Which libraries required for being a Data Scientist

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I want to become a data Scientist and I need to your support Please tell me which libraries I should learn and which is the best Roadmap for this

Gaurav Kumar · Answer

Libraries required foBecoming a data scientist requires a solid foundation in programming, statistics, data manipulation, and machine learning, and there are several libraries in Python (most commonly) and R that are essential. Here's a detailed breakdown: 1. Python Libraries a. Data Manipulation Pandas – for handling structured data (DataFrames, CSVs, Excel). NumPy – for numerical operations, arrays, and linear algebra. SciPy – for advanced mathematical and statistical operations. b. Data Visualization Matplotlib – basic plotting and visualization. Seaborn – statistical data visualization (built on Matplotlib). Plotly / Bokeh – interactive plots and dashboards. c. Machine Learning / AI Scikit-learn – classical machine learning (regression, classification, clustering). TensorFlow / Keras – deep learning (neural networks). PyTorch – alternative deep learning framework, popular in research. XGBoost / LightGBM / CatBoost – gradient boosting algorithms for structured data. d. Natural Language Processing (NLP) NLTK – text processing and NLP. spaCy – fast NLP library for large-scale text. Transformers (Hugging Face) – for modern NLP models like BERT, GPT, etc. e. Data Handling & Web Scraping BeautifulSoup / Scrapy – web scraping. Requests – HTTP requests for APIs and web data f. Others Statsmodels – statistical modeling and hypothesis testing. OpenCV / PIL – image processing if working with computer vision. --- 2. R Libraries If using R, popular libraries include: dplyr – data manipulation. tidyr – data tidying. ggplot2 – visualization. caret / mlr3 – machine learning. randomForest / xgboost – advanced ML algorithms. shiny – interactive web apps and dashboards. 3. Optional but Useful Tools SQL / SQLAlchemy – for database queries. Spark (PySpark) – big data process

ALOK KUMAR · Answer

Start with Python, Jupyter or VS Code, NumPy, pandas, Matplotlib or Seaborn, and SQL. Add SciPy and statsmodels for stats, scikit-learn for classic ML, and XGBoost or LightGBM for stronger tabular models. For deep learning, pick one stack: PyTorch, TensorFlow, or Keras. For NLP, use spaCy and Hugging Face Transformers. For vision use OpenCV or scikit-image. To demo or deploy, use Streamlit or FastAPI. Keep Git and virtual environments in your routine. Practical Roadmap Build two small projects that clean, join, visualize, and explain insights from real data. Learn end-to-end modeling with scikit-learn : proper train/validation/test splits, metrics, and simple pipelines. Pick a track and ship one complete project: Tabular → feature engineering + model explainability, or NLP → spaCy / Hugging Face with a pre-trained model, or Vision → OpenCV / CNNs with a pre-trained model Package each project with a short README and, if possible, a tiny app (Streamlit/FastAPI). Repeat with a new dataset to deepen skills and show progression. Structured options (alongside self-study) Great Learning: Microsoft Accelerator Program in Data Science & AI (3-month, weekend format; Python, SQL, ML, deep learning, applied GenAI; case studies; co-branded certificate). Udacity: Data Scientist / Machine Learning Nanodegree (project-based, mentor feedback, code reviews). General Assembly: Data Science Short Course (part-time, portfolio-oriented fundamentals). Pick one structured path to impose deadlines and feedback , but let your portfolio projects do the heavy lifting in interviews.

Which libraries required for being a Data Scientist

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