Starting a Career in Data Analytics? Here Are 23 Must-have Tools

Around 2.5 quintillion bytes of data are produced every day, making data analytics an attractive career choice. Data analytics career paths range from data engineer to data analyst and even data scientist, with opportunities in finance, health care, e-commerce, retail, B2B marketing, manufacturing, logistics and many more.

Data analytics involves collecting, organizing and analyzing data to gain insights that can help make business decisions. Data analytics professionals use data analysis techniques to enrich the data and interpret and draw insights from it. To do this, they need to be well-versed in a variety of tools – from understanding software and programming languages, data engineering and machine learning technologies to utilizing visualization tools. Keep reading to learn about essential tools needed to succeed in data analytics.

23 Tools for Anyone Entering a Data Analytics Career

When starting a career in data analytics, it’s essential to have the right tools and technologies to be successful. Whether you’re a data engineer, data analyst or data scientist, a variety of tools can help you. Understanding what tools are available and how they can help improve business performance can make you a valuable asset to any analytics team.

*Tools with an asterisk are not always adopted by junior data analyst, but professionals with 2 or more years of experience in the field.

Data Visualization Tools

Microsoft Power BI, Tableau, Qlik and Sisense* are popular tools for data visualization and analysis. They can create dashboards, charts and other visuals that can communicate complex data in an easy-to-understand manner. With intuitive interfaces and a range of features, these tools allow users to tailor the data visualization to their needs.

Features:

• Data wrangling and preparation
• Advanced analytics and predictive modeling
• Natural language processing capabilities
• Integration with other data sources
• Advanced dashboarding capabilities

Pros:

• Intuitive user interface
• Wide range of advanced data analytics features
• Can export data to other programs and systems

Cons:

• The cost for the advanced features can be quite high
• Complex tools for non-technical users

Pricing:

• Microsoft Power BI: Free and paid versions
• Tableau: Free and paid versions
• Qlik: Free and paid versions
• Sisense: Pricing information not available

Programming Languages

R Studio, Python and SQL are the primary programming languages used in data analytics and are a must-have for anyone wanting to start a career in data analytics. They are versatile and powerful, and can be used for data wrangling, cleaning, analysis, machine learning models and statistical analysis, and creating advanced visualizations and interactive dashboards.

R Studio uses the R programming language, a powerful data analysis language. It has many expansion libraries critical for data analytics, such as ggplot2, Shiny and the "Tidyverse" collection of packages.

Python is a universal programming language for data wrangling and cleaning, analysis, machine learning models and statistical analysis. The expansion packages such as NumPy, pandas, Seaborn, Matplotlib and scikit-learn are essential for data analysis. SQL is a powerful query language used to manage databases. It is used for data extraction and manipulation and to create complex queries that can produce meaningful insights.

A related tool is Jupyter Notebook, a computational notebook. It’s a free online tool that allows users to share several programming-related resources such as live code, visualizations, equations, etc. It is popular among data scientists and developers for its interactive environment that allows users to work with data quickly and easily.

Features:

• Machine learning capability
• Statistical analysis capabilities
• Integration with other data sources
• Data wrangling and preparation
• Visualization capabilities

Pros:

• Versatile and powerful tools for data analytics
• User-friendly interface
• Predictive analytics capabilities

Cons:

• Complex languages require some technical background or skills to use properly
• Advanced features aren’t always accessible to all users

Pricing:

• R Studio: Monthly subscription pricing
• Python: Free
• SQL: Free and paid versions for a one-time fee
• Jupyter Notebook: Free

AI Cloud Analytics Platforms

DataRobot, Google Cloud Platform and Microsoft Azure are examples of cloud-based analytics platforms that offer a range of services and tools for data analysis. They are options for data analysts looking to perform complex predictive analytics and machine learning tasks. Whether an advanced user or a non-technical analytics enthusiast, these platforms can help you get the most out of your data.

DataRobot aims to make data science and analytics easier for everyone. It provides a proprietary AutoML technology that automates data analysis, from data preparation to building and deploying predictive models. Google Cloud Platform is a suite of cloud computing services offered by Google. It offers a wide range of analytics and machine learning services and can be used for data processing, analytics, machine learning, artificial intelligence and more.

Microsoft Azure is a cloud platform that supports a range of data analysis and machine learning capabilities. It offers data storage, processing, analytics, machine learning and visualization services.

Features:

• Cloud-based analytics services
• Machine learning and predictive modeling
• Data visualization capabilities
• Integration with other data sources
• Advanced dashboarding capabilities

Pros:

• Cloud-based services make data analysis easier
• Predictive analytics capabilities
• AI-powered data analysis and insights
• Secure, reliable and scalable platform

Cons:

• Costly data storage and processing fees
• It can be complex for non-technical users to navigate

Pricing:

• DataRobot: Monthly subscription
• Microsoft Azure: Free and paid versions, which vary depending on the product
• Google Cloud Platform: Free and paid versions (the price varies depending on the product and usage)

Machine Learning Tools

Machine learning tools like KNIME*, Databricks and DataRobot provide powerful predictive capabilities for data analysis. KNIME allows users to build and deploy models quickly, while Databricks and DataRobot provide advanced AI-powered data analysis capabilities.

KNIME provides a user-friendly environment to build, test and deploy predictive models. It allows more advanced users to work with data from different sources and use a range of algorithms for predictive modeling. Databricks is an AI-powered analytics platform that helps businesses quickly build and deploy analytical models. It offers a range of data processing, machine learning and analytics capabilities.

DataRobot is an AI-powered analytics platform that provides advanced predictive analytics capabilities. It allows users to build, deploy and manage predictive models quickly and easily. It’s not a tool to be used by those just starting out, but it’s good for you to know that it exists and its purpose.

Features:

• Data processing, exploration and modeling capabilities
• Integration with other data sources
• Advanced machine learning capabilities
• Automated model validation

Pros:

• Quick and easy model building and deployment
• Advanced predictive analytics capabilities
• Secure and reliable data processing

Cons:

• Expensive solutions for non-enterprise users
• Advanced features require technical knowledge

Pricing:

• KNIME: Free and paid versions based on a subscription model
• DataBricks: Free trial and paid versions on a monthly subscription
• DataRobot: Monthly subscription-based pricing

ETL and Predictive Analytics Tools

ETL and Predictive Analytics tool like Google Data Studio, RapidMiner*, Microsoft Excel/Google Sheets, Alteryx and SAS Business Intelligence help businesses extract, transform and load data (ETL) and perform predictive analytics. Whether you’re a data analyst, business analyst or non-technical user, these tools can help you get the most out of your data.

Google Data Studio is a free data visualization and reporting tool. It helps you quickly create dashboards, reports and visualizations from your data. RapidMiner is a tool for the more experienced data analysts that enables users to quickly build, deploy and monitor models using AI-powered data mining. Microsoft Excel is a widely used analytics and data analysis tool. It helps you organize, analyze and visualize data quickly. Google Sheets is a similar tool.

Alteryx is an ETL and predictive analytics platform that allows users to build and deploy models and manage data quickly. SAS Business Intelligence is a powerful suite of analytics and reporting tools that offers a range of data preparation, analysis and visualization capabilities.

Features:

• Business intelligence and analytics capabilities
• Data preparation, exploration and modeling
• Data transformation, ETL and predictive analytics
• Data visualization capabilities

Pros:

• Improves data analysis and decision-making
• Comprehensive BI and analytics capabilities
• Data entry and preparation made easy
• Wide range of data exploration and modeling capabilities

Cons:

• Limited support for complex data sets
• Advanced features require technical knowledge

Pricing:

• Google Data Studio: Free
• Rapid Miner: Free and paid versions that have a one-time fee
• Microsoft Excel: Monthly or yearly pricing
• Alteryx: Free trial and paid versions based on yearly subscriptions
• SAS Business Intelligence: Free trial and paid versions based on yearly subscriptions

DevOps Monitoring Tools

DevOps monitoring tools help businesses track and monitor their applications' real-time performance, availability and scalability. Apache Spark and Splunk* are two popular options.

Apache Spark is an open-source distributed analytics engine allowing users to analyze the process quickly and transform data. It also provides data visualization capabilities and streaming analytics.

Splunk is a data management and analysis platform that helps businesses visualize, monitor and analyze their data. It supports streaming analytics, real-time monitoring, and machine learning. It’s appropriate for professionals with over two years of experience.

Features:

• Real-time data analysis capabilities
• Data visualization and monitoring capabilities
• Streaming analytics
• Machine learning capabilities

Pros:

• Quick and easy data analysis and monitoring
• Improved scalability and performance
• Competent data visualization capabilities
• Advanced data exploration and machine learning tools
• Secure and reliable data management

Cons:

• Steep learning curve for non-technical users
• Expensive solutions for small businesses

Pricing:

• Apache Spark: Free
• Splunk: Monthly subscription pricing based on the number of hosts you need

Cloud Databases

Cloud databases allow businesses to store and manage data in the cloud. Airtable is a cloud database that allows users to store, manage and query data. SAP Hana is a powerful relational database management system that provides businesses with real-time analytics capabilities.

Features:

• Real-time data management and analysis
• Data visualization capabilities
• Cloud storage solutions

Pros:

• Quick and easy data access and management
• Competent data visualization capabilities
• You can access data from anywhere
• Competent data security and protection

Cons:

• Internet connection necessary to access data
• Advanced features require technical knowledge

Pricing:

• Airtable: Free and paid versions with a one-time fee
• SAP Hana: Free version and paid versions based on capacity units and charged monthly

Get the Necessary Tools to Start a Career in Data Analytics

A successful career in data analytics requires mastering tools and technologies that enable you to gain insights from data. Depending on your career path, you need to know different programming languages and analytics platforms. Popular tools include Microsoft Power BI and Google Analytics, enabling you to understand data quickly and easily.

Consider using a powerful tool like Splunk or Apache Spark to gain insights from complex data. These tools provide data exploration, machine learning and streaming analytics capabilities. Additionally, cloud databases like Airtable and SAP Hana can help you to store and manage data in the cloud.

Consult a career expert to ensure you have the necessary tools and expertise for a career in data analytics. As the field evolves, staying up to date with the latest technologies and trends is essential to remain competitive in your career.

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