13 Essential Data Science Tools
(and How to Use Them)
Powerful new data science tools are created nearly every day. These 13 are among the best you can take advantage of right now.
So many data science tools are hitting the market that keeping up with what’s out there can feel like a full-time job. And then there’s the small matter of making sure you can use all these tools to their fullest potential.
Luckily, your team doesn’t have to try to keep up on their own. The data scientist community excels at providing tutorials and mentorship for how to make the most of new tools just as quickly as they emerge.
Here, we’ve collected 13 of the best data science tools available to scientist teams right now, and provided our best-in-class LinkedIn Learning resources for how your team can get started learning to use them right away.
13 Essential Data Science Tools Your Team Can Learn Now
Python remains the gold standard of dynamic coding languages used by data scientists. It has the largest data science user base of any programming language, more data science tools are written using it than any other language, its data science support community is the largest, most active, and fastest growing, and it’s the most commonly-used dynamic language for major organizations including Google and IBM.
"Python is one of the best introductory programming languages because of its intuitive syntax, wide popularity, ease of use, and similarity to other programming languages. This makes it not only easy to learn but easy to port your Python skills over to other languages you might program with in the future."
How to use it:
Data Cleaning and Preprocessing
Before diving into analysis, data often needs to be cleaned and prepared. Python's libraries, like Pandas, make it easy to handle missing data, remove duplicates, and transform data into a suitable format for analysis.
Matplotlib (see below) is a popular Python library for creating insightful visualizations, allowing data scientists to present their findings in a more understandable and compelling manner.
Python's robust ecosystem of machine learning libraries, such as scikit-learn, TensorFlow, and PyTorch enable data scientists to effectively build and deploy predictive machine learning models for tasks like classification, regression, clustering, and natural language processing.
- Python Essential Training with GLG Senior Software Engineer Ryan Mitchell
- Python for Data Science Essential Training, Part 1 and Part 2, with professional data consultant Lillian Pierson
- Python Data Analysis with NASA Theoretical Astrophysicist Dr. Michele Vallisneri
- Python Functions for Data Science with Madecraft Technical Curriculum Architect Lavanya Vijayan
2. The Natural Language Toolkit (NLTK)
The Natural Language Toolkit is a comprehensive suite of libraries and tools for natural language processing in Python. It provides a wide range of functionalities for working with human language data, including automated text processing, translation, classification, and categorization, making it a popular choice for data scientists who need to work with language and text data as part of their sets.
"NLTK is a suite of libraries for natural language processing available in Python. It provides text processing capabilities, analytics capabilities, a corpora (language dataset) of sample data of various types, and it also supports a number of machine learning features, like classification and clustering algorithms. This makes NLTK an end-to-end library for text processing and analytics."
How to use it:
Data scientists use NLTK to clean and preprocess text data before feeding it into models via tokenization, removing stopwords, and converting text into a suitable format for analysis.
NLTK can extract useful features from text data, which can be utilized as input for machine learning algorithms.
NLTK's language processing functionalities assist in part-of-speech tagging, parsing, and entity recognition, enabling data scientists to understand the structure and meaning of sentences.
Matplotlib is a popular Python library used to create high-quality chart, plot, and graph data visualizations quickly and easily. Matplotlib is particularly useful for data scientists because it provides a wide range of customization options for data visualizations, allowing for a high degree of versatility and flexibility without sacrificing ease of use.
"One of the key features of Matplotlib that I find valuable is the possibility to use a programmatic approach in which graphs are created by writing code. You control every aspect of their appearance instead of manually creating graphs using a graphical user interface. This is extremely important because programmatically created graphics can be made reproducible or easily adjusted when data is updated and save time."
How to use it:
Data scientists can use the visualizations Matplotlib generates to efficiently discover underlying patterns, trends, and relationships in visualized datasets.
Matplotlib is very helpful for visualizing model performance metrics such as accuracy, precision, recall, or ROC curves, all of which aid in evaluating machine learning models.
Matplotlib’s clear data visualizations are particularly helpful for communicating data narratives and results to stakeholders.
- Matplotlib section from Python for Data Visualization with Python instructor Micheal Galarnyks
- NumPy Essential Training 2: MatPlotlib and Linear Algebra Capabilities with software developer Terezija Semenski
- Matplotlib section from Python: Programming Efficiently with NASA Theoretical Astrophysicist Michele Vallisneri
TensorFlow is an open-source machine learning library designed to help data scientists and developers build and deploy machine learning models more efficiently. TensorFlow’s data flow graph, which represents mathematical operations as nodes and data as edges between these nodes makes it far easier for data scientists to execute efficient computations on CPUs, GPUs, or specialized hardware.
"TensorFlow is designed to be very generic and open-ended. You can define the graph of operations that does any calculation that you want. While TensorFlow is most often used to build deep neural networks, it can be used to build nearly anything that involves processing data by running a series of mathematical operations."
How to use it:
Data scientists can use TensorFlow to deploy trained models to production systems and edge devices for real-time inference.
TensorFlow makes updating and adding customization to models easy, allowing data scientists to experiment with architectures designed to suit specific needs.
It’s simple to add pre-trained models to TensorFlow and update or customize them with new data and architectures.
- Building and Deploying Applications with TensorFlow with machine learning consultant Adam Geitgey
- TensorFlow: Neural Networks and Working with Tables with data science consultant Jonathan Fernandes
- Deep Learning Foundations: Natural Language Processing with TensorFlow with Harshit Tyagi
Scikit-learn is another open-source library for Python. It remains one of the most popular and widely-used libraries in all of data science, because of its user-friendliness and the flexibility of its set of simple data analysis and machine learning tools. Scikit-learn provides a particularly wide range of machine learning algorithms and utilities, including classification, regression, clustering, preprocessing, and more.
"Scikit-learn is a very popular open source machine learning project which is constantly being developed and improved upon. It’s widely used in academia and industry, and there are always more scikit-learn tutorials being written. This also means it’s easier to get your questions answered on stack overflow."
- Machine Learning with Scikit-Learn with Python instructor Michael Galarnyk
- Scikit-learn section of Data Science Foundations: Python Scientific Stack with 353Solutions CEO Miki Tebeka
- Scikit-learn section of Machine Learning and AI Foundations: Value Estimations with machine learning consultant Adam Geitgey
R is an open-source code language designed specifically for statistical computing, data analysis, and other data science needs. It provides a set of powerful user-designed and supported tools specially designed for data science applications such as statistical analysis and modeling and data manipulation.
Compared to Python, R is less user friendly and takes more time to learn, but its specialized packages are more capable of simplifying highly-complex and time-consuming statistical analysis.
"Now, if you go to any data science website, one of the first questions people are going to ask is, should you learn Python or R? Now, this is based on the assumption that you can only learn one. Let me say this. First off, Python is especially strong in machine learning and data-based app development. So, if you're developing those kinds of things, Python will probably be your first choice. On the other hand, R is especially strong in data analysis and scientific research."
How to use it:
R provides a wide range of tools and packages for data wrangling and manipulation, making it easy to clean, reshape, and preprocess data before analysis.
R offers powerful data visualization capabilities through packages like ggplot2, which allows data scientists to create complex but clear visualizations to explore and communicate insights effectively.
Statistical analysis and modeling
R has an extensive collection of statistical functions and libraries, which allow data scientists to perform various analyses, including regression, hypothesis testing, time series analysis, clustering, and more.
The Getting Started with R for Data Science Learning Path, featuring courses by Data Analytics Professors Barton Poulson and Mike Chapple, will help your team:
- Learn how R works, from foundational concepts to advanced applications
- Practice using R with Excel and Tableau
- Explore the applied use of R in social network analysis
How to use it:
D3.js makes it easy to construct web-based dashboards for visualizing and presenting data.
By connecting multiple visualizations in custom dashboards, D3.js provides a helpful way to visually illustrate data narratives on the web.
Interactive data exploration
Data visualizations created using D3.js can be interacted with directly via the web, allowing for interactive presentations and data sharing.
KNIME is an open-source data analytics platform that provides a graphical interface for data scientists and analysts to design and execute many data workflows without needing to code. Instead, KNIME users simply connect various data processing and analysis modules, called nodes, to create the architecture they need to parse and analyze their sets.
"KNIME is a popular open-source option that is very easy to learn. It offers just about all the functions you could ever need natively, but for that rare function that isn’t available, you can always use R and Python right in KNIME. You’re never limited with KNIME."
How to use it:
KNIME provides an easy and intuitive way to find hidden patterns in relationships in large datasets.
By connecting nodes in KNIME, data scientists can quickly and easily create automated processes for their daily applications.
KNIME’s nodes can be connected to create customized automated workflows for preprocessing data sets, performing cleaning and transforming functions.
The Learning Codeless Machine Learning with KNIME learning path with data miner, speaker, and author Keith McCormick is an excellent introduction to using the KNIME suite to conduct visual programming for machine learning. When taking this learning path, your team will:
- Explore no-code solutions to modeling with KNIME
- Understand the principles of predictive analytics and decision trees
- Learn how to work with data for predictive modeling
WEKA stands for “Waikato Environment for Knowledge Analysis.” It is a popular open-source data mining software written in Java. WEKA provides a large collection of machine learning algorithms, data preprocessing and visualization tools, and evaluation methods. Data scientists appreciate its flexibility and extensive use-cases.
"Weka is open source software that offers a collection of machine learning algorithms through its user-friendly graphical user interface. In addition to the familiar tools for classification, regression, and clustering, Weka also has features such as data pre-processing and visualization."
What to use it for:
Association rule mining
WEKA is frequently used for market basket analysis, because it’s useful for discovering relationships between large data sets.
WEKA has powerful tools for grouping data instances into clusters based on similarities automatically.
WEKA tools can also predict continuous numeral values within and across data sets, making it highly useful for predictive analysis.
- WEKA section in Data Science Tools of the Trade: First Steps with Professor Jungwoo Ryoo
- WEKA section in Machine Learning and AI Foundations: Advanced Decision Trees with KNIME with data miner and author Keith McCormick
- Machine Learning and AI Foundations: Classification Modeling with Keith McCormick
Statistical Analysis System, or SAS, is a programming language primarily used for advanced statistical analysis, data management, data visualization, and business intelligence. Though it is not open source and has a smaller user base than either Python or R, it is also considerably more advanced, making it particularly useful for handling, cleaning, and preprocessing large datasets efficiently and conducting fast and accurate data transformations and aggregations.
"Why SAS Studio? SAS Studio is consistent, meaning you learn one interface that you can use throughout your career. It’s highly available, meaning you can use the same interface wherever you need it. It’s assistive, and it’s for programmers, because, while you can do all the coding if you want, there are also point and click functions which actually help create code for you. And this functionality is great to take advantage of if you're either new to SAS or simply new to programming in general."
How to use it:
SAS can handle large datasets very efficiently, cleaning and preprocessing data and performing large transformation and aggregations quickly.
SAS is a comprehensive data science tool that provides a wide range of built-in applications for data analysis applications including regression analysis, data mining, time series analysis, forecasting, and more.
High reliability and security
SAS’s robust security features and extensive testing of new features make it a favorite of industries dealing with particularly sensitive data, such as healthcare, finance, and government.
The Prepare for the SAS 9.4 Base Programming (A00-231) Certification Exam learning path, provided for LinkedIn Learning directly from the SAS Institute, will provide everything your team needs to earn the SAS Base Programming Specialist certification. Have them take it to:
- Learn the skills needed to pass the SAS 9.4 (A00-231) exam
- Learn how to read and create data files
- Understand the concepts to manage data using SAS 9.4
Tableau’s built-in tools and integrated functionalities are designed to allow users to manage, visualize, and organize data from across sources within a single, fully-supported environment. Tableau is very popular with data scientists because its drag-and-drop interface makes it easy to sort, compare, and analyze data from across multiple sources at once, while its data organization and visualization tools make it just as easy to arrange that disparate data into coherent, visualized narratives.
"Tableau is a powerful and versatile tool for visualizing data. Mastering the skills you need to use Tableau 2023.1 effectively will let you work quickly and make great decisions."
- Curt Frye in Tableau Essential Training
How to use it:
Tableau allows data scientists to create compelling and meaningful representations of complex data sets quickly and easily.
Tableau can connect to various data sources, including spreadsheets, databases, cloud services, big data platforms and programming languages such as R and Python.
Tableau's collaborative features enable data scientists to share their insights and visualizations easily. They can also publish interactive dashboards to Tableau Server or Tableau Online, allowing real-time access to data and analysis for a broader audience.
12. Apache Spark
Apache Spark is an open-source distributed computing framework designed for processing large-scale data and performing real-time data analysis. Its primary feature is its in-memory processing capabilities, which accelerate data processing speed compared to traditional processing systems.
"Apache Spark is arguably the best processing technology available for data engineering today. It has been constantly evolving over the last few years, adding new capabilities and improving in reliability."
How to use it:
Big data analytics
Spark is particularly well-suited to big data analytics because it is a distributed framework. Data scientists frequently use Spark to process and analyze large data sets at scale efficiently.
Real-time data processing
Spark Streaming enables data scientists to perform real-time analytics on streaming data, making it particularly helpful for applications like fraud detection or IoT data processing.
A graph processing library included in Spark called GraphX allows data scientists to analyze and process graph data instantly.
13. Apache Hadoop
Apache Hadoop is an open-source distributed computing framework designed to process and store large volumes of data across clusters of hardware. The Hadoop Distributed File System stores distributed data in a single location, from which the MapReduce programming model is used to process it in parallel. This saves considerable time and increases visibility into cross-data patterns for analysis.
"We have more and more information being made available from the devices that we wear, and it makes good sense to integrate that information with other types of solutions, but we don’t want any data inconsistency between them. So, in addition to the volume of data, it’s the relationships between the data. If the data is mission critical or line of business, you’re really going to want to keep that in a relationship database system. That kind of behavior data is a great candidate for Hadoop."
Hadoop is great for processing large volumes of data in batch mode, where data scientists can analyze historical data easily.
Hadoop is a cost-effective solution for data warehousing, as it can store and manage vast amounts of data for future analysis.
Hadoop can store log files from various sources, making it easier for data scientists to collect, store, and analyze these logs together all at once.
Keep up with the best training for the latest data science tools
The LinkedIn Learning courses featured here are only a small sample of the data science tool tutorials and walkthroughs available right now. Even better, LinkedIn Learning is committed to developing expert-led courses for all ability and experience levels just as quickly as new data science tools emerge.
If you want to ensure your data scientists always have access to the latest and greatest data science tools training and information available, request a demo of LinkedIn Learning today.