Data science has been called the sexiest profession of the 21st Century—but you could be forgiven for thinking that the job description sounds anything but. As an interdisciplinary field, data science incorporates scientific methods, algorithms, systems, and processes in the study and management of data. Working in the field involves handling processes like data engineering, data visualization, advanced computing, and machine learning. Feeling hot under the collar yet?
Fortunately, there are a range of powerful tools that make all of the above achievable for data scientists. A big part of becoming a data scientist is understanding how to utilize these tools meaningfully in your role.
This article takes a look at some of the popular tools used in data science and what they can do. We’ll finish up by taking a look at some of the popular data science job descriptions where you’re likely to use these tools in your day to day role.
Tools Used by Data Scientists
Data scientists have a range of tools at their disposal that make all of the above achievable. Some of the more popular tools used in data science include:
SQL: SQL (Structured Query Language) is considered the holy grail of data science. You won’t get very far in this field without knowledge of this important tool. SQL is a domain-specific programming language used for managing data. It’s designed to enable access, management, and retrieval of specific information from databases. As most companies store their data in databases, proficiency in SQL is essential in the field of data science. There are several types of databases, like MySQL, PostgreSQL, and Microsoft SQL Server. Since most of them recognize SQL, it’s easy to work on any of them if you have a thorough knowledge of SQL. Even if you’re working with another language, like Python, you’ll still need to know SQL to access and manage the database to work with the information.
Apache Spark: Spark is a powerful analytics engine. It’s one of the most popular and most-used data science tools. It was specially created to perform stream processing and batch processing of data. Stream processing means processing the data as soon as it’s produced, while batch processing is the running of jobs in batches, as opposed to individually. One of the best features of Apache Spark is its Machine Learning APIs, which enable data scientists to make essential predictions through raw data. It also has the ability to handle streaming data, which means it can process real-time data. This powerful feature makes it an invaluable tool, especially when compared to other tools that can only process and manage historical data in batches.
MATLAB: MATLAB is a multi-paradigm numerical computing environment. It processes mathematical information and is used widely in scientific fields and disciplines, with data science being one of them. MATLAB is a closed-source software that enables statistical modeling of data, matrix functions, and the implementation of algorithms. It’s also used in image and signal processing and boasts a graphics library for creating powerful visualizations. In the field of data science, this tool is used for simulating neural networks and fuzzy logic. Neural networks are computing systems that emulate biological neural networks. So MATLAB is a powerful tool for AI and deep learning.
BigML: BigML is a leading machine learning platform and one of the most widely used data science tools. It features a completely intractable graphics user interface (GUI) environment that is cloud-based. BigML uses cloud computing to deliver standardized software across various different industries. Organizations can use it to employ machine learning algorithms across the board. Predictive modeling is BigML’s specialty and it uses a wide range of machine learning algorithms, such as time-series forecasting, classification, and clustering. It also comes with numerous methods of automation that can help you automate processes such as hyperparameter models. Not only that, but you can also automate the workflow of reusable scripts using this BigML.
SAS: SAS is a statistical software tool. It’s a closed-source proprietary software developed by SAS Institute for data management, advanced analytics, predictive analysis, business intelligence, multivariate analysis, and criminal investigation. In the data science field, SAS is used by large organizations for data analysis. SAS has a range of statistical libraries and tools you can use to model and organize your data. Being on the expensive end, SAS is typically only purchased and used by large industries.
Excel: Most people have heard of Excel as it’s a widely used tool across all business sectors. Its use in the data science field has increased recently because it offers easy access for data processing, data visualization, and complex calculations. Features like formulae, tables, filters, and slicers make it a very useful tool for data scientists managing intricate data. One of its advantages is that users can customize functions and formulae according to their task requirements. While Excel is not suitable for large data sets, you can manipulate and analyze data quite effectively when paired with SQL.
Tableau: Tableau is a data visualization tool full of graphics to enable you to create interactive visuals of your data. Tableau’s target market includes the industries working in the area of business intelligence. Some of its most important features are the ability to interface with databases, Online Analytical Processing (OLAP) cubes, and spreadsheets. Tableau is also distinguished by its feature of visualizing geographical data. With this tool, you can plot longitudes and latitudes on a map. Other than creating intuitive visualizations, you can also utilize Tableau’s analytics tool for data analysis.
Scikit-Learn: This is a Python-based library you can use to implement algorithms for machine learning. It’s a convenient tool for data science and data analysis as it’s simple and easy to implement. Scikit-Learn is most useful in situations where you have to perform rapid prototyping. It’s also very suitable for research that requires basic machine learning. Scikit makes use of numerous Python libraries, such as Matplotlib, SciPy, and Numpy.
Apache Hadoop: This is an open-source framework that allows you to manage and store huge amounts of data. It offers distributed computing of massive data sets. This is done by dividing the data sets over a cluster of thousands of computers. Data scientists use Hadoop for high-level computations and data processing. Its stand-out features include:
- effectively scaling large data in clusters;
- functions of variant data processing modules, such as Hadoop YARN, Hadoop MapReduce; and
- usage of the Hadoop Distributed File System (HDFS) for data storage, which allows the distribution of massive data content across several nodes for parallel and distributed computing.
Becoming a Data Scientist
Thinkful offers a full-time five month data science course as well as a part-time six month flexible course. Each course is taught by experts in the field, and includes consistent one-on-one mentorship to assist you while learning the curriculum and tackling the real-world projects. Following the coursework, you get six months of dedicated career coaching that will get you into your new profession.
For further reading, check out our four must-read tips for launching a career in data science. Or get a glimpse of what’s ahead: find out what a day in the life of data scientist looks like.
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