Top 10 Data Science Tools To Learn in 2022

A successful career in data science depends on what data science tools you are proficient in. The relevance of data has made it so that even marketing strategists and small business owners need to be able to use data analytics tools. Now, there are a lot of data science tools available, with differences and similarities that may leave you confused.

So if you are wondering what tools can position you for a successful career, here are the top 10 data science tools to learn this year. These tools are used by industry giants and are lucrative knowledge investments.

Why learn more data science tools?

The world runs on information and that’s why data science is unsurprisingly one of the largest tech fields. The data science industry is predicted to grow by a CAGR of 25% through 2030. That’s great news for data scientists but it also means the competition is going to get stiffer.

To stand out from the crowd and remain relevant in the volatile tech industry, the data scientist must be versatile in knowledge and skills. Being a specialist in one tool isn’t bad but what happens if the industry leaves that tool behind?

Well, you can imagine.

Also, the labor market has seen more fluidity post-pandemic. People are climbing career ladders and searching for work-life balance by switching roles or finding new employers. Having more than a passing knowledge of various data science tools gives you the flexibility to pursue a more satisfying career.

Top 10 Data Science Tools to Know

Data scientists mine, organize, analyze, cleanse, visualize, and report data with the aid of data science software. There are many data science tools out there and you may find yourself wondering which ones you should invest your time in. Here are our top 10 recommendations

1. Statistical Analysis System (SAS)

SAS is one of the oldest and most popular data processing systems. It is used by well over 2000 companies globally including Zendesk, Honda, and Telenor. The system is targeted at companies that require complex statistical operations and analytics.

With this closed-source data processing software, data scientists can process data with intention-tailored tools. There are products for customer intelligence, fraud detection, forecasting, data visualization, machine learning, etc.

SAS is popular in the corporate world and learning it could be vital for career advancement. Also, the company organizes training programs that certify people as SAS experts.

However, some startups may prefer to use more convenient alternatives to SAS. To stand a better chance, in this case, it is smart to acquire general statistical knowledge that leaves you open to other opportunities. SAS or not.

Features:

•   Has an extensive and organized library of specialized tools in its product suite.
•  It can access and handle data from various databases including online databases, SAS tables, and Microsoft Excel tables
•  Has a stable graphical user interface (GUI)
•  Analyzes data using multivariate techniques, statistical forecasting, modeling, and linear programming.
•  Provides industry-standard data security measures using encryption algorithms.
  You can easily access SAS customer support for training, tutorials, free trials, and other resources.

2. Tableau

Tableau is one of the most used data visualization tools, well known for its richness and interactiveness. Data scientists use this tool to visualize statistical and geographical data. Since Tableau is a no-code platform, data scientists don’t need a major technical background to use the software. As such, it is seen as a super easy tool to use. Tableau is a good tool to master if you are considering a career in business intelligence alongside your data science pursuit. This tool offers a bright and lucrative future for professionals in BI and data analysis. About 64,000+ companies use Tableau and the expert demand is high. A quick check on Naukri showed 22,000+ Tableau vacancies waiting to be filled.

Here is a dedicated Tableau training platform to start your journey to being a Tableau expert.

Features:

• A drag-and-drop interface that is easy to learn and use.

•  Can extract and synthesize data from multiple and disparate sources, making it useful for solving complex data problems.
•  Allows in-memory connection to extract data as needed from external sources and live data connections for consuming data straight from the source.
•  It is very accessible via desktop, web, and mobile apps.
•  It comes with preinstalled maps and has predictive analytics capabilities.

3. Google Analytics

Google Analytics is directed at leveraging data science for digital marketing. It is used by businesses to observe, analyze and visualize the digital footprint of consumers. The data they get is then applied to marketing strategies. It is a no-code tool that anyone can easily learn and use. Google Analytics is one of the best data science tools for beginners to kickstart their careers. Over 73 million websites use Google Analytics, which tells you how mainstream this tool is. It looks great on any marketer or analyst resume. You can learn how to use this tool from the Google Analytics Academy.

Features:

•  High-end functionalities and in-depth analytics.
•  Allows creation of custom reports using Google Data Studio.
•  Integrates with other Google products like Google Ad Manager, Display & Video 360, Google Search Console, etc.
•  Funnel analysis that presents a complete user journey data map.
•  Capable of predictive analytics and pattern anomaly detection.

4. Tensorflow

TensorFlow’s Machine Learning (ML) and Deep Learning (DL) libraries make it one of the most used open-source data science tools in the world today. The tool is used to build ML and data processing algorithms and predictive models. Developers and data scientists who are interested in building careers in Artificial Intelligence will find it helpful to learn to use TensorFlow. Moreso, the opportunity is enormous. You’ll find 1700+ TensorFlow jobs on Glassdoor and more across other job platforms.

TensorFlow has rich learning resources to help you get started. Here’s a certified training platform to begin your TF journey.

Features:

•  Strong community support due to its free and open-source nature.
•  Has data visualization capacity.
•  Compatible with different programming languages like Python, Swift, and JavaScript.
•  Supports desktop, mobile, web, and cloud platforms for ML models.
•  Has multiple levels of abstraction and APIs that make model building easy.

5. Microsoft HDInsight

Microsoft HDInsight is also known as Azure HDInsight. This tool is an open-source data analytics tool used to process extremely large datasets. It can process both historical and real-time data, mostly suitable for ML, data warehousing, ETL (extract, transform, load), and Internet of Things (IoT) functions. Big companies like Adobe, Jet, and Milliman use this tool. That is a hint about how far an Azure HDInsight certification can take you. If you are ready, Microsoft has a 33 minutes Azure HDInsight Introductory course to help you learn more about the tool.

Features:

•  Integrates with big data processing frameworks such as Hadoop Spark, Hive, Mahout, Hbase, Storm, and Kaa.
•  Available as cloud service or on-premises structure.
•  Integration with Azure Monitor for real-time data monitoring and analytics.
•  HDInsight is compatible with different development environments like Visual Studio, VSCode, Eclipse, and IntelliJ for Scala, Python, R, Java, and more.

6. NaturalLanguageToolkit(NLTK)

NLTK is one of the tools that programmers use for Natural Language Processing (NLP). It is a Python-based tool. The data science software is used for text preprocessing, analysis, and visualization. NLTK is one of the most valuable and free DS tools available. As a matter of fact, universities across the United States teach it.

NLP is a pretty recent tech field and there are very limited tools available for it at the moment. Learning to use the NLTK is beneficial for enhancing careers in AI and NLP in Python-inclined workplaces. To learn NLTK, you’ll need at least a basic understanding of Python.

Features:

•  Open source with a rich community support environment.
•  Integrates with programs like WordNet.
•  Has accessible interfaces and allows programmers to drag and drop pieces of code.
•  Has pre-built algorithms and allows customizable NLP models.
•  Its algorithm can detect emotion and sentiments, provide summaries and recognize names. 7

7. D3.js

D3.js is a JavaScript library for creating interactive data visualizations using HTML, SVG, and CSS. People use it for data visualization with a data-driven approach in web browsers. According to Stackshare, about 695 top companies including Coursera, Accenture, Odoo, and Graphy use D3.js. This tool is accessible and a lot of data scientists use it. However, the flexibility of the tool and the relative difficulty in using it makes it a scarce skill. If you are interested in data-oriented programming and web development, D3.js is a good tool to learn.

Features:

•  Integrates with over 17 other tools including React, Bootstrap, C3.js, AngularJS, and Sencha Ext JS.
•  Free to use, open source, and has a vibrant and helpful support community.
•  Uses declarative programming and gives you total visualization customization control.
•  Supports large datasets.
•  It can use static data or fetch it from the remote server in different formats.

8. RStudio

R is a prestigious programming language used for statistical graphics and computing. R Studio is the Integrated Development Environment (IDE) for R, it also works with Python. R Studio provides packages and libraries for different phases of the data science life cycle.

R is open source and widely used across industries, from programming to business analytics and even biological sciences. Knowing how to deploy R Studio to solve data problems makes you versatile and extra-valuable to employers in various markets.

Interestingly, R Studio provides a broad learning curriculum for data scientists of all levels. A section of this curriculum is dedicated to helping beginners start their journey towards becoming an R Studio professional. If you are a beginner, you can start here.

Features:

•  User-friendly interface that makes it easy to use R.
•  Has robust libraries like ggplot2 and plotly for high-end and interactive data visualization.
•  Automatic code completion, bracket matching, and smart indentation.
•  Supports Word documents, PDF, HTML, and slideshows.
•  Has an interactive debugging function for fixing errors quickly and easily.

9. ApacheHadoop

Apache Hadoop is an open-source big data tool used to process very large datasets and high-level computations. As the need for big data insights grows across industries, many businesses turn to this tool, making it very affordable and accessible. More companies and even medium-scale businesses are using big data. There’s no shortage of demand for data scientists, and business intelligence and marketing professionals who can use Hadoop. You’ll find about 17,000+ Hadoop jobs on Indeed alone. There are lots of blog articles, video tutorials, and courses on the Hadoop website to help you learn to use this tool.

Features:

•  Uses Hadoop Distributed File System (HDFS) for large-scale data storage and parallel computing.
•  Integrates with other data processing modules such as Hadoop YARN, Hadoop MapReduce, etc.
•  Has high fault tolerance due to its data replication feature.
•  Data locality allows fast data transfer that consumes less bandwidth.
•  Open source, scalable, and affordable.

10. KNIME

KNIME is used for data mining, data reporting, data analysis, predictive analytics, ETL, and ML. The open-source tool is popular for its comprehensiveness and ease of use. It doesn’t necessarily require coding, you can simply drag and drop. But if you prefer to write codes, you can integrate R and Python.

This tool is highly ranked and widely used. Roles that request KNIME experience are abundant, LinkedIn alone has over a thousand vacancies currently.

Here are some valuable resources to help you get started with KNIME.

Features:

•  Interactive reporting.
•  Easy-to-use, intuitive GUI that allows users to perform data science tasks with basic programming knowledge.
•  Compatible with numerous data science tools, including Spark, Python, and Scala,
•  Allows collaboration via import and export of workflows
•  Allows parallel execution on multi-core systems. Conclusion Data science is an evolving career field. It has experienced constant growth in popularity in the past decades. Both small and big businesses depend greatly on data for survival. To stand a chance, they all need experts who are proficient in the tools required to collect and process the data. This article highlighted the top data science tools to know in 2022. With a decent knowledge of these tools (or a majority of them), you will position yourself for a limitless future as technology transforms and reforms before our very eyes.
  
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