Top Seven (7) Tools Data Analysts Will Need In 2021
Which Data Analytics tools will smart analysts require in 2021? Here are our picks for the top 7 data analytics tools.
The increasing demand and importance of data analytics have generated multiple opportunities globally. It has become tough to shortlist the top data analytics tools as the open-source tools have become more popular, user-friendly and performance-oriented than the paid version. Many open-source tools don’t require much coding and manage to deliver similar results to paid versions. But to perform data analysis at the highest level possible, data analysts should use tools and software to ensure accurate results. Here are the top 7 data analytics tools and their key features.
1. R-Programming
R is one of the most powerful analytics tools in the industry and widely used for statistics and data modelling. It can easily manipulate the data and present it in multiple ways. It is usually referred to as a language designed by statisticians. It was founded and developed in 1995 and is one of the most used tools for statistical analysis and data science. It is an open-source tool that runs on various platforms like Windows and macOS.
RStudio is one of the most popular integrated development environments. When it comes to data cleaning, data reduction and data analysis report, R’s capability is exceptional. It can cover both general and academic data analysis. It is designed in such a way — where an ecosystem contains more than 10,000 packages and extensions. You can explore by categories and perform any statistical analysis like regression, conjoint, factor clustering, etc.
Key Features:
- An ecosystem of 10,000+ packages and extensions of various types of data analysis
- Statistical analysis, modelling and hypothesis testing
- Active and communicate community of researchers, statisticians and scientists
Source: https://blog.rstudio.com/
2. Microsoft Excel
Excel or Spreadsheets — they are one of the most traditional forms of data analysis. Immensely popular in any and every industry, business or organisation. There are significantly fewer chances that you have not created at least one spreadsheet to analyse your data. It is used by people who don’t have high technical abilities to code themselves. Spreadsheets can be used for reasonably easy analysis that doesn’t require considerable training, complex and large volumes of data and databases to manage.
A wide range of functionalities accompanies Excel, from arranging to manipulating, calculating and evaluating quantitative data to building complex equations, using pivot tables, conditional formatting, adding multiple rows, and creating charts and graphs.
Key Features:
- Part of the Microsoft Office Family, but still compatible with other Microsoft applications
- Pivot tales and building complex equations through designated rows and columns
- Effective for smaller analysis processes through workbooks and quick sharing
Source: https://www.microsoft.com/en-in/
3. Tableau
Tableau Public is free software that connects any data source — it can be Corporate Data Warehouse, Microsoft Excel or web-based data. It creates data visualisations, maps and dashboards. In real-time, updates presented on the web can also be shared through social media or with the client. It also allows you to download the file in different formats. Tableau’s Big Data capabilities make its powers more exceptional.
Key Features:
- It allows you to share data from various data sources such as on-premise securely.
- It comes with a dull-proof security system based on authentication and permission systems for data connections and user access
- Easy to create trend lines and forecast with Tableau’s robust back-end
Source: https://www.tableau.com/products
4. RapidMiner
RapidMiner is a tool used by data experts and data scientists worldwide to prepare data, utilise ML-powered tech and model operations in more than 40,000 organisations that heavily rely on analytics in their operations. RapidMiner is built on five core platforms and three automated data science products that help design and deploy analytics processes by harmonising the entire data science cycle. Like descriptive statistics and visualisation, its data exploration features will allow you to get the required information. At the same time, predictive analytics will help you with churn prevention, risk modelling, text mining and customer segmentation.
Key Features:
- Comprehensive data science and ML-powered platforms with more than 1500 algorithms
- With RapidMiner, it is possible to integrate Python and R-programming as well as support for database connections like Oracle
- Features like descriptive and prescriptive analytics for Advanced analytics
Source: https://rapidminer.com/
5. KNIME
KNIME is an open-source, reporting and integrating analytics tool that will allow you to analyse and model data through visual programming. It will incorporate various components for data mining and machine learning via its modular data pipelining concept. It will enable you to build machine learning models. Also, you can use advanced algorithms like deep learning, tree-based methods and logistic regression. Software provided by KNIME includes the KNIME Analytics platform, KNIME Server, KNIME Extensions and KNIME Integrations.
Key Features:
- It automates workflow execution and supports team-based collaboration
- It guides you in building workflows
- It is a multi-threaded data processing
Source: https://www.knime.com/knime-analytics-platform
6. Highcharts
Highcharts is a multi-platform library designed for developers looking to add interactive charts to web and mobile projects. Its library works with a back-end database, and data can be given in CSV, JSON or updated live. They feature intelligent responsiveness, which fits the desired chart into the dimensions of the specific container. Highcharts support line, spline, area, column, bar, pie, scatter graphs and many others, helping developers in their online-based projects. It allows you to download and make your edits to the source code.
Key Features:
- An interactive JavaScript engine for charts used in web and mobile project
- Designed mainly for a technical-based (developers)
- WebGL-powered boost module to render millions of data points directly in the browser
Source: https://www.highcharts.com/
7. Apache Spark
To date, Apache Spark has expanded across industries and companies like Netflix, Yahoo and eBay, — which have already deployed Spark, processed petabytes of data and proved that Apache is the go-to solution for large-scale data management. Its ecosystem consists of SparkSQL, streaming, machine learning, graph computation and core Java, Scala and Python API to ease the development. Spark comes with support for SQL queries, MLib for machine learning and GraphX for streaming data which can be combined to create an additional, complex analytical workflow.
Key Features:
- High-performing tool in the extensive data processing
- A massive ecosystem of data frames, streaming, machine learning and graph computation
- A vast collection of over 100 operators for operating large scale data
Source: https://github.com/
Originally published on: Datatechvibe
