{"id":1098,"date":"2024-01-11T03:01:00","date_gmt":"2024-01-11T03:01:00","guid":{"rendered":"https:\/\/blog.ngocha.biz\/?p=1098"},"modified":"2024-01-11T03:01:00","modified_gmt":"2024-01-11T03:01:00","slug":"list-best-frameworks-data-scientists","status":"publish","type":"post","link":"https:\/\/blog.ngocha.biz\/?p=1098","title":{"rendered":"List of 11 Best  Frameworks used by Data Scientists"},"content":{"rendered":"<p>The practice of data science requires the use of <a href=\"https:\/\/en.wikipedia.org\/wiki\/Machine_learning?ref=devopscube.com\" rel=\"noreferrer noopener\">machine learning <\/a>frameworks extensively. Now, this could be for many reasons but largely to automate the processes that drive their business forward.<\/p>\n<p>Data science frameworks enable data scientists to organize, process, model, and interpret data with greater efficiency.<\/p>\n<p>Framework-focused solutions mean data scientists don\u2019t always need to have extensive experience in <a href=\"https:\/\/devopscube.com\/top-websites-to-learn-programming-online\/\" rel=\"noreferrer noopener\">coding and programming languages<\/a>, and can instead use their expertise in solving bigger problems on their table. Reports show that 85% of data pros have used at least one ML framework.<\/p>\n<h2 id=\"top-frameworks-used-by-data-scientists\">Top Frameworks Used by Data Scientists<\/h2>\n<p>If you are on your path to becoming a data savy, here&#8217;s a list of the 10 best open source ML frameworks available in the market that are reportedly the most used by data science professionals.<\/p>\n<blockquote><p><strong>Note<\/strong>: The choice of the right tool often depends on the specific needs of the project.<\/p><\/blockquote>\n<h3 id=\"1-tensorflow\">1. TensorFlow<\/h3>\n<p><a href=\"https:\/\/www.tensorflow.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Tensorflow<\/a> is an open-source machine learning library developed at Google for numerical computation using data flow graphs is arguably one of the best, with Gmail, Uber, Airbnb, Nvidia, and lots of other prominent brands using it. It\u2019s handy for creating and experimenting with deep learning architectures, and its formulation is convenient for data integration such as inputting graphs, SQL tables, and images together.<\/p>\n<h3 id=\"2-scikit-learn\">2. Scikit-learn<\/h3>\n<p><a href=\"https:\/\/scikit-learn.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Scikit-learn<\/a> is a very popular open-source machine-learning library for the Python programming language. With constant updations in the product for efficiency improvements coupled with the fact that its open-source makes it a go-to framework for machine learning in the industry.<\/p>\n<h3 id=\"3-keras\">3. Keras<\/h3>\n<p><a href=\"https:\/\/keras.io\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Keras<\/a> is an open-source neural network library written in Python. It is capable of running on top of other popular lower-level libraries such as Tensorflow, Theano &amp; CNTK. This one might be your new best friend if you have a lot of data and\/or you\u2019re after the state-of-the-art in AI: deep learning.<\/p>\n<h3 id=\"4-pandas\">4. Pandas<\/h3>\n<p><a href=\"https:\/\/pandas.pydata.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Pandas<\/a> is yet another open-source software library written for the Python programming language for <a href=\"https:\/\/devopscube.com\/python-web-scrapping\/\" rel=\"noreferrer noopener\">data manipulation and analysis<\/a>. In particular, it offers data structures and operations for manipulating numerical tables and time series. Pandas work well with incomplete, messy, and unlabeled data and provide tools for shaping, merging, reshaping, and slicing datasets.<\/p>\n<h3 id=\"5-spark-mlib\">5. Spark MLib<\/h3>\n<p>Spark MLib is a popular machine-learning library. As per the survey, almost 6% of the data scientists use this library. This library has support for Java, Scala, Python, and R. Also you can use this library on Hadoop, Apache Mesos, Kubernetes, and other cloud services against multiple data sources.<\/p>\n<h3 id=\"6-pytorch\">6. PyTorch<\/h3>\n<p><a href=\"https:\/\/pytorch.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">PyTorch<\/a> is developed by Facebook&#8217;s artificial intelligence research group and it is the primary software tool for deep learning after Tensorflow. Unlike TensorFlow, the PyTorch library operates with a dynamically updated graph. This means that it allows you to make changes to the architecture in the process.<\/p>\n<h3 id=\"7-matplotlib\">7. Matplotlib<\/h3>\n<p><a href=\"https:\/\/matplotlib.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Matplotlib<\/a> is a plotting library for Python, a library mostly used for data visualization by plotting histograms, scatterplot, 3D plot, etc., and also serves as a numerical extension to the Numpy library. It\u2019s the de facto visualization library used in every data science test case in Python as it makes visualizations easy and interactive giving you the power to produce histograms, scatterplot, 3D plot, image plot, bar charts, power spectra, and many more.<\/p>\n<h3 id=\"8-numpy\">8. Numpy<\/h3>\n<p><a href=\"https:\/\/numpy.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Numpy<\/a> is an open-source library that gives programmers the versatility to work with matrices and multi-dimensional arrays. It&#8217;s the standard library for scientific computing in Python and provides powerful tools for integrating C\/C++ and Fortran code. Check out the <a href=\"https:\/\/devopscube.com\/python-numpy-tutorial\/\" rel=\"noreferrer noopener\">NumPy tutorial <\/a>and <a href=\"https:\/\/devopscube.com\/numpy-practical-examples\/\" rel=\"noreferrer noopener\">NumPy practical examples<\/a>.<\/p>\n<h3 id=\"9-seaborn\">9. Seaborn<\/h3>\n<p><a href=\"https:\/\/seaborn.pydata.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Seaborn<\/a> is an open-source Python data visualization library based on matplotlib. The main focus of this package is on the visualization of statistical models. visualizations that include heat maps, those which summarize the data but still depict the overall distributions.<\/p>\n<h3 id=\"10-theano\">10. Theano<\/h3>\n<p><a href=\"http:\/\/deeplearning.net\/software\/theano\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Theano<\/a> Python library is for numerical computation and is similar to NumPy. Some libraries such as Pylearn2 use Theano as their base component for mathematical computation. Theano helps you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently<\/p>\n<h3 id=\"11-spacy\">11. Spacy<\/h3>\n<p>Spacy is an advanced Natural Language Processing (NLP) in Python. It is primarily used for research and industrial applications. It is very fast and efficient.<\/p>\n<p>An advantage of Spacy is that, it comes with pre-trained models that can be used for various NLP tasks. Also, it has an easy to use API with lot of customizations and extensibility.<\/p>\n<p>Here are some other frameworks and tools worth considering.<\/p>\n<ol>\n<li><a href=\"https:\/\/cran.r-project.org\/web\/packages\/randomForest\/?ref=devopscube.com\" rel=\"noreferrer noopener\">RandomForest<\/a><\/li>\n<li><a href=\"https:\/\/xgboost.readthedocs.io\/en\/latest\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Xgboost<\/a><\/li>\n<li><a href=\"https:\/\/lightgbm.readthedocs.io\/en\/latest\/?ref=devopscube.com\" rel=\"noreferrer noopener\">LightGBM<\/a><\/li>\n<li><a href=\"https:\/\/spark.apache.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Apache Spark<\/a><\/li>\n<li><a href=\"https:\/\/www.fast.ai\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Fast.ai<\/a><\/li>\n<li><a href=\"https:\/\/onnx.ai\/?ref=devopscube.com\" rel=\"noreferrer noopener\">ONNX<\/a><\/li>\n<li><a href=\"https:\/\/jupyter.org\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Jupyter Notebook<\/a><\/li>\n<li><a href=\"https:\/\/aws.amazon.com\/sagemaker\/data-scientist\/?ref=devopscube.com\" rel=\"noreferrer noopener\">Amazon SageMaker for Data Scientists<\/a><\/li>\n<li><a href=\"https:\/\/cloud.google.com\/datalab\/docs?ref=devopscube.com\" rel=\"noreferrer noopener\">Google Cloud Datalab<\/a><\/li>\n<\/ol>\n<h2 id=\"conclusion\">Conclusion<\/h2>\n<p>In this blog, I have covered the top frameworks used by Data Scientists. With the advent of cloud-based AI\/ML tools, Data scientists can increase their productivity using prebuilt tooling.<\/p>\n<p>If you are someone who is getting started with Data Science, you can try <a href=\"https:\/\/devopscube.com\/datacamp-review\/\">Datacamp<\/a> free courses which are focused on data science. Also, check out the <a href=\"https:\/\/devopscube.com\/datacamp-discount\/\" rel=\"noreferrer noopener\">Datacamp discounts<\/a> if you want to consider advanced learning from them. You can get free access if you are an educator.<\/p>\n<hr>\n<p><strong>Ngu\u1ed3n:<\/strong> <a href=\"https:\/\/devopscube.com\/list-best-frameworks-data-scientists\/\" target=\"_blank\" rel=\"noopener noreferrer\">List of 11 Best  Frameworks used by Data Scientists \u2014 DevOpsCube<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Source: https:\/\/devopscube.com\/list-best-frameworks-data-scientists\/<\/p>\n","protected":false},"author":1,"featured_media":1099,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1098","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-devops"],"_links":{"self":[{"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=\/wp\/v2\/posts\/1098","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1098"}],"version-history":[{"count":0,"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=\/wp\/v2\/posts\/1098\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=\/wp\/v2\/media\/1099"}],"wp:attachment":[{"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1098"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1098"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.ngocha.biz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1098"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}