Data Analytic tools (group 16 Aiml sec 2)CAT3
Data Analysis Tools- What are they?
Data analysis is working with data to organize it logically, explain it, make it presentable, and draw a conclusion. It helps gain valuable insights from data to make reasonable decisions. Identifying patterns is one of the key purposes of statistical data analysis. For instance, it can be helpful in the retail industry to find patterns in unstructured and semi-structured data to help make more effective decisions to improve the customer experience.
why we need data analytic tools?!
Quickly and accurately analyze vast amounts of data from several sources in various formats.
Faster, more accurate decision-making for more effective strategizing might benefit and improve the supply chain, logistics, and other tactical decision-making industries.
A deeper understanding of user needs, preferences, and sentiments could improve strategic procedures and data for product development.
Extract significant insights hiding within large datasets to impact business decisions.
Rapidly reduce risks by planning for unforeseen circumstances and potential challenges.
Facilitate the prompt delivery of goods and services that meet and surpass customer expectations.
Encourage the development of innovative business plans, products, and services.
Some of most used data analysis tool
1. Python
With a popularity share of over 28 percent and a large community of over 10.1 million users, Python programming language is one of the fastest growing and most popular data analysis tools. Python’s wide range of libraries and applications make it an essential tool for every data analyst. Due to its large active community, Python has a nearly infinite range of resource libraries suitable for various data analytics tasks. Among these are tools for general data manipulation like Pandas and specialized frameworks.Python's three most common applications for data analysis include data mining, data processing, modeling, and visualization. The NumPy library is excellent for data processing and facilitating heavily computational operations. The two most popular Python libraries for data visualization are Matplotlib and Seaborn
With a popularity share of over 28 percent and a large community of over 10.1 million users, Python programming language is one of the fastest growing and most popular data analysis tools. Python’s wide range of libraries and applications make it an essential tool for every data analyst. Due to its large active community, Python has a nearly infinite range of resource libraries suitable for various data analytics tasks. Among these are tools for general data manipulation like Pandas and specialized frameworks.Python's three most common applications for data analysis include data mining, data processing, modeling, and visualization. The NumPy library is excellent for data processing and facilitating heavily computational operations. The two most popular Python libraries for data visualization are Matplotlib and Seaborn
Pros of Python
Extensive community support- Python has been around for a while and brings together many Python developers. It also provides more than 90,000 repositories on GitHub.
Supports machine learning- The primary language for machine learning is Python. Data scientists and analysts widely use Python to build predictive and simulative models that discover new insights in their data.
Easy to understand- Due to its simple syntax and readability, Python is one of the easiest languages to learn. It just needs minimal code, making it simple to grasp the language.
Extensive community support- Python has been around for a while and brings together many Python developers. It also provides more than 90,000 repositories on GitHub.
Supports machine learning- The primary language for machine learning is Python. Data scientists and analysts widely use Python to build predictive and simulative models that discover new insights in their data.
Easy to understand- Due to its simple syntax and readability, Python is one of the easiest languages to learn. It just needs minimal code, making it simple to grasp the language.
Cons of Python
Dynamic Typing- Python is a general-purpose language that can be useful for data analysis and various other tasks, including software and web development.
Excessive memory usage- Python receives a lot of criticism for its excessive resource consumption. When processing massive datasets, a data analyst must put extra effort into identifying the causes of the additional memory requirements and improving memory management.
Dynamic Typing- Python is a general-purpose language that can be useful for data analysis and various other tasks, including software and web development.
Excessive memory usage- Python receives a lot of criticism for its excessive resource consumption. When processing massive datasets, a data analyst must put extra effort into identifying the causes of the additional memory requirements and improving memory management.
2. Tableau
One of the best data analytics tools, Tableau, enables you to build interactive dashboards and visualizations without requiring deep programming knowledge. Tableau is a top-rated data analysis tool due to the Tableau Community comprising over one million members, more than 500 user groups worldwide, and active Community Forums. Multinational companies like Citibank, Deloitte, Skype, and Audi leverage Tableau to visualize their data and generate analytical insights.Tableau’s ability to work with a live dataset and spend more time on data analysis processes than data mining makes it the right data analytics tool for data scientists and analysts.
Pros of Tableau
Offers powerful analytics for everyone- Regardless of the skill level of business users, Tableau can meet their demands with role-based licensing. The innovations in augmented analytics enable anyone from data scientists to business users to discover insights more quickly.
Easy to learn- Tableau is based on scientific research to make analysis processes quicker, simpler, and more understandable. Its products are interesting, engaging, and simple to learn since data is analyzed quickly and iteratively with instant feedback.
Provides high-level data security- Tableau equips you with the enterprise-grade security and governance mechanisms to keep data in the right hands, especially when scaling analytics throughout your organization.
Cons of Tableau
No Automated Report Refreshing- The ability to automatically update your reports with the help of a schedule is unavailable. Therefore, updating the data at the back end requires some manual effort.
Lack of Version Control- You cannot return to Tableau's earlier data versions once the dashboards and reports have been published on the server. There is no option to return and retrieve the lost file.
Requires knowledge of SQL- You need to have SQL knowledge for performing data analysis on rich and complex datasets from multiple data sources.
4. Excel
Pros of Excel
Excel is the best option for arranging huge data sets into structured data tables. Such data organization makes data processing and analysis simpler and more efficient for data analysts.
Excel is accessible online and offline on all major operating systems, including Windows, Mac, Android, and iOS.
Accounting, mathematics, statistics, logic, or any quantitative data calculations are simpler with the help of various built-in functions in Excel.
Cons of Excel
Excel is not scalable. Excel workbooks become static, slow, and inefficient when doing calculations on hundreds of rows.
The lack of support for quick autofill options might be annoying for someone without extensive knowledge.
Calculation errors are common if you aren't diligent enough. There are numerous instances of misplacing decimals or percentages, misinterpreted data types, and mixed date or currency formats.
4. SQL
SQL, or Structured Query Language, has been in use since 1974. Its ease-of-use results from the language's simplicity and the ease of retrieving data from a database. It is the fourth-most used programming language worldwide (right after Python). Data scientists and analysts depend heavily on SQL to retrieve data from the source. SQL allows them to retrieve data from the database, modify it, deal with null values, etc. SQL has been adapted into various customized tools, each with its purposes, such as MySQL, Microsoft Access, and PostgreSQL, to create and interact with databases quickly.
Pros of SQL
Open-source Tool- Being an open-source programming language, Open-Source Structured Query Language is ideal for making relational database management systems (RDBMS).
Exceptional Performance- SQL provides high-performance programming support for database systems with large workloads, high transactional rates, and high consumption. Programming in Structured Query Language offers numerous options for more analytical data description.
Rapid query processing- SQL offers faster and faster query processing, which is an advantage. A significant volume of data can be efficiently and quickly retrieved. Additionally, one of the key benefits of SQL is the speed with which one executes operations like data manipulation, deletion, and insertion.
Cons of SQL
Although SQL supports stored queries (such as VIEW and STORED PROCEDURE), it lacks flexibility. It cannot exhibit all of a programming language's capabilities because it is embedded within an RDBMS and is also not secure.
Given the limitations of SQL's data visualization capabilities, it is often necessary to transfer data to another platform for additional analysis and visualization.
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