With each new day, we move closer to a data-focused era where the importance of data analytics can’t be understated. Data analytics has evolved into the foundation on which organisations base their approaches, make well-informed choices, and create development. Organisations are constantly searching for solutions that can comprehend the complex structure of modern data and turn it into meaningful insights.
Following this advancement, a career in data science is booming due to the continuous increase in data production and the unquenchable desire for insights to drive decision-making across industries. As the data analytics market is set to reach $650 billion by 2029, knowing your way through the right analytics tools can bring you greater efficiency and impact.
Learn about the top 20 data analytics tools of 2023 to uncover the tools that will influence the development of data analysis and are extremely beneficial for anyone interested in pursuing a data analytics course.
Traditional Data Analytics Tools
1. SAS
SAS, or Statistical Analytical System, enables users to access data in any form (SAS tables or Excel workbooks). Aside from that, it provides a cloud-based platform for business analytics named SAS Viya.
Features of SAS:
- SAS BI is easily accessible via Microsoft Office, allowing analysts to produce and disseminate reports and graphics as needed.
- SAS can forecast behaviours as well as manage and optimise connections.
2. R
R is a powerful data analytics tool that can be used for data mining, modelling, and intensive statistical computation. This open-source, free programme is used to analyse data and develop software and apps to analyse statistical data in real time.
Features of R:
- Versatility to be used in a variety of fields, such as data visualisation and data processing
- Built-in graphics for stunning visuals
- Provides a supportive environment to answer inquiries and solve problems
3. Python
Python is a flexible programming language with several data analytics tools and structures. Due to its versatility and variety of uses, it is becoming increasingly common for data analytics.
Features of Python:
- Large packages and libraries that improve functionality
- Open source and commonly used so that faults may be easily corrected
4. Microsoft Excel
Excel serves as one of the most frequently used spreadsheet programmes for data analysis. It provides users with the ability to execute basic data manipulation, visualisation, and statistical evaluation.
Features of Excel:
- It uses pivot charts to sift complex data and extract insights as needed by the client.
- It features powerful analytics capabilities that aid in modelling.
5. SPSS (Statistical Package for the Social Sciences)
SPSS is a data analysis application employed in social sciences and market analysis. It offers a straightforward data processing and tracking interface.
Features of SPSS:
- It offers data cleansing tools for detecting and dealing with values that are not present, duplicated cases, and anomalies.
- SPSS users can generate new variables, modify existing variables, and compute fresh ones using formulas.
6. Minitab
Minitab is an analytical tool that makes managing data analysis, statistical modelling, and quality improvement projects easier.
Features of Minitab:
- Minitab is frequently used in Six Sigma along with quality enhancement initiatives.
- Minitab provides a full set of DOE tools for trial planning, execution, and analysis.
7. IBM SPSS Statistics
SPSS Statistics is a robust statistical software programme created by IBM for data analysis, prediction, and monitoring.
Features of IBM SPSS Statistics:
- Import data from a number of sources, such as databases, Excel sheets, and text documents, with ease.
- Data cleaning entails identifying and dealing with missing data, anomalies, and duplicate occurrences.
8. JMP
JMP is the SAS Institute’s data visualisation and analytical software. It is well-known for its innovative and dynamic analysis of data functionalities.
Features of JMP:
- JMP enables users to successfully clean and preprocess information.
- JMP is an extremely dynamic and user-friendly interface for constructing various visualisations.
9. Stata
Stata is a data handling and statistical evaluation software package. It is commonly used in scholastic and sociological research.
Features of Stata:
- It offers descriptive statistics in abundance, including medians, variances, frequencies, and others.
10. RapidMiner
RapidMiner is an effective data science platform extending a wide array of data preparation, assessment and modelling capabilities to analysts.
Feature of RapidMiner:
- Provides a visual workflow to simplify model deployment
- Enables versatile data integration capabilities, allowing users to work with diverse data sources
Advanced Data Analytics Tools
1. Tableau
Tableau operates as a simple data visualisation tool which enables the creation of interactive graphics and displays simply through a dragging and dropping interface. Organisations may use this to quickly create graphics that add some context and significance to raw data, making it much easier to interpret.
Features of Tableau:
- You can locate buried data using the Data Discovery function.
- You may employ many data sources such as SQL Server and Oracle.
2. Power BI
Power BI is the Microsoft Business Analytics tool. It improves interactive visualising and business intelligence skills. All of this is accomplished while maintaining an easy-to-use interface.
Features of Power BI:
- Excellent compatibility with Microsoft products
- The ability to make visually appealing paginated documents
3. QlikSense
QilkSense is a business optimisation tool that helps with visualising data and evaluation. QuilkSense covers a wide range of data sources, including databases, Excel sheets, and cloud-based services.
Features of QlikSense:
- Interactive data visualisation tools that are both appealing and dynamic
- Qlik Insight Bot provides interactive AI-powered analytics
4. Qubole
It serves as an open-source big data application that aids in retrieving data in the value chain through ad-hoc evaluation in machine learning. Qubole is a lake of information platform that provides end-to-end service while reducing the time and effort necessary to move data streams.
Features of Qubole:
- It monitors users’ systems and provides real-time insights.
- Qubole provides predictive analysis so businesses can take appropriate measures to pursue more transactions.
5. Mongo DB
It launched in 2010 as a free, open-source software and a document-oriented (NoSQL) database designed to store large amounts of data. It stores data in sets and papers. Its documents are made up of keys and values, regarded as the core unit of Mongo DB.
Features of Mongo DB:
- A user can effortlessly store documents with the aid of Mongo without creating any disruption in the stack.
- It’s capable of writing/reading information to the main system and can be fetched back for backup.
6. KNIME
KNIME is an interface for data analytics. It has an easy-to-use user interface. KNIME is designed for scalability and expansion via an established API plugin.
Features of KNIME:
- Support for advanced analytics technologies such as artificial intelligence, data mining, and big data handling
- Tools for creating high-quality visualisations are provided
7. Hadoop
Hadoop is an APACHE integrated processing and storage platform. It is a platform for storing and processing data with the MapReduce method.
Features of Hadoop:
- It exhibits fault tolerance by continuing to function even in the event of a unit failure.
- Highly versatile, having the flexibility to distribute data across different platforms.
8. Spark
APACHE Spark is yet another structure for processing data and doing multiple activities on a huge scale. It is also used to analyse data across numerous computers using spreading techniques.
Features of APACHE Spark:
- It enables users to run in the language of their choice.
- Spark Streaming is capable of handling real-time streaming.
9. TensorFlow
TensorFlow is a set of functions for machine learning. This library was created by Google and accessed by many firms wishing to add machine-learning skills to their data analytics process.
Features of TensorFlow:
- Provides a huge community for crisis and issue resolution
- It includes a built-in visualisation tool to see whether the model is functioning.
10. Cassandra
APACHE Cassandra is a NoSQL distributed database which is amongst the most prominent data analytics tools. And, many tech organisations have complimented it for its great scalability and accessibility.
Features of APACHE Cassandra:
- It accepts various data types, including unorganised, structured, and semi-structured information, and lets users customise it to their specific needs.
- It is simple to disseminate data by replicating it across various data centres.
Conclusion
The data analytics landscape in 2023 is primed for tremendous development and innovation. The data analytics sector faces unprecedented potential and challenges due to the rapid evolution of technology. With a data analytics certification course as a guide, you’ll be well-equipped to traverse the ever-changing environment of data analytics and define a future driven by insights, intelligence, and creativity.
The postgraduate programme in data science and analytics at Imarticus can help you realise your career goals. Benefit from project-based learning, hands-on instruction in data analytics technologies, and industry-recognized certifications.
