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Modern data science technologies need modern approaches and skills. Perhaps a higher level of competency in the industry.
As a result, a day to day work is set to become challenging. Professionals in the data science industry will require to use the data science framework more often. Such frameworks will define the problem, helps gather relevant data, prepare the data for consumption, perform exploratory data analysis, model the data, validate the data model, and strategize the data for positive business outcomes.
Without further ado, we will head right in and talk about the most common machine learning frameworks used by data scientists daily.
Around 90 percent of the data scientists worldwide spend their time working on machine learning projects, source: Figure Eight report.
So, for them to drive businesses forward, they would require to extensively work closely with machine learning algorithms. However, this does not mean the individual need to always have in-depth experience in coding. Instead, they can use their expertise to solve bigger problems. Based on a report, it is said that most data science professionals have used at least one machine learning framework in their project.
Top 10 Machine Learning Frameworks a Data Scientist needs:
Pandas
Written in Python programming language, Pandas are a great framework for data analysis and data manipulation. An important aspect, Pandas tend to offer data structures and operation to manipulate time series and numerical tables. This framework works well with unstructured, unlabeled, or messy data giving them tools to reshape and slice datasets.
NumPy
Being an open-source library, NumPy offers versatility thus making it easier to deal with multi-dimensional arrays and matrices. It is also used as one of the standard libraries used in scientific computing for Python. NumPy provides tools to integrate Fortran code and C++.
Matplotlib
Matplotlib is a Python library ideal for data visualization. This framework eases the work of data scientists by helping them plot histograms or a 3D plot. Matplotlib offers a numerical extension to the NumPy library.
Scikit-learn
Another popular machine learning library popular among data science professionals. Scikit-learn generally gets constant updates to improve its efficiency. And since it is open-source, it also makes it a go-to framework for machine learning.
TensorFlow
The TensorFlow was developed at Google to undergo multiple numerical computations with the help of data flow graphs. The machine learning library has been extensively used by top brands like Nvidia, Uber, Gmail, and Airbnb.
This library is proven to be handy in aspects of experimenting and creating deep learning architectures.
Keras
Keras is a neural-network library, it is open-source and written in Python language. It has the advantage of running on top of the other open-source libraries like Theano and TensorFlow. Keras could be great if you have a large volume of data.
PyTorch
PyTorch was developed by Facebook’s artificial intelligence group and is used as a tool for deep learning. However, PyTorch is operational only with a dynamically updated graph. In simple terms, it offers the capability to change to the architecture even during the process.
Theano
Similar to NumPy, Theano also works with numerical computation. This Python library helps in defining, optimizing, and evaluating mathematical expressions which also includes multi-dimensional arrays.
Seaborn
Seaborn is used for visualization of the statistical models. This is also an open-source data visualization library written in Python language. Now, this visualization includes heat maps which are crucial in summarizing data.
Spark MLib
A machine learning library is used by nearly 6 percent of data science professionals. The best part about this library is, it supports Scala, Python, Java, and R. Spark MLib can also be used on Kubernetes, Hadoop, and Apache Mesos.
- Endnote
The demand for expertise in the data science field remains high. About 49 percent of 240 data scientists that were surveyed mentioned they were being approached at least once a week for a new job offer. The major reason is that companies are rapidly looking to hire data scientists with exceptional skills in data science. More precisely, someone having in-depth knowledge of applying machine learning and AI in their production.
With the machine learning community massively using Python, it is evident that machine learning frameworks will be in wider use.