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30 Days Python Roadmap for Data Analysts 👆

𝗔𝗱𝘃𝗮𝗻𝗰𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗧𝗼𝗽 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 ✅ Microsoft Power BI Data Analyst Professional Certificate ✅ Meta Data Analyst Professional Certificate ✅ IBM Data Analyst Capstone Project 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/49X5JPB 💡 𝗧𝗶𝗽 𝘁𝗼 𝗔𝗰𝗰𝗲𝘀𝘀 𝗧𝗵𝗲𝘀𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 (𝗖𝗵𝗲𝗰𝗸 𝗶𝗻 𝗪𝗲𝗯𝘀𝗶𝘁𝗲)📌

Machine Learning Algorithms every data scientist should know: 📌 Supervised Learning: 🔹 Regression ∟ Linear Regression ∟ Ridge & Lasso Regression ∟ Polynomial Regression 🔹 Classification ∟ Logistic Regression ∟ K-Nearest Neighbors (KNN) ∟ Decision Tree ∟ Random Forest ∟ Support Vector Machine (SVM) ∟ Naive Bayes ∟ Gradient Boosting (XGBoost, LightGBM, CatBoost) 📌 Unsupervised Learning: 🔹 Clustering ∟ K-Means ∟ Hierarchical Clustering ∟ DBSCAN 🔹 Dimensionality Reduction ∟ PCA (Principal Component Analysis) ∟ t-SNE ∟ LDA (Linear Discriminant Analysis) 📌 Reinforcement Learning (Basics): ∟ Q-Learning ∟ Deep Q Network (DQN) 📌 Ensemble Techniques: ∟ Bagging (Random Forest) ∟ Boosting (XGBoost, AdaBoost, Gradient Boosting) ∟ Stacking Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc. Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D React ❤️ for more free resources

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Here's the A–Z list of essential Python programming concepts A - Arguments B - Built-in Functions C - Comprehensions D - Dictionaries E - Exceptions F - Functions G - Generators H - Higher-Order Functions I - Iterators J - Join Method K - Keyword Arguments L - Lambda Functions M - Modules N - NoneType O - Object-Oriented Programming P - PEP8 Q - Queue R - Range Function S - Sets T - Tuples U - Unpacking V - Variables W - While Loop X - XOR Operation Y - Yield Keyword Z - Zip Function These concepts are foundational to mastering Python and writing clean, efficient, and Pythonic code. Credits: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

How to get job as python fresher? 1. Get Your Python Fundamentals Strong You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview. 2. Learn Python Frameworks As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers. 3. Build Some Relevant Projects You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies. @crackingthecodinginterview 4. Get Exposure to Trending Technologies Using Python. Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity. 5. Do an Internship & Grow Your Network. You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc. Python Interview Q&A: https://topmate.io/coding/898340 Like for more ❤️ ENJOY LEARNING 👍👍

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Essential Python Libraries for Data Science - Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions. - SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing. - Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames. - Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations. - Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning. - TensorFlow: An open-source machine learning framework widely used for building and training deep learning models. - Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling. - Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics. - Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing. - NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more. These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations. ENJOY LEARNING 👍👍

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