The Top Data Analytics Tools Every Data Analyst Should Know in 2023
Data analytics continues to be one of the most in-demand skill sets in today‘s job market. As companies seek to harness the power of data to drive better decision making, the need for talented data analysts who can collect, analyze and interpret data keeps growing.
According to the U.S. Bureau of Labor Statistics, employment of data analysts is projected to grow 25% from 2021 to 2031, much faster than the average for all occupations. To take advantage of these expanding opportunities, aspiring and current data analysts need to have the right tools in their toolkit.
Data analytics tools are software and platforms that enable data analysts to extract insights from data. With the right set of tools, you can streamline data processing, uncover hidden patterns, create compelling data visualizations, build predictive models, and more.
But with the data analytics tools landscape rapidly evolving, it can be challenging to know which ones are worth investing your time and energy into learning. To help you navigate this complex space, we‘ve put together a guide to the top data analytics tools every analyst should know.
We‘ll explore tools across key categories like programming languages, BI and visualization software, big data platforms, and more. For each category, we‘ll highlight a few specific tools that are widely used in the industry and offer our take on their strengths and weaknesses. We‘ll also share our insights on data analytics tools trends to watch and tips for assembling your own world-class analytics toolkit.
Programming Languages for Data Analytics
Programming languages are the foundation of a data analyst‘s toolkit. Writing code allows you to automate data collection, manipulation and analysis at scale. It also provides more flexibility to customize your analysis compared to out-of-the-box tools.
The three most essential programming languages for data analysts to learn are:
1. Python
Python has become the most popular language for data analytics and data science. It has a beginner-friendly syntax, making it accessible even if you don‘t have a computer science background. But don‘t let the simple syntax fool you – Python is a powerful, general-purpose language suitable for everything from data cleaning to machine learning.
Python has an extensive ecosystem of open source libraries for data analysis, including:
- pandas for data manipulation and analysis
- NumPy for numerical computing
- Matplotlib and Seaborn for data visualization
- scikit-learn for machine learning
- TensorFlow and PyTorch for deep learning
If you‘re just getting started with Python for data analytics, we recommend checking out the pandas library first. Pandas allows you to easily load, filter, aggregate, merge and analyze tabular data using a DataFrame object.
2. R
R is another popular open source programming language used widely in statistical computing and data analysis. Compared to the general-purpose Python, R is more specialized for statistical analysis, data visualization, and machine learning.
Some key R packages to know for data analysis include:
- dplyr for data manipulation
- ggplot2 for creating plots and charts
- caret for machine learning
One advantage of R over Python is that it has more advanced statistical modeling capabilities out-of-the-box. It also makes it very easy to create publication-quality charts and graphs. However, Python is catching up quickly in these areas.
3. SQL
SQL (Structured Query Language) is a must-know for querying and manipulating relational databases. As a data analyst, you‘ll likely work with SQL on a daily basis to extract data from databases for further analysis.
SQL has been around for decades and is still the most widely used language for working with structured data. Learning SQL will give you a strong foundation for all your data analysis work.
While knowing SQL is essential, you don‘t necessarily need to be an expert to be an effective analyst. Focus on mastering the fundamentals – querying data, filtering results, joining tables, aggregating data and using subqueries. More advanced concepts like window functions and recursive queries are worth learning over time but aren‘t required on day one.
Business Intelligence and Data Visualization Tools
BI and data visualization tools enable you to explore and derive insights from data, create interactive dashboards and reports, and share your findings with stakeholders. These tools emphasize ease-of-use and typically don‘t require coding skills, making them accessible to analysts from non-technical backgrounds.
Some of the most popular BI and visualization tools used by data analysts include:
1. Tableau
Tableau is a market-leading analytics platform that allows you to connect to almost any data source, then visualize and analyze data without needing to know any code. It offers a highly intuitive drag-and-drop interface for building interactive dashboards, charts and graphs.
With Tableau, you can easily drill down into data, filter views, and see automatic visualizations based on the fields you select. It also has great mapping capabilities for visualizing geospatial data.
Tableau is a comprehensive platform with capabilities that extend beyond visualization into data prep, natural language queries, data science and more. But visualization remains its core strength and the most common use case for data analysts.
2. Microsoft Power BI
Power BI is Microsoft‘s business analytics solution that lets you visualize and analyze data from a variety of sources. It has a very user-friendly interface and allows you to create impressive dashboards and reports by simply dragging and dropping.
As a Microsoft product, Power BI has very tight integrations with other Microsoft tools like Excel, SQL Server, and Azure. So if your organization uses a Microsoft-centric data stack, Power BI will likely fit well into that ecosystem.
Power BI has both free and paid versions. The free version has robust basic capabilities but some advanced features like data flows and AI insights are only available with a Pro license.
3. Google Data Studio
Google Data Studio is a free, cloud-based data visualization tool that turns your data into dashboards and reports. It has native integrations to pull in data from Google marketing and advertising products as well as from databases and files.
Data Studio has a simple, intuitive report builder for creating interactive dashboards with tables, charts, geo maps and more. It‘s a good choice if you‘re already using other tools within the Google ecosystem like Analytics, Ads and BigQuery.
While not as powerful as Tableau or Power BI, Data Studio is completely free to use, making it an attractive option for smaller teams and companies.
Big Data Processing Tools
Big data processing tools are used to process and analyze massive, complex data sets that are too large to handle with traditional databases and tools. These tools are designed to distribute data processing across large clusters of commodity servers to enable faster, more cost-effective analysis of big data.
Two of the most widely used big data processing tools are:
1. Apache Spark
Apache Spark is a distributed computing framework and set of libraries for processing big data. Spark can run workloads 100x faster than Hadoop MapReduce by doing processing in-memory and can be used to build data pipelines, machine learning models, graph algorithms and more.
Spark has APIs for programming in Python, R, Scala and Java so data analysts can leverage it using their language of choice. The Spark SQL module and dataframes API make it easy to interact with structured data.
Spark can run on top of Hadoop, Mesos, Kubernetes or as a standalone cluster. All the major cloud providers also offer managed Spark services to simplify deploying and maintaining Spark apps in the cloud.
2. Apache Hadoop
Hadoop is an open source framework for distributed storage and processing of big data. At its core, it has a distributed file storage system called HDFS and a parallel processing framework called MapReduce.
On top of the core modules, the Hadoop ecosystem includes a large collection of tools for data processing, querying, machine learning and more. These include:
- Hive and Pig for SQL-like querying
- Kafka and Flume for data ingestion
- HBase for a NoSQL database
- Mahout for machine learning
Hadoop was one of the first widely adopted big data platforms and remains in use at many large enterprises with on-premises data centers. But adoption has slowed as more companies shift big data workloads to the cloud and real-time processing engines like Spark.
Trends Shaping the Data Analytics Tools Landscape
Here are a few key trends in the data analytics tools space that are worth keeping an eye on:
The Rise of AutoML
Automated machine learning (AutoML) tools are democratizing access to advanced analytics and data science capabilities. AutoML platforms like H2O.ai and DataRobot automate the process of training and tuning machine learning models, allowing analysts to leverage powerful ML capabilities without needing deep expertise in data science and coding.
Convergence of Data Analytics and Data Science
The lines between traditional BI and data science are blurring as BI platforms add augmented analytics and data science capabilities. Analysts are increasingly expected to go beyond descriptive analytics into diagnostic, predictive and prescriptive analytics. Tools are evolving to support this shift, with leading platforms like Tableau and Power BI adding features for statistical analysis and machine learning.
Explosion of Cloud-Based and SaaS Analytics
Cloud data platforms are making it easier than ever to store, process and analyze data without having to maintain on-premises infrastructure. The major cloud providers all offer comprehensive analytics solutions, from data warehouses to BI tools to AutoML services. There are also a growing number of SaaS analytics vendors providing cloud-native, vertically focused analytics for sales, marketing, finance and other business functions.
Tips for Building Your Data Analytics Toolkit
With so many tools to choose from, building a well-rounded data analytics toolkit may seem daunting. But you don‘t need to learn every tool out there. Here are a few tips to guide your journey:
-
Start with the fundamentals – make sure you have a solid grasp of at least one programming language (Python or R) and know SQL. These form the foundation needed to work with other tools.
-
Focus on tools that align with your role and company‘s tech stack. If you‘re working primarily with a particular BI platform or big data technology, go deep on mastering that toolset.
-
Continuously expand your tool proficiency over time. Make it a habit to learn a new tool or take a deep dive into a new area every few months to keep your skills fresh.
-
Don‘t neglect soft skills. Communication, problem solving and domain knowledge are just as critical as technical chops for success as a data analyst. Practice communicating insights and collaborating cross-functionally.
-
Tap into the wealth of online resources to keep learning. From documentation to online courses to community forums, there are ample resources available to deepen your expertise with data analytics tools.
The data analytics tools landscape will keep evolving, but the core skills of an analyst – understanding the business, working with data, deriving insights, telling stories with data – will continue to be in high demand. By continuously learning and maintaining a balanced set of tools and skills, you‘ll be well-positioned to thrive in a data-driven world.