trending Python tutorial concepts in 2024. This is also one of the easier Python projects for beginners GitHub. GitHub Python projects for beginners like these are really fun to work on. 6. Pandas Project When it comes to performing flexible data analysis and manipulation, the Pandas library proves to be an excellent resource. Its expressive data structures offer many benefits over other alternatives. Have a look at some of them below. Flexibility in working with relational/labeled data Convenient handling of missing data and size mutability Intuitive data set operations, including merging, reshaping, and pivoting  Automatic data alignment features with additional capabilities While looking through the panda’s codebase, you will come across various issues in the documentation. This may prompt you to contribute your own ideas and improve the existing tool. You can find the open-source package on GitHub along with other packages like Django and Keras that enable fast experimentation.  pandas is a great data analysis project in Python GitHub that you can try out. You can also use pandas for other EDA projects in Python GitHub. 7. Xonsh Interactive applications require command-line interpreters like Unix. Such operating systems control the execution using shell scripts. Now, instead of making a trade-off, wouldn’t it be more convenient if your shell could understand a more scalable programming language? Herein enters Xonsh (pronounced ‘Konk’). It is a Python-powered shell language and commands promptly. This cross-platform language is easily scriptable and comes with a vast standard library and types of variables. Xonsh also has its own virtual environment management system called vox.  Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 8. Manim Manim is short for Mathematical Animation Engine. This project is about programmatically creating video explainers. The program runs on Python 3.7 and produces animated video content, covering complex topics with the aid of illustrations and display graphs. You can watch these videos on the 3Blue1Brown YouTube channel.  The source code for Manim is freely available on GitHub. You can also refer to online tutorials to learn how to install the package, run a project, and create simple personal projects.  Read: Data Science Project Ideas and Topics 9. AI Basketball Analysis This project is built on the concept of object detection. The artificial intelligence application digs into the collected data to analyze basketball shots. You can easily find the AI web app and API under Python Projects on GitHub. Let us look at how the tool works: You upload files to the web app Alternatively, you can submit a POST request to the API The OpenPose library implements calculations The web app produces results based on the shooting pose data This is one of the most popular artificial intelligence projects with source code in Python (GitHub) among young developers. 10. Rebound It is common for computer program source codes to encounter compiler errors. Rebound can instantly fetch StackOverflow results in such a scenario. It is a command-line tool written in Python and built on the Urwid console user interface. If you choose to implement this project, you can learn how the Beautiful Soup package scrapes StackOverflow content. You can also familiarize yourself with the subprocess that catches the compiler errors.  11. NeutralTalk You can fine-tune your knowledge of multimodal recurrent neural networks with NeutralTalk. It is a Python and NumPy project which focuses on describing images.  Typically, image caption generation methods involve a fusion of computer vision and natural language processing. The system can understand scenes and produce descriptions of the content observed in a picture.  If you are looking for the latest captioning code, you can refer to NeutralTalk2. Written in Lua, a lightweight and high-level programming language, this project is faster than the original version.  Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? This is also a project that can seem interesting for developers who are looking for chatbot project in python with source code from GitHub. 12. TensorFlow Projects TensorFlow is a Python library used for building deep learning models. The Model Garden repository centralizes many code examples for TensorFlow users in one place. It aims to showcase best practices for research and product development while providing ready-to-use pre-trained models. With the help of this official resource, you can explore how to implement distributed training and solve computer vision and NLP problems.   13. Maps Models Importer Maps Models Importer works by importing 3D models from extensive maps. It is an experimental tool containing only a Blender add-on and the process requires 3D content software, such as Google Maps. In this project, you can get the hang of importing models from Google Maps. Also Read: Python Project Ideas and Topics for Beginners How to build your Python Project GitHub? Making GitHub projects with source code in Python is a skill that every software engineer and data scientist should master. You’ll discover the steps as you go. Just be certain you have enough time. Although creating free python projects with source code GitHub, may seem difficult, you don’t have to be an experienced pro. Additionally, you don’t require a complex product concept. You do require patience and determination. With any luck, this advice will enable you to use neither as much and help you create your best GitHub Python project.  Here are some steps that will help you build your perfect GitHub Python projects –  Step 1 – Create the plan basics  Our final goal is to create a very basic library that can be included in a Python programme. Our package’s initial version will let a user understand the prospects of the project and use it further. Step 2 – Choose the perfect name for your your Python project GitHub This is also quite important in building a project python Github. An easy-to-pronounce, an impactful name can have a huge influence on the popularity of your Python package. Naming anything is challenging. Names must be distinctive, succinct, and memorable. Additionally, they must be entirely lowercase and must not contain any dashes or other punctuation. Underscores can be avoided. Ensure the name is accessible on GitHub and Google while you construct your project python GitHub. Step 3 – Create a GitHub account  To make a free python projects with source code GitHub, you must first create an account on the platform. You can create a free account and install GitHub on your system to make your process easier.  Step 4 – Create an organization on GitHub Create a brand-new Github organisation.  The purpose behind this is to understand how to set up an open source project for the community; however, you may establish the source on your personal account for your GitHub projects with source code in Python.  Step 5 – Set up the repo  Start with creating a new repository. Choose a ‘.gitignore’ to add from the dropdown menu. Elect Python as your repository language. Your ‘.gitignore’ file’s content will match the directories and file types you want to exclude from your Git repository. Later, you may modify your ‘.gitignore’ file to remove more pointless or delicate items. You may select a licence from the dropdown list labelled ‘Add a licence’. What users of your repository material may do is outlined in the licence. Different licences offer different levels of flexibility. If no licence is specified, default copyright laws will be followed. You may choose the licence perfect for your project.  Step 6 – Add Directories Choose where you want to clone the repository. You may also create a subfolder of your primary files. Ensure that the name does not have any spaces in the middle. Initially, this file may remain empty, and the files placed in the folder need to be imported. You may also create another file that can be referred to while using your package.  Step 7 – Create setup.py The build script for your package is contained in the setup.py file. Your package will be built using Setuptools’ setup function before being uploaded to PyPI. Details about your program, its version number, and any additional packages needed by users are all included in Setuptools. Step 8 – Build the first version of your project  This is a very important step. You must finish the coding and create a draft version of your project that must include all the functions. Once built, you must test all the functions, and if it works well, you are ready to launch! Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Setting Up Your Python Projects (GitHub) Magenta Automated Install (with Anaconda): We can try using an automated installation script If we are running Ubunty or Mac OS X. Here is the command: curl https://raw.githubusercontent.com/tensorflow/magenta/main/magenta/tools/magenta-install.sh > /tmp/magenta-install.sh bash /tmp/magenta-install.sh We have to open a new terminal window once the script completes itself so that the environment variable changes can take effect. Now, the Magenta libraries are available to us for use within Jupyter notebooks Python programs and Python programs, and the Magenta scripts have been installed in our system’s path. Note: We will need to run conda activate magenta (or source activate magenta for older conda versions) to use Magenta every time we are opening a new terminal window. Manual Install (without Anaconda): If the automated script fails for any reason, or you prefer installing it by hand, do the following steps. Installing the Magenta pip package: pip install magenta Note: In order for us to install the rtmidi package that Magenta depends on, we may need to install headers for certain sound libraries. Command for Ubuntu Linux for installing the necessary packages: sudo apt-get install build-essential libasound2-dev libjack-dev portaudio19-dev Command for Fedora Linux for installing the necessary packages: sudo dnf group install “C Development Tools and Libraries” sudo dnf install SAASound-devel jack-audio-connection-kit-devel portaudio-devel The Magenta libraries can now be for used by us within Python programs and Jupyter notebooks, and the Magenta scripts are installed in our system’s path. We can now train various models and use them to generate music, audio, and images. Instructions for each model can be found in the ‘models’ directory. We first need to set up the full Development Environment if we want to work on Magenta.  Here are the steps: Cloning the magenta repository: git clone https://github.com/tensorflow/magenta.git Installing the dependencies by changing to the base directory and executing the setup command: pip install -e . You can now edit the files and run scripts by calling Python as usual. For example, this is how you would run the melody_rnn_generate script from the base directory: python magenta/models/melody_rnn/melody_rnn_generate –config=… You can also install the (potentially modified) package with: pip install . Before creating a pull request, please also test your changes with: pip install pytest-pylint pytest PIP Release: To build a new version for PIP, bump the version and then run: python setup.py test python setup.py bdist_wheel –universal twine upload dist/magenta-2.1.0-py2.py3-none-any.whl Note: We are using magenta-2.1.0 but this will be replaced with the actual version number you are releasing. If you are looking for this Python project with source code GitHub, we have got your back.  You can check out this link: magenta by Magenta, Github Pandas Installing pandas is a very easy process. You can install it using pip. Here are the steps: Installing pandas: First open your terminal or command prompt and run the following command to install pandas using pip: pip install pandas If you’re using a virtual environment, make sure it’s activated before running this command. If you’re using a Jupyter notebook, you can run the installation commands directly in a notebook cell. Verifying installation: After the installation is complete, you can verify it by importing pandas in a Python script, interactive shell, or Jupyter notebook: import pandas as pd Checking version: To check the installed version, you can run the following in a Python environment: import pandas as pd print(pd.__version__) This will print the version of pandas that you have installed. Now, let us look at a simple example program using pandas to create a pandas DataFrame and perform basic operations: import pandas as pd # Creating a DataFrame data = {‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’],         ‘Age’: [25, 30, 35],         ‘City’: [‘New York’, ‘San Francisco’, ‘Los Angeles’]} df = pd.DataFrame(data) # Displaying the DataFrame print(“Original DataFrame:”) print(df) print(“\n”) # Accessing columns print(“Column ‘Name’:”) print(df[‘Name’]) print(“\n”) # Adding a new column df[‘Occupation’] = [‘Engineer’, ‘Developer’, ‘Designer’] # Displaying the modified DataFrame print(“Modified DataFrame with new column ‘Occupation’:”) print(df) print(“\n”) # Filtering rows print(“People older than 30:”) print(df[df[‘Age’] > 30]) print(“\n”) # Basic statistics print(“Basic statistics:”) print(df.describe()) This above program is involves the creation of a DataFrame, accessing columns, adding a new column, filtering rows based on a condition, and computing basic statistics. You can run this script in a Python environment or save it as a .py file and execute it using the command line. Pandas is one of the most popular data science projects in python with source code GitHub. Photon Here are the general steps for installing the Photon web crawler from GitHub: Clone the Repository: Open your terminal or command prompt and use the git clone command to clone the Photon repository: git clone https://github.com/s0md3v/Photon.git Navigate to the Project Directory: Change into the newly cloned directory: cd Photon Install Dependencies: Check if there is a requirements.txt file in the project and install dependencies using pip: pip install -r requirements.txt Run Photon: You can run the program with the command below and a link such as: python photon.py -u http://example.com (I am using ‘google.com’ but you can replace this with the target URL you wish to crawl. Note: Depending on your Python environment, you might need to use python3 instead of python if you have both Python 2 and Python 3 installed. Photon has various options and parameters. You can explore them by running: python photon.py –help We should always check the project’s official documentation or README for any specific instructions or configurations. Additionally, ensure that you have Python and pip installed on your system before attempting to install and run Photon. If you are looking for this Python project with source code GitHub, you can check out this link: Photon Repository Google Images Download Let us learn how to use google_images_download, one of the most popular tools for downloading google images. Here are the steps for installing and using it: Install the Package: Like in the other programs, we open the terminal or command prompt and use the pip command to install google_images_download: pip install google_images_download Run the Tool: After installation, we can run the tool using the following command: googleimagesdownload Follow the Prompts: The tool will prompt us to enter various parameters, such as the search query, the number of images to download, and other options. We can customize our search by providing specific keywords, image size, file types, etc. Here is an example of me running the following command to download 5 images of “cats” automatically from Google Images: googleimagesdownload –keywords “cats” –limit 5 The downloaded images will be saved in a folder named downloads by default. Explore Additional Options: The tool provides various options that we can explore by running: googleimagesdownload –help Mailpile Here is how you can install Mailpile: Clone the Repository: Use the git clone command to clone the Mailpile repository: git clone https://github.com/mailpile/Mailpile.git Navigate to the Project Directory: Change into the newly cloned directory: cd Mailpile Install Dependencies: Check if there is any documentation or README file specifying dependencies and install dependencies accordingly. This might involve using a package manager or installing required Python packages. Run Mailpile: Start Mailpile. ./mp (This command might be different based on the project’s structure or setup.) Access Mailpile: Once Mailpile is running, access it through your web browser. By default, it might be available at http://127.0.0.1:33411/. If you are looking for more Python projects with source code from GitHub, you can follow our blogs. Future Scope for Python The modern industry is increasingly looking to discover hidden patterns from data pools. Moreover, emerging technologies like artificial intelligence and machine learning add new capabilities and complexities to the landscape. And high-level language like Python is integral to software development and analytics procedures. Naturally, present-day recruiters place immense value on Python skills when they hire for roles like data scientist, Data/research analyst, Python developer, DevOps engineer, etc. Technology bigwigs like Google, Facebook, Spotify, Netflix, Dropbox, and Reddit offer lucrative career options to candidates with practical training.  We hope that you can polish your programming skills with the above list on Python projects on GitHub. As the big data market evolves and expands further, Python’s open source community is expected to release even more libraries in the coming years. So, stay up to date and keep learning! If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Hemant

19 Feb 2024

Blogs

5738

Source The market has grown customer-centric like never before, and this has also intensified the dependency on product managers. Product Managers are the people who hear and understand the customers’ voices and customize the product to their requirements. Their customer-centric approach enables the product team to develop and deliver better-designed and higher-performing products. At every phase of a product’s life cycle, from development, positioning, and pricing to customer delivery, a product manager’s role is crucial as the customers’ advocate. If you are into the role of product management, you understand that one of the key facilitators that let you do your job more efficiently is a product management software. For creating tailored products/solutions for the customers, you need to hear all sides of a story. Before arriving at a clear decision, you need to synthesis and assess different perspectives. Read: Product management jobs and their career prospects. In the process, you require taking up different roles like a subject expert, a growth hacker, a retention strategist, and a solution seeker. To do all of it correctly, you require being handy with the best product management tools within the esteemed time frame. Source  Prospering in the Field of Product Management  The promising career path from Product Manager to Chief Product Officer and beyond starts with the ability to formulate and ask the right kinds of questions that identify the customer issues and eventual market opportunities. The final goal of product management is to present a product that emphasizes users. To be an efficient product manager, you need to master skills like market research, user design, analytics, engagement & growth, product development, and planning. Along with all these skills, you need a comprehensive product management software to assist you in handling the different responsibilities that this role entails. If you are already into the career of product management, then here is a list of five product management tools that you must check out to increase your work efficiency. Explore our Popular Business Management Courses Leadership and Management in New-Age Business Post Graduate Certificate in Product Management Executive Post-Graduate Programme in Human Resource Management Professional Certificate Programme in HR Management and Analytics Executive Post-Graduate Programme in Healthcare Management Executive Management Programme in Strategic Innovation Digital Marketing and Business Analytics Certificate Programme in Finance for Non Finance Executives Certificate Programme in Operations Management and Analytics Global Master Certificate in Integrated Supply Chain Management upGrad's Job Linked Advanced General Management Program from IMT Ghaziabad Global Professional Certificate in Effective Leadership & Management Advanced General Management Program Strategic Human Resources Leadership Cornell Certificate Program Digital Transformation Cornell Certificate Program Executive Leadership Cornell Certificate Program Management Essentials Business Management Courses 5 Product Management Tools that you Should Include in your Product Stack 1. Productboard Everything related to product management can be handled using this ProductBoard. The software makers claim that it can help you get the right products to the market faster. The software is instrumental in helping you understand the users’ needs, prioritize what to build next, share your roadmap, monitor progress, and engage with the customer community. It facilitates all these with its features like insights repository board, scoreboards, user impact scoreboard, user segmentation, competitive analysis, status monitoring, data visualization customizable boards, app integration, etc. You can check out the product through its free trial, available on their website. 2. GoodDay GoodDay is much more than just a product management software; it is a comprehensive work management one-stop platform with amalgamated tools for managing different. These jobs include planning, task organization, product or project execution & management, track, and ing progress KPI, productivity growth, and results. The interface of the software is highly user friendly. It provides you around 20 ‘at-a-glance view’ layouts like Task List, Gantt Chart, Workload, Pastdue, etc. These layouts are related to task management, planning & resource management, events & milestones, management views, etc. These layouts let you quickly view what’s going on, get an overview of your overall workload, comprehend current priorities, streamline the work pipeline. Top Essential Management Skills to Learn SL. No Top Management Skills to Learn 1 Consumer Behaviour Online Certification Financial Analysis Certification FinTech Certification Online 2 HR Analytics Certification Online Communication Courses Online Effective Communication Certification 3 Research Methodology Certification Mastering Sales Certification Business Communication Certification 4 Fundamentals of Journalism Certification Economics Masterclass Online Certification 3. Aha! Aha is a product management software that is popular for building product road maps. You can opt for a free trial on their website. This software has prebuilt strategy templates for SWOT analysis, creating business models, articulating value prepositions, etc. Using these templates, you can create a vivid picture of your vision, set the goals and initiatives. You can customize the templates by prioritizing the right features. Data visualization is made easy with user story maps, custom feature scorecard, backlog board, etc. 4. Backlog The backlog is another popular product management software. Its highlight is that it brings project and code management together. The software is an all-in-one solution for project management, task management, issue & bug tracking, team collaboration, remote work, and others. The interface is simple enough to be quickly adopted by anyone. It is a powerful collaboration platform for product managers to connect with remote developers, clients, and other teams. Assessing and tracking progress is comfortable with at-a-glance visuals like boards, Gantt charts, Git graphs, and burndown charts. Check out: Product Manager Salary in India Our Top Management Articles Top 7 Career Options in Management To Choose [For Freshers & Experienced] Online Product Management Courses to Kickstart your Career Top 10 Career Options in Business Management in India 8 Crucial Business Management Skills Every Manager Should Have Future Scope of Management: Scope, Salary, Career Opportunities Career Options After MBA – Highest Paying Management Jobs 5 Key Skills Required for Successful Management Career & How To Achieve Those Skills? What is The Nature and Scope of Management? Importance of Management in Every Organisation – [A Complete Guide] upGrad’s Exclusive Product Management Webinar for you – How to craft GTM Strategy for a Product? document.createElement('video'); https://cdn.upgrad.com/blog/panel-discussion-on-crafting-gtm-strategy-for-a-product.mp4   5. Pendo It is a user tracking and analysis tool that can be an invaluable source of intelligence and insight throughout your product journey. It is an end-to-end product management software that lets you live-communicate with customers and spot risks related to customer retention through product engagement, surveys, and feedback. It also lets you increase the product’s efficiency by optimizing trial conversions and user onboarding in-product. The website’s interface is quite visually representative, so it is easy to understand. You can better understand the product by taking up visual product-tour available on Pendo’s website. Study Product Management Courses online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. What You Should Look for in Product Management Software? As businesses strive to stay ahead in a rapidly evolving market, harnessing the power of product management tools has become essential for success. Product management software has evolved as an essential tool for excellent product managers wanting to optimize operations, improve collaboration, and produce amazing products.  Let’s look at the features and benefits of product management tools and how it is changing how things are conceptualized, created, and delivered. Improving Communication and Collaboration Product management software improves communication and cooperation significantly by offering a centralized platform for product managers, designers, developers, and stakeholders to engage and collaborate towards a single goal. These software solutions improve communication among cross-functional teams, providing a collaborative atmosphere where ideas can be shared, comments can be received, and everyone can work together to make the product successful.  Efficient Task and Project Management Product data management software automates task and project management, allowing product managers to manage activities and projects from inception to completion. These software solutions include sophisticated functionality for task creation, assignment, and tracking, allowing product managers to distribute resources effectively and keep projects on schedule. Product management software boosts productivity and supports on-time product delivery by streamlining tasks and project management. Roadmap and Strategy Simplified Product management tools give product managers the tools to develop strategic roadmaps and link their product vision with business goals. Product managers can make educated judgments, respond to changing market dynamics, and ensure their products are aligned with customer expectations if they can prioritize features, track dependencies, and manage resource allocation. Scalability and integration Product management software is frequently integrated with other critical tools, including project management platforms, problem-tracking systems, and communication channels. This interface enables smooth data flow, reducing duplication of work and ensuring information is up to date across several platforms. Furthermore, when organizations develop, and product portfolios evolve, product management software expands, allowing new features and functions to suit changing demands. Customer-Centric Product Development Understanding and addressing client demands is fundamental to effective product management. Product management software includes capabilities that make obtaining consumer feedback, user research, and data analysis easier. Product managers may make data-driven choices, verify concepts, and tweak their products to provide better user experiences by incorporating consumer insights into product development. Product managers may also utilize these technologies to track user acceptance, assess feature usage, and gather feedback to iterate and continually improve their offers. Prospering in the Field of Product Management  These software solutions optimize the whole product management process by providing a centralized platform for task and project administration, supporting strategic road mapping, empowering customer-centric development, and delivering integration and scalability. Harnessing the potential of product management software has become critical for success as organizations struggle to remain ahead in a continually developing market. Conclusion The tools mentioned above and their powerful features clarify the relevance and importance of product management software to efficiently handle the entire product cycle. Knowledge and competency in these tools are beneficial for those who are already into product management or aim to build a career.  Product management is one of the fastest-growing careers as customer-centricity has become the driving factor in product and service-based industries. Freshers and working professionals who wish to leap into this career can check out the upGrad Post Graduate Certificate in Product Management of 6 months. This program is relevant to marketing professionals, sales professionals, project leads, project managers, software developers, technical managers, UI-UX design, analysts, consultants, entrepreneurs, and business development managers. If you’re eager to learn about Product Management and interested to become a great product manager, you need to demonstrate qualities of leadership, check out the Post Graduate Certificate in Product Management for it is you who’ll guide your team towards the path of success

by Hemant

21 Jun 2023

Blogs

5281

Thanks to the number of benefits of the cloud – scalability, security, and cost-effectiveness, to name a few – more and more businesses are resorting to working on some of the other cloud services. Needless to say, cloud services have successfully taken over the way we knew IT. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification According to a study by Binx, close to 98% of businesses will depend upon some cloud software and services in the next five years. But how to get a cloud? Or how to migrate your business to the cloud? The first step is to find a reliable cloud service provider, such as Amazon Web Services or AWS. Learn more: What Are The Skills to Become a Cloud Engineer ?? What is AWS? AWS is short for Amazon Web Services, which is a secure cloud services platform. It offers a range of services including content delivery, database storage, computes power, etc. All these combined add scalability and growth to a business. AWS allows individuals to: Run web and app servers on cloud and host websites. Store all your files and access them from anywhere, anytime. Send emails to your customers in bulk. All in all, it is one of the best platforms to rely on for your cloud needs. Now let’s understand how to migrate the business to AWS. But first…. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. What is AWS Migration? Moving data from an on-premise data center to a virtualized server – cloud – is called migration. AWS migration is when you move your data to the AWS cloud to make your business functions more smooth and scalable. Companies like GoDaddy and Expedia have already moved their business to the cloud, and many others are following suit. Read more: Amazon AWS VPC Tutorial How to do On-Premise AWS Migration? With the help of AWS, several top business giants like Coca-Cola Company, NewsCorp, Samsung, and GE have been able to migrate a significant amount of workload to the cloud. This helped them save costs and improve productivity. So, if you’re planning for AWS migration, you must follow the steps given below. Step 1: Conduct As-Is Analysis To get an overview of your IT ecosystem, you need to conduct an as-is analysis. This will help you identify the current processes of your business. Here’s how to do as-is analysis: Evaluate an application, considering its user base, access controls data type and state, etc. Find out the dependency on servers, storage devices, resource consumption, workloads, and networks. Evaluate the cost of operations and see whether your business will benefit in terms of cost-effectiveness from moving to the cloud. Step 2: Identify Migration Feasibility In the second step, it is important to see how dependent application and their services are on each other. Also, you would need to find out your migration objectives and workloads that you’re willing to conduct migration to AWS on. Identify the workloads and IT requirements that should be migrated to the cloud-first. Check whether it is financially feasible to migrate your business from an on-premise server to a cloud. Identify the characteristics of the applications and see whether you can map it on the AWS cloud. Step 3: Come up with a Migration Plan After the analysis is done, design a plan stating how to conduct migration to AWS. This will include shifting of all data and infrastructure from the on-premises server to the cloud. Step 4: Conduct an Audit In this step, you need to do the following things: Conduct an integration test, including DNS and IP remapping and checking identity and access controls. Log all your audit steps and its results to get insights into how the process of AWS migration is working for your business. To maintain security, get a firewall and check VPN connectivity, incident tracking, and management. Step 5: Backup and Restore Finally, to ensure that there is no data loss during migration to AWS, come up with a backup plan. This will help you get access to data that was skipped from migration and thus, minimize risks. Restore your backed up data to keep it safe from unforeseen events. AWS Migration Tools and Services There are some tools and services that can make migration to AWS quite easy. Here are a few of the most important ones. Cloud Endure With the help of this tool, migrating applications and databases that run on AWS supported versions of Linux and Windows OS will become automated. AWS Cloud Adoption Readiness Tool (CART) To begin a migration plan, you need a report on the critical areas of your organization. CART does just that. It helps you understand where your organization is lagging behind in terms of operations and necessary purposes. Dynatrace This is an excellent AI-based tool that helps you: Monitor cloud infrastructure Analyze app performance Identify and provide solutions to improve user experience AppDynamics This is a robust and intuitive app intelligence platform to help improve the performance monitoring of your business. It has the following features: App monitoring Automatic maps generation topology Instant scaling up of AWS instances Turbonomic Allocation of resources to avoid heavy bills cannot get any easier than with this tool. You also get a number of tools to analyze and optimize the way you use your resources. Why Migrate to AWS? Amazon Web Services was propelled in 2006.  While cloud computing was not a fresh-out-of-the-box idea around then, Amazon’s entrance into this part commoditized this innovation and made it progressively open to organizations.  By 2018, AWS had caught 40% of the cloud market, despite the fact that Microsoft Azure and Google have moved into this market too, claiming 17% and 8%.  AWS has kept its position of authority in cloud migration and cloud services.  While there positively are enormous endeavors utilizing AWS, the intriguing reality is that small and medium-sized organizations really make up most of the clients – about 90%, indeed. Knowing this, AWS has done numerous things to deal with its clients. Specifically, they made a lot of supportive instruments and assets for making and executing an AWS migration procedure easy. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Conclusion As you must have gathered by now, AWS migration is the need of the hour for most businesses today. And why not? It saves costs, reduces workload, and ensures data access from any part of the world! With this guide, you surely should find it easy to conduct migration to AWS. For more information, feel free to reach out to us directly! Or why not try our various courses on cloud computing? That should help! 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by Hemant

24 Mar 2020