The Ultimate Set of Tools You Need To Ace Data Analysis

Data analytics is a top career option for IT professionals seeking a specialization. The World Economic Forum reported in 2023 that Opens a new window more companies consider analytical thinking a core skill than any other skill. The US Bureau of Labor and Statistics predicts the job market for data scientistsOpens a new window will grow 36% over the next decade. In other words, brushing up on your data science skills is a good investment.

You can start your journey by assembling the perfect toolkit for conducting data analysis. Ideally, your initial exploration should cover the entire spectrum of data science, including programming languages, business intelligence (BI), predictive analytics (powered by ML), and deep learning. 

Whether you are an IT professional looking to diversify or an aspiring data scientist just getting started, the following list will ensure that you are equipped to take on common data modeling tasks, integrate different data sources, uncover business insights, and factor in a company’’ unique security, governance, and cost limits. 

Learn More: What Is Data Analytics?

Which Programming Languages Should You Include? 

Knowledge of different programming languages will help you organize unstructured datasets in a specific application environment and create the foundation for an insight generation engine. Kaggle’s 2022 State of Data Science and Machine Learning study Opens a new window found that Python is the most common programming language used by data scientists. For developers, Python and SQL are among the top 5 languagesOpens a new window to master (the first two – JavaScript and HTML/CSS are for generic app development). 

As a data analysis beginner, the following languages belong in your toolkit: 

1. Python 

There are several reasons to pick up Python skills – it offers incredible ease and flexibility when cleaning, manipulating, or analyzing data. Python fits into general applications of data science. Machine learning use cases prefer Python as the base language. Within Python’s 200,000+ libraries, prioritize the following libraries that are specifically relevant for data analysis. 

• • Pandas – an open-source data analysis and manipulation tool built on top of Python. 
  • Natural Language Toolkit (NLTK) – useful for language processing and text to speech 
  • Scikit-Learn –  to train ML models 
  • Jupyter – a web app that lets you create and share your Python-based data reports

2. R Studio

Originally intended for statistical computing, R has several features you would need as a data analyst. It can help in data exploration, statistical modelling, and data visualization, easily integrating with other languages like C++, Java, or Python. Apart from learning the language, the RStudio Desktop application should also be there in your toolkit. Like Python, R has thousands of open-source packages – here are the ones you would need for data analysis. 

• • Tidyverse – a set of R packages that help you tidy data
  • GGPlot2 – used for visualizing data without being limited to pre-defined charts
  • R Markdown – used to convert data analysis into high-quality reports 
  • Shiny – lets you build web apps using R for interactive data exploration 

3. SQL

According to Kaggle’s latest data, SQL has replaced R as the second most popular programming language for data scientists. SQL is one of the oldest querying languages associated with data science, and used by 60% of professionals. Unlike R or Python, its purpose isn’t manipulation or app integration. SQL serves as the programming language of choice for data archiving and database management, a staple for large enterprises around the world.

SourceOpens a new window : Indeed Hiring Lab

Learn more: 12 Differences Between R and Python

Which Business Intelligence (BI) Tools Should You Adopt?

If programming languages deal with the technical aspects of data analysis, BI is key to the business side. Using BI tools, you can present data more meaningfully, convince non-technical stakeholders of its value, introduce data reusability and modularity, and essentially “productize” your data analysis. The U.S. Bureau of Labor Statistics predicted that demand for BI skills would rise by 11%Opens a new window through 2033, making it an important skill to acquire over the next five years. 

Some of the tools you need for BI in data analysis are:

1. Tableau

Even after Salesforce’s acquisition of Tableau, it remains one of the most popular software for business intelligence and data visualization. There are several Tableau solutions for free, business, and technical use, based on the core query language VizQL. It can handle data at scale, creating reports/dashboards that are easily shareable and embedding-friendly. 

2. Microsoft Power BI 

Gartner’s Magic Quadrant for Analytics & Business Intelligence places Power BI ahead of Tableau by a significant margin, thanks to its frequent updates that improve reporting, modeling, and data preparation capabilities. For organizations with an existing Microsoft dependency, it makes sense to add Power BI to your toolkit as it will integrate seamlessly. 

Source: MicrosoftOpens a new window

3. Qlik 

Qlik is among the more accessible BI solutions out there, integrating with your existing data lakes, data streams, and data warehouses to create business-ready applications. Combined with programming know-how, Qlik can help you perform sophisticated analytics exercises and gain from capabilities like artificial intelligence (AI) and machine learning (ML).  

4. KNIME

For those just getting started with data analysis, KNIME is an open-source, low-code platform that is available for Windows, MacOS, and Linux. It requires minimal coding skills to use, and covers the end-to-end analysis lifecycle from data transformation to presenting insights. 

5. D3 

Technically, this is a subset of the popular programming language JavaScript – but it is included under BI on this list due to its data visualization capabilities. D3 uses industry standards like Scalable Vector Graphics and Cascading Style Sheets to bring your data to life. There are plenty of open-source D3Opens a new window assets on GitHub that definitely belong in your toolkit. 

6. Tellius

Tellius incorporates machine learning and artificial intelligence into its data analysis, so it may be more user-friendly for those without a strong analytics background. End users can ask questions in natural language, while data analysts can model and transform data from multiple sources. It is powered by a “dual analytics engine” so that it can respond to ad hoc queries, generate insights, and develop models based on existing data.

Keep in mind that for several BI tools, you do not need any prerequisite programming expertise.

Learn More: 10 Popular Business Intelligence Platforms 

How to Use Machine Learning and Deep Learning for Better Insights 

This last set of tools have to do with advanced insight generation, taking off from the data you already have to extract the future insights users need. An academic reportOpens a new window based on 16,000+ survey responses and job ad analysis found that predictive analytics and machine learning are two of the most valuable data science skills. For further specialization, you can explore deep learning, which specifically delves into human behavioral data. 

Some of the tools you need at this end of the spectrum are: 

1. Apache Spark 

Apache Spark is an open-source analytics engine for large-scale datasets that let you program entire clusters for predictive insights. Spark ML is one of the key components to explore and process large-scale unstructured data and generate predictive insights. 

2. BigML

As the name suggests, BigML is a machine learning specialist that will prove useful across your data analysis career. The platform offers ready-to-use ML libraries for supervised and unsupervised learning and is fully programmable and interoperable with your existing IT/data tools. 

3. TensorFlow 

TensorFlow by Google is almost synonymous with machine learning and deep learning, comprising a purpose-built symbolic math library. The core library is open-source for training ML models, but you have options for javaScript, mobile/IoT, and end-to-end data solution production as well. It has applications in speech recognition, drug discovery, image classification, and more – so, it is definitely a tool that keeps on giving. 

4. MATLAB

MATLAB is a proprietary programming language designed specifically for mathematical analysis and UI design. It is an important tool in your data analysis toolkit. It supports big data use cases, machine learning, deep learning, ML model conversion, document data analysis, and integration with live data sources. However, MATLAB is best suited for hardware engineering and not app development. 

5. Altair’s RapidMiner

For machine learning, deep learning, text mining, and predictive analytics, RapidMiner is an extremely popular data science platform. It was recognized as a Magic Quadrant leader in June 2024, owing to its ease of use for professional data scientists and data-literate business users alike. The platform offers multiple products, including those for cloud-based development, connecting smart products, extracting data from documents and text files, and replacing existing SAS environments. 

Learn more: 5 AI Programming Languages for Beginners

Getting Started 

Once you start acquiring data analysis tools and skills, the marketplace is almost infinite. Nearly every leading software company like SAP, Microsoft, etc., has a data analysis tool on offer – which can pay rich dividends if you come equipped with the necessary programming tools and theoretical understanding. Consider taking a free course through CourseraOpens a new window , DataquestOpens a new window , or Grow with GoogleOpens a new window so you can test yourself, obtain learning recommendations, and complete courses to augment your data analysis skills and turbo-charge your career.