The Ultimate Set of Tools You Need To Ace Data Analysis

For IT professionals looking for a specialization, data analytics is among the top career options. ReportsOpens a new window suggest that nearly 70% of business leaders will prefer job applicants who bring data skills in 2021. Another reportOpens a new window called data science one of the “sexiest jobs of the 21st century,” with over 3 million job openings worldwide by the end of this year. In other words, it is a good investment to start brushing up on your data science skills as you propel your career forwards in 2021, assembling the perfect toolkit to conduct data analysis and trial your new capabilities. 

Ideally, your data analysis toolkit 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’s unique security, governance, and cost limits. 

Learn More: Are BI Tools Delivering on Their Promise? 

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. A surveyOpens a new window found that Python is the most popular language among data professionals, recommended by 3 in 4 respondents. 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. 
  • 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
  • R Markdown – used to convert data analysis into high-quality reports 
  • Shiny – lets you build web apps using R for interactive data exploration 

Importantly, the survey that named Python as the #1 language for data pros placed R at #3. 

3. SQL

Between Python, which is no. 1, and R, which is no. 3, you have one of the oldest querying languages associated with data science used by 44% of data 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 : Dataquest

As this image suggests, the demand for SQL skills in data jobs is actually ahead of Python and R in 2021, making it an important addition in your toolkit.

Learn More: Looker Exec on the Integration of Enterprise-Ready BI Platform With Google Cloud 

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 14%Opens a new window through 2026, 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 a good option, as it is entirely open-source, requires very basic programming skills, 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. TIBCO Spotfire

TIBCO, a globally recognized software integration and analytics company, acquired BI platform Spotfire in 2007. It gives you a one-stop tool for data wrangling, exploration, and visualization, aided by a natural language interface for minimal complexities.  

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

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. RapidMiner

For machine learning,  deep learning, text mining, and predictive analytics, RapidMiner is an extremely popular data science platform. It was recognized by Gartner and Forrester in 2020, owing to its ease of use for professional data scientists and data-literate business users alike. RapidMiner Studio offers a GUI-based predictive analytics engine powered by an AI hub. 

Learn More: Data Warehouses: Why a Single Source of Truth Is Necessary for Customer Analysis 

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. A good place to get started is Workera’s data-AI skills platformOpens a new window that lets you test yourself, obtain learning recommendations, and undertake courses that can augment your data analysis toolkit and turbo-charge your career in 2021 and beyond.

Is there a data analysis tool you want to add to this list of recommendations? Comment below or let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you!