The emergence of big data has triggered a growing need for skilled professionals who can mine and interpret massive datasets to help businesses prepare strategy. As the finance, retail and e-commerce sectors are now looking for a large army of data scientists, it’s time to add the best tools and platforms for Data Analysis to your resume before the placement drive begins in your college.
What are Data Analysis tools
Data Analysis tools enable businesses to analyse large, disparate and proliferating sets of data to gain a significant competitive advantage in the market. By handling both structured and unstructured data, these tools enable data insights ranging from predictive analysis to business intelligence, helping the top management to take strategic decisions involving different consumer groups and preferences.
Top 10 Data Analytics tools to beef up your resume:
R programming uses a graphical user interface for developing computer programs. This popular open-source language is used for statistical analysis, data visualisation and data mining. R is one of the most highly rated tools as it supports analytical model techniques like classical statistical tests, linear and non-linear modelling and time-series analysis. Extension plugins like R studio and R Excel are also available.
Python is also an open-source language that runs smoothly on all operating systems. It is a preferred language for beginners as the syntax is easy to understand. It has a vast library that helps users perform simple tasks to complete data transformation, analytics and visualisation. Apart from this, Python has an active community and plenty of online resources. Like R, Python regularly updates its functionalities for better speed and accuracy.
Tableau is the best platform to handle big data. It helps one prepare, analyse, collaborate and share big data. This platform can handle millions of rows of data without affecting the performance of the dashboard. It natively supports rich time series analysis, giving analysts the scope to run predictive analysis and perform other common time-series tasks. A super powerful tool for seasoned analysts, Tableau is also a good platform for beginners to enter the world of data.
Another popular tool for data analysts, MS Excel helps manipulate real-time collaboration. This tool helps analysts transform data into meaningful forms and provides relevant insights. Its built-in pivot tables and various kinds of formatting tools and functions are an added advantage. It is often used by analysts when they work together with the business team.
This multilanguage open-source processing engine is simple, fast, scalable and unified. It is used to handle large-scale data analysis. Its key features include batch or streaming data and performing exploratory data analysis (EDA) on the petabyte scale without resorting to downsampling. It also supports machine learning algorithms.
This platform by Microsoft helps extract and visualise data easily. The key benefits of this tool are getting self-service analysis at enterprise scale, getting access to smart inbuilt tools like AI capabilities, tight Excel integration and pre-built custom data connectors. This is highly secure — it has security facilities like sensitivity labelling, end-to-end encryption and real-time access monitoring.
Redash helps build dashboards to visualise data and share it with the company. It has a powerful online SQL editor, browse schema, click-to-insert feature and options to create snippets and reuse them. It is a popular open-source project and uses query results as data sources to join different databases. Search results are cached in this lightweight and cost-effective tool, which enables users to automatically schedule updates.
Knime is another open-source analysis platform that supports data integration, processing, visualisation and reporting. This software allows you to do visual programming without coding, model each step of your data analysis and blend data from any source. Its graphical interface is perfect for point-and-click analysis and modelling. From simple statistics to big analysis, Knime is a perfect tool for data scientists and analysts.
SAS is used in exploring large datasets in a visually appealing form. It performs trend analysis, spots unprecedented patterns, visualises trends and predicts outcomes. This tool has features like built-in data access, mashup, filtering and data transformation. It offers high security and comprises a wide range of statistical libraries.
Google Data Studio
Google Data Studio is one of the free Data Analysis tools for dashboarding and data visualisation. It automatically integrates with most Google applications like Google Analytics, Google Ads and Google BigQuery. This popular platform automatically combines data from different sources, analyses and allows the user to make interactive charts and dashboards. This can analyse more than 800 datasets simultaneously.