Data Science has begun to witness a bright-ever future since companies worldwide have been becoming highly refined in their approach towards polishing their niche. The companies have been looking forward to improving the sales, winning more clients, scale-up customer satisfaction, and earning more effortlessly.
The role of Data scientists has been significant for most organizations. According to the Glassdoor reports, the Data Scientist job position has witnessed about 5X job growth with an average salary of USD 120,931.
With expert data science professionals focusing on data science frameworks, many businesses have been able to successfully expedite data science projects.
What is Data Science Framework and Why to Use One?
In software terminology, a framework means a set of individual software components which are available in the form of code and ready to be run either independently or collectively to implement a sophisticated task on any device. The major part is ready to run with libraries and you need not make efforts to reinvent them, all you have to learn is to customize them to build your application-specific software according to your business needs.
The data science programming framework is software readily developed which incorporates reusable features so that you can develop your projects effortlessly and quickly. Hence, it is logical and practical to use a data science framework. With the frameworks developed by giants like Google and Facebook, data scientists have been leveraging the benefits of easy-to-use interfaces.
While programming languages evolve, they will never be completely obsolete, hence data scientists and programmers prefer working with the data science frameworks and consider them as advanced and cutting edge technology with numerous benefits.
Features of Data Science Framework
Data science frameworks offer numerous benefits to the data science professionals and the technology teams with the features listed below.
1. Creation of high-end codes
Data science Frameworks can let the coders create ideal design patterns and prevent any possible insecure codes. The codes developed would be rather easier to write, test, and debug.
2. Utilization of pre-tested and pre-optimized Frameworks
Data science professionals can save time by utilizing the pre-tested and pre-optimized code instead of building code from scratch.
3. Rapid Implementation
When data science teams use the code highly documented and tested, the implementation duration will be rather less, giving them more time on the other tasks like model analysis and optimization.
5 Best Data Science Frameworks
Here are five popular data science framework picks that will help boost your business.
1. TensorFlow
An open-source Machine Learning (ML) platform, being developed at Google, Tensorflow is used for numerical computation with the data flow graphs. Many prominent brands like Nvidia, Uber, Intel, Twitter, PayPal, Airbnb, Snapchat, and Gmail use TensorFlow with intense experimentation on deep learning architecture, data integration, and formulation useful for inputting the SQL tables, graphs, images, etc.
Being a versatile platform useful to build ML and Deep learning modules relevant for the implementation of Mobile, Web, Desktop, and cloud solutions, Tensorflow is considered the most popular data science framework used by successful businesses. For instance, Airbnb data scientists use Tensorflow to develop deep learning models that can categorize the listing images, since they are the core elements used to pick up the accurate place for a vacation stay. This option has helped companies to create the perfect solutions that can classify the type of room, thereby improving User Experience (UX).
2. Scikit-learn
Created and developed by David Cournapeau, Scikit Learn has evolved from a mere framework in 2007 into a Python library as of now, that can be utilized to create amazing data science projects. The framework includes numerous Machine Learning tools beneficial for data analysis and data mining. This can be used in many operations like stock pricing analysis, spam email identification, customer data analysis, etc.
Many popular companies like Evernote, Spotify, MARS, J.P. Morgan, and Booking.com use the models based on Scikit-learn libraries
For instance, Spotify uses these frameworks models to generate the song recommendations, while Booking.com uses ML-based algorithms for various benefits like destination and hotel recommendations, fraud detection, and service agent reservations.
3. Keras
Kagle, the data scientist community by Google, had listed Keras as the No. 1 deep learning tool. This open-source neural network library is written with the Python codebase and capable of operating over the other low-level libraries namely CNTK, Tensorflow, and Theano. If you have huge amounts of data and you are chasing the best AI/deep learning possibilities, then Keras would be your best friend.
Keras is one of the excellent frameworks useful for your projects by making it easier to try various data science ideas, like building neural networks with no hitches. Famous brands like Uber, Netflix, Freeosk, Yelp, Wells Fargo, and NASCENT Technology use the endless possibilities of Keras.
4. Pandas
Yet another open-source library, written mainly for the data analysis and data manipulation in Python programming language, Pandas offers various data structures and operations ensuring the expedition of numerical tables or time series manipulations. This is a perfect platform that deals well with the unlabeled, messy, and incomplete data and offers the tools for merging, shaping, reshaping, and slicing the various datasets.
Pandas, the Python-based framework was originally formulated by AQR Capital Management, a financial company. However, now this open-source platform has been accurate for data preparation, data wrangling, and management of unstructured or unlabeled data. Giant companies like Tesla Motors, Target, Delivery Hero, and NVIDIA, use Pandas for their coding efficiency.
5. PyTorch
Being developed by the Artificial Intelligence research group of Facebook, PyTorch is the primary software tool you can rely on after Tensorflow. There is a huge rivalry between Tensorflow and PyTorch since both are highly used and tagged as the favorite among data scientists or programmers. Unlike TensorFlow, Pytorch operates with a graph that is dynamically updated. This lets you make changes to the architecture which is under process.
In addition to the dynamic computational graphs, other aspects like ease of use, API simplicity, and efficiency are also added to the success of PyTorch. With this framework, you can train the models to find solutions to several tasks like object detection, research, and production operations. Major companies like Salesforce, Stanford University, Udacity, and Microsoft, use PyTorch in their data science applications.
Conclusion
The above-listed top five data science frameworks can let you build amazing data science projects as data science professionals while saving your time. TensorFlow, Scikit-learn, Pandas, Keras, and PyTorch form only some of the core frameworks with which you can develop extraordinary solutions to fulfill your business challenges easily. Most businesses, regardless of being big or small, use these frameworks to ease the job and maximize the results in the least time.
Hence, it is quite practical and logical to use a data science framework that suits your business needs, to simplify the programming aspects to a great extent. These frameworks also get updated on a regular basis which a data science professional must know. The best way to know and keep yourself updated is through certifications or training programs. Some of the best data science certification programs are from MIT, USDSI, Stanford, Georgia Tech, etc. With the certifications your knowledge can be relevant with the industry.
