4. PyTorch PyTorch is an open-source machine-learning library developed by Facebook. It is a popular choice for deep learning and provides support for dynamic computation graphs, which allow for more flexible and efficient model design. https://
pytorch.org
@saboo_shubham_
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PyTorch: Facebook’s Open-Source Deep Learning Framework
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scikit-learn: comprehensive machine learning library for Python
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3. scikit-learn scikit-learn is a library for machine learning in Python. It provides a wide range of algorithms for classification, regression, clustering, and model selection, as well as tools for evaluating the performance of these models. https://
scikit-learn.org/stable/ -
Pandas: Essential Data Manipulation Library for Analysis
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2. Pandas Pandas is a library for data manipulation and analysis. It provides functions and data structures for efficiently working with large datasets, including support for handling missing data, time series analysis, and merging and joining data. https://
pandas.pydata.org -
NumPy: Essential Python Library for Scientific Computing
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1. NumPy NumPy is a fundamental library for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices of numerical data, as well as functions to perform operations on these data structures. https://
numpy.org -
Seven Python Libraries Every Machine Learning Engineer Should Know
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7 Python libraries every machine learning engineer should know. (A thread)
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Essential Collaboration Skills for Data Analysts
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5. Collaboration skills: Data analysts often work on cross-functional teams and must be able to collaborate effectively with others in order to achieve their goals.
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Essential Communication Skills for Data Analysts
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4. Communication skills: A data analyst should be able to communicate findings and recommendations clearly and effectively to both technical and non-technical audiences. Learn communication skills from: Harappa Education Mind Tools LinkedIn Learning
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Essential Technical Skills for Data Analysts
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3. Technical skills: A data analyst should be proficient in a range of data analysis tools and technologies. Get really good at: Microsoft Excel SQL, Power BI Python/R programming
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Essential Data Analysis Skills and Learning Platforms
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1. Strong analytical skills: The ability to analyze data and draw meaningful insights from it is critical for a data analyst. Learn these skills from: DataCamp Coursera Khan Academy
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Data Analyst Skills: Attention to Detail with Real Datasets
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2. Attention to detail: A data analyst needs to be able to work with large amounts of data and must be able to identify patterns, trends, and anomalies in the data. This requires close attention to detail. Look at real-world datasets from: Kaggle Dataquest