Tools and Technologies

Data science is an emerging field with support in various languages, such as Python, R, and Java. There are different frameworks and cloud resources for these languages. In this lesson, we’ll discuss some of the well-known examples used in data science.

Languages

The two most prominent languages used for data science projects are Python and R. They both contain various libraries, which come in handy for data science projects.

Python

Python is a programming language that’s easy to understand. It works well for quickly creating applications, writing scripts, or linking different parts of a system together. Python has its own methods of organizing data and is flexible when it comes to handling various data types and connections within a program.

Many of its libraries are ideal for data science tasks, including, but not limited to, the following:

• The TensorFlow, PyTorch, and Keras libraries: These are widely used for building and training deep learning models.

• The seaborn library: This is Python’s data visualization library that simplifies the creation of statistical graphs.

• The pandas library: This versatile Python library provides powerful tools for data manipulation and analysis

• The scikit-learn library: This library offers a wide range of machine-learning tools and techniques for tasks such as classification, regression, clustering, and more.

• The SciPy library: This library includes a wide range of statistical functions for data analysis. It also offers various tools and functions for tasks such as optimization, integration, and linear algebra.

• The PyCharm IDE: This integrated development environment (IDE) provides a comprehensive platform for Python programming and development tasks.

R language

The R programming language offers a free software platform for statistical computing and creating graphs. With R, we can accomplish various tasks, like linear regression, time series analysis, and statistical inference. It’s a suitable choice for developing the most up-to-date statistical and graphical programs.

Many of its packages are used for data science tasks, including, but not limited to, the following:

• The dplyr package: This library simplifies and enhances data manipulation and transformation. It’s a valuable tool for data analysis and preprocessing tasks.

• The tidyr package: As the name suggests, this library is used for tidying the data by restructuring and cleaning the dataset.

• The ggplot2 package: This library is used for visualization in R.

• The stringr package: This library is used for manipulating text.

• The git2r package: This library provides R language access to GitHub repositories.

• The ggmap package: This library enables integration with maps, such as Google Maps.

• The Stats package: This library provides a wide array of functions and methods for conducting statistical analyses and hypothesis testing.

• The RStudio IDE: This library is an integrated development environment tailored for the R programming language.

SQL

Structured Query Language (SQL) is a common language for managing data in databases. In a system called relational database management system (RDBMS), we can store and change data using small code bits called queries. The data is organized in the RDBMS with connections between different parts, and the database schema tells us how elements are connected and structured.

There are many RDBMSs in which SQL is used for data science tasks, including, but not limited to, the following:

• Microsoft SQL Server

• SQLite

• MySQL

• PostgreSQL

• Oracle

In addition to these, there are other services and platforms available for data science tasks. Nevertheless, these are typically the most highly sought-after resources in the field of data science.

Services and platforms

It’s usually expected that data scientists are able to utilize several cloud services, along with open-source and interactive platforms. Let’s look at some of these.

Jupyter Notebook

Jupyter Notebook is an interactive computing platform that allows users to create and share documents containing live code, equations, visualizations, narrative text, etc. It’s a versatile tool commonly used for data analysis, scientific research, machine learning, and collaborative coding.

Google Colab

Google Colababoratory, often referred to as Colab, is a cloud-based Jupyter Notebook environment provided by Google Research. It offers a free and accessible platform, eliminating the need for expensive computational infrastructure. With the ability to write and execute Python code directly in a web browser, Colab offers easy access to writing codes.

Kaggle Notebook and Kaggle datasets

Kaggle is a collaborative platform for data scientists that allows them to access diverse datasets, write code for advanced programming, and engage in several programming competitions.

The datasets on Kaggle include a variety of data types, such as text, video, and even audio, which makes them a popular resource for various tasks. Kaggle notebooks are similar to Google Colab, allowing users to code online without using their own computing power.

Amazon AWS and Microsoft Azure

Microsoft Azure and Amazon Web Services (AWS) both offer remote access to strong computational resources through the internet. Individuals and businesses use their resources for various tasks, such as storing data and running websites and apps, alongside doing other computing tasks, without the need to use high computational resources of their own.

Data visualization tools

Effective data visualization is a crucial component of the data science pipeline. Data scientists should be able to use a diverse array of software and applications available to show data in a simple manner.

• Tableau: This is a data visualization and business intelligence tool used by data scientists to connect to various data sources, perform data transformation and analysis, and create interactive dashboards for data-driven decision-making.

• Power BI: Developed by Microsoft, Power BI is another data visualization and business intelligence tool frequently used in data science for importing and analyzing data and building interactive reports and dashboards. It’s known for its seamless integration within Microsoft’s ecosystem and offers various data connectors and modeling capabilities.

• Microsoft Excel: This widely used spreadsheet application is commonly employed in data science for fundamental data analysis and manipulation tasks, making it a versatile choice in the early stages of data science projects.