Top Technologies You Must Focus On to Build Data Science Expertise

Data Science is the hottest technology of modern times. Owing to the rising demand in the market and great payscale, budding tech professionals are getting more and more inclined towards becoming data scientists.

While the demand for data science skills keeps rising, the nature of that demand has remained roughly constant, according to a Jeff Hale analysis. Given how fast technologies in the data science space seem to rise and fall, even over the course of a year we might expect to see more variance in technology preferences. Instead we find a (somewhat) remarkable stasis, one that continues to remind us: It's never a bad time to learn Python.

According to Hale, rather than 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)

Python is an interpreted, object-oriented, high-level programming 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, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms and can be freely distributed.

Pandas (for data manipulation)

Pandas is a newer package built on top of NumPy and provides an efficient implementation of a DataFrame. DataFrames are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data. As well as offering a convenient storage interface for labeled data, Pandas implements a number 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 appropriate tools to compile the C and Cython sources on which Pandas is built.

Scikit-learn library (for learning ML)

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python. It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use. The library is built upon the SciPy (Scientific Python) that must be installed before you can use sci-kit-learn. Extensions or modules for SciPy care conventionally named SciKits. As such, the module provides learning algorithms and is named sci-kit-learn. The vision for the library is a level of robustness and support required for use in production systems. This means a deep focus on concerns such as ease of use, code quality, collaboration, documentation, and performance.

SQL (for querying)

Structured Query Language is a standard Database language that is used to create, maintain, and retrieve the relational database. The following are some interesting facts about SQL. It is case insensitive. But it is 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 can write comments in SQL using "–" (double hyphen) at the beginning of any line. SQL is the programming 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 do not use SQL. Although there is an ISO standard for SQL, most of the implementations slightly vary in syntax. So we may encounter queries that work in SQL Server but do not work in MySQL.

Tableau (for data visualization)

Tableau is a compelling visualization software which centers on business intelligence and data interpretation, which is employed and used by industries all around the world. Tableau allows users to create stunning visualizations instantly with a simple drag-and-drop design. You can make use of the community discussion and many tutorials online to extract features of the benefits of Tableau. However, there is always a scope of a few 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 can choose the right 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 need more flexibility, eager execution allows for immediate iteration and intuitive debugging. For large 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 based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab (FAIR). It is free and open-source software released under the Modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface.

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