Python Interviews

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COMMON TERMINOLOGIES IN PYTHON - PART 1 Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them? In this series, we would be looking at the common Terminologies in python. It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few: IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts. Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately System Python - This is the version of python that comes with your operating system Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed) Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed. Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g. >>> print("Hello World") Hello World Where Hello World is your return value. Note: A return value can be any of these variable types: handle, integer, object, or string Script - This is a file where you store your python code in a text file and execute all of the code with a single command Script files - this is a file containing a group of python scripts

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If you are a data analyst and thinking of getting started with freelancing on upwork then here's something you should know. You should be ready to invest money if you want to get started with freelancing on upwork. So there's something called connects on Upwork. For simplicity you can consider connects as the currency of upwork which one will spend while submitting a proposal for the freelancing tasks listed on the platform. Previously upwork used to give some free connects to every new account but these days they don't. So you have to buy the connects at the rate of 100 connects per $15 + Taxes (without upgrading to upwork plus) which will be 1.3k + taxes in INR. Let's say you submit proposal for those jobs asking for 20 connects, the max you will be able to submit is 5 jobs and you will get the job or not again depend on many factors. You may end up having no jobs even after spending 100 connects and then again you have to repeat the cycle. Everything looks shiny from outside but reality can be different. Every platform requires investment either in the form of time, dedication, money or combination of all.

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Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started: 1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python. 2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn. 3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio. 4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science. 5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have. 6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus. 7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills. Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!

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Python Top 40 Important Interview Questions and Answers ✅

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10 great Python packages for Data Science not known to many: 1️⃣ CleanLab Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. 2️⃣ LazyPredict A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code. 3️⃣ Lux A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data. 4️⃣ PyForest A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code. 5️⃣ PivotTableJS PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥 6️⃣ Drawdata Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook. 7️⃣ black The Uncompromising Code Formatter 8️⃣ PyCaret An open-source, low-code machine learning library in Python that automates the machine learning workflow. 9️⃣ PyTorch-Lightning by LightningAI Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation. 🔟 Streamlit A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

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