10 Best Data Analytics Tools

 

The growing demand and importance of data analysis in the marketplace has spawned many discoveries around the world. It gets a little tricky to come up with a list of the best data analysis tools as open source tools are more popular, user-friendly, and performance-oriented than the paid version. There are many open source tools out there that don't require much / any coding and are capable of providing better results than the paid versions, for example. - R Programming in Data Mining and Public Tables, Python in Data Visualization. Below is a list of the top 10 data analysis tools, both open source and paid, based on their popularity, training, and performance.

 

1. R Programming

R is the industry's leading analytics tool, widely used for statistics and data modeling. It can easily manipulate your data and represent it in different ways. It outperformed SAS in many ways, such as data volume, performance, and outcome. R compiles and runs on a wide variety of platforms such as UNIX, Windows, and MacOS. It has 11,556 packages and allows you to browse packages by category. R also provides tools to automatically install all packages as per user requirements, which can also be well combined with big data.

 

2. Tableau Public

Tableau Public is free software that connects any data source, be it corporate data warehouse, Microsoft Excel, or web data, and creates data visualizations, maps, dashboards, and more, with real-time updates provided in The Internet. They can also be shared via social networks or with a client. This allows you to download files in different formats. If you want to see the full power of tables, then we must have a very good data source. Tableau's big data capabilities make it important and can be analyzed and visualized better than any other data visualization software on the market.

 

3. Python

Python is an object-oriented scripting language that is easy to read, write, maintain and is a free and open source tool. It was developed by Guido van Rossum in the late 1980s and supports both functional and structured programming techniques.

Python is easy to learn as it is very similar to JavaScript, Ruby, and PHP. Also, Python has some very good machine learning libraries, viz. Scikitlearn, Theano, Tensorflow and Keras. Another important feature of Python is that it can be compiled on any platform such as SQL Server, MongoDB, or JSON. Python can handle text data very well too.

 

4. SAS

Sas is a programming environment and data manipulation language, a leader in analytics, developed by the SAS Institute in 1966 and further developed in the 1980s and 1990s. SAS is easily accessible, manageable, and can analyze data from any source. In 2011, SAS introduced a large suite of customer analysis products and numerous SAS modules for the web, social media, and marketing analytics that are widely used to profile customers and leads. He can also predict their behavior, manage and optimize communications.

 

5. Apache Spark

The UC Berkeley AMP Lab developed Apache in 2009. Apache Spark is a fast, large-scale data engine that runs applications on Hadoop clusters 100 times faster in memory and 10 times faster on disk. Spark is based on data science and its concept makes data analysis easy. Spark is also popular for developing data pipelines and machine learning models.

Spark also includes the MLlib library, which provides a progressive set of machine algorithms for repetitive data analysis techniques such as classification, regression, collaborative filtering, clustering, and more.

 

6. Excel

Excel is a basic, popular and widely used analytical tool in almost all industries. Whether you are an expert in Sas, R, or Tableau, you still have to use Excel. Excel becomes essential when internal customer data analytics is required. It analyzes a complex task that summarizes data with a preview of pivot tables, which helps to filter the data according to customer requirements. Excel has an advanced business intelligence option that helps in modeling capabilities that have pre-built options such as auto-detecting relationships, generating DAX metrics, and grouping by time.

7. RapidMiner

RapidMiner is a powerful integrated data analytics platform developed by the same company that performs predictive analytics and other advanced analytics such as data mining, text analytics, machine learning, and visual analytics without any coding. RapidMiner can be included with any type of data source including Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase, IBM DB2, Ingres, MySQL, IBM SPSS, Dbase, etc. This very powerful tool can generate data based on real data transformation settings, that is, you can control the format and dataset for predictive analysis.

 

8. KNIME

KNIME was developed in January 2004 by a group of software engineers from the University of Constanta. KNIME are leading open source, reporting and integrated analytics tools that enable you to analyze and model data through visual programming with a modular data mining and machine learning pipeline concept that integrates components.

 

9. QlikView

QlikView has many unique features such as patented technology and in-memory data processing that delivers very fast results to end users and stores the data in the report itself. The Data Link is automatically maintained in QlikView and can be compressed to about 10% of its original size. A data link is rendered using colors — corresponding data is assigned a specific color, and unrelated data a different color.

 

10. Splunk

Splunk is a tool that analyzes and discovers data generated by a machine. Spunk extracts all the textual data of the log and provides an easy way to search through it, the user can extract all kinds of data and do all sorts of interesting statistical analyze on it, and it can be displayed in various formats.

SASVBA Institute

SASVBA Delhi offers comprehensive Data Analytics Training in Delhi. Extensive hands-on sessions delivered by the Data Analytics Institute in Delhi include live projects and simulations. Such detailed courses in data analysis have helped our students find jobs in various multinational companies. SASVBA Delhi instructors are corporate subject matter professionals who provide in-depth training in data analysis courses in Delhi. Members who are certified in data analytics have a wide range of employment opportunities in the industry.

 

In addition, we have maintained the flexibility of the data analysis curriculum throughout the Delhi period. From online classes to accelerated learning and one-to-one data analytics training for learners on weekdays and weekends. Our state-of-the-art laboratory is equipped with the latest technology to help students successfully complete their training and certification at the Data Analytics Institute.

Reasons for choosing Us for Data Analytics Course

• Our Data analytics course in Delhi has been designed to meet the demands of today's IT industry.
• We provide the best data analytics training in Delhi with data analytics classes spanning the entire curriculum module. In addition, students take data analysis courses in Delhi with employment assistance.
• Data analytics training in Delhi is scheduled for weekdays and weekends. Also, students can choose an individual program in accordance with the requirements.
• Our team of trainers are industry experts with over a decade of training experience.
• Data Analysis Training in Delhi helps students not only carry out live projects, but also provides job interview preparation classes with job placement assistance.

 

For More Information:

Website: SASVBA

Phone Number: +91 7703 871 499

Location: Delhi, Laxmi Nagar