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Top technologies to become good in data science

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data science

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Trending technologies to learn data science

Data Science is the hottest technology of recent times. due to the rising demand within the market and great payscale, budding tech professionals are becoming more and more inclined towards becoming data scientists.

data science
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While the demand for data science skills keeps rising, the character of that demand has remained roughly constant, consistent with a Jeff Hale analysis. Given how briskly technologies within the data science space seem to rise and fall, even over a year, we’d expect to ascertain more variance in technology preferences. Instead, we discover a (somewhat) remarkable stasis, one that continues to remind us: It’s never a nasty time to find out Python.

According to Hale, instead of trying to master the list of technologies above, it’s best to “focus on learning one technology at a time.” Which order does he recommend?

Python (for general programming)

programming language skills
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Python is an interpreted, object-oriented, high-level programing language with dynamic semantics. It’s high-level built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, also as to be used as a scripting or glue language to attach existing components together. Python’s simple, easy to find out syntax emphasizes readability and thus reduces the value of program maintenance. Python supports modules and packages, which inspires program modularity and code reuse. The Python interpreter and therefore the extensive standard library are available in source or binary form for free of charge for all major platforms and may be freely distributed.

Pandas (for data manipulation)

Pandas may be a newer package built on top of NumPy and provide an efficient implementation of a DataFrame. DataFrames are essentially multidimensional arrays with attached row and column labels, and sometimes with heterogeneous types and/or missing data. also as offering a convenient storage interface for labeled data, Pandas implements a variety of powerful data operations familiar to users of both database frameworks and spreadsheet programs. Installation of Pandas on your system requires NumPy to be installed, and if building the library from source, it requires the acceptable tools to compile the C and Cython sources on which Pandas is made.

Scikit-learn library (for learning ML)

Scikit-learn provides a variety of supervised and unsupervised learning algorithms via a uniform interface in Python. it’s licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use. The library is made upon the SciPy (Scientific Python) that has got to be installed before you’ll use sci-kit-learn. Extensions or modules for SciPy care conventionally named SciKits. As such, the module provides learning algorithms and is known as sci-kit-learn. The vision for the library may be a level of robustness and support required to be used in production systems. this suggests a deep specialize in concerns like simple use, code quality, collaboration, documentation, and performance.

SQL (for querying)

Structured command language may be a standard Database language that’s wont to create, maintain, and retrieve the electronic database. the subsequent are some interesting facts about SQL. it’s case insensitive. But it’s a recommended practice to use keywords (like SELECT, UPDATE, CREATE, etc) in capital letters and use user-defined things (liked table name, column name, etc) in small letters. we will write comments in SQL using “–” (double hyphen) at the start of any line. SQL is that the programing language for relational databases (explained below) like MySQL, Oracle, Sybase, SQL Server, Postgre, etc. Other non-relational databases (also called NoSQL) databases like MongoDB, DynamoDB, etc don’t use SQL. Although there’s an ISO standard for SQL, most of the implementations slightly vary in syntax. So we may encounter queries that employment in SQL Server but don’t add MySQL.

Tableau (for data visualization)

Tableau may be a compelling visualization software that centers on business intelligence and data interpretation, which is used and employed by industries all around the world. Tableau allows users to make stunning visualizations instantly with an easy drag-and-drop design. you’ll make use of the community discussion and lots of tutorials online to extract features of the advantages of Tableau. However, there’s always a scope of a couple of basic errors that occur with its operations.

TensorFlow (most popular) or PyTorch (growing fastest) (for deep learning)

TensorFlow offers multiple levels of abstraction so you’ll choose the proper one for your needs. Build and train models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.

If you would like more flexibility, eager execution allows for immediate iteration and intuitive debugging. for giant ML training tasks, use the Distribution Strategy API for distributed training on different hardware configurations without changing the model definition.

Moreover, as described by Wikipedia, PyTorch is an open-source machine learning library supported the Torch library, used for applications like computer vision and tongue processing, primarily developed by Facebook’s AI lab (FAIR). it’s free and open-source software released under the Modified BSD license. Although the Python interface is more polished and therefore the primary focus of development, PyTorch also features a C++ interface.

Several pieces of Deep Learning software are built on top of PyTorch, including Uber’s Pyro, HuggingFace’s Transformers, and Catalyst.

 

Souce: analyticsinsight

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