Modern data analytic tools

Modern data analytic tools

• These days, organizations are realizing the value they get out of big data analytics and hence they are deploying big data tools and processes to bring more efficiency to their work environment. 
• Many big data tools and processes are being utilized by companies these days in the processes of discovering insights and supporting decision-making.
• Data Analytics tools are types of application software that retrieve data from one or more systems and combine it in a repository, such as a data warehouse, to be reviewed and analyzed.
• Most organizations use more than one analytics tool including spreadsheets with statistical functions, statistical software packages, data mining tools, and predictive modelling tools.
• Together, these Data Analytics Tools give the organization a complete overview of the company to provide key insights and understanding of the market/business so smarter decisions may be made.
• Data analytics tools not only report the results of the data but also explain why the results occurred to help identify weaknesses, fix potential problem areas, alert decision-makers to unforeseen events and even forecast future results based on decisions the company might make.

Some of the most popular modern data analytics tools include:

Python: A powerful programming language with extensive libraries for data analysis and machine learning, including NumPy, Pandas, and Scikit-learn.

R: A statistical programming language with a large library of packages for data analysis, visualization, and machine learning.

SQL: A language for managing and querying relational databases, widely used for data warehousing and business intelligence.

Tableau: A data visualization tool that allows users to create interactive dashboards and visualizations from a variety of data sources.

Power BI: A business analytics service by Microsoft that provides interactive data visualization and business intelligence capabilities with an interface simple enough for end-users.

Alteryx: A self-service data analytics tool that allows users to combine, cleanse, and analyze data from various sources.

Databricks: A cloud-based data engineering platform that provides tools for processing and analyzing large-scale data using Apache Spark.

Looker: A business intelligence platform that provides data analytics and visualization tools for a variety of industries.

Google Analytics: A web analytics service by Google that tracks website traffic and provides insights into user behavior and demographics.

Snowflake: A cloud-based data warehousing platform that allows users to store and analyze large-scale data from various sources.

These modern data analytics tools are designed to provide a variety of features and capabilities for different use cases, such as data exploration, data cleaning, machine learning, and predictive analytics.