Data analysis may be defined as a subject that attempts to reveal an aspect of the truth from a collection of facts relevant to a particular class of phenomena. In other words, data analysis is done with a view to deriving insight into some information or events which have been observed at one time or another. The word “analysis” adopted here means the identification and critical examination of the critical elements for arriving at a conclusion.
Qualitative data analysis is a form of research that seeks subjective information, data about an individual’s experience, behavior, and opinions. It is based on the observation and interpretation of human behavior.
Mode
Mode is a Data analytics software aimed at providing Data Scientists an easy and iterative environment. It offers an interactive SQL editor and notebook environment for analysis and visualization, and collaboration tools for novice users. The mode has a unique Helix Data engine that streams and stores Data from external databases to allow swift and interactive analysis. The Data Analysis supports up to ten GB of data in memory.
KNIME
KNIME is the abbreviation for the Konstanz Information Miner and is a free, open-source Data analytics software that supports Data integration, processing, visualization, and reporting. It integrates Machine Learning and Data mining libraries with minimal or no programming requirements. KNIME is excellent for Data Scientists who do not inherently have proficient programming skills and need to incorporate & process Data for building Machine Learning and other statistical models. Its graphical interface facilitates point-and-click analysis and modeling.
Looker
Looker is one of the cloud-based business intelligence and data analytics tools. It automatically generates a Data model to scan Data schemas and connect tables with Data sources. Through an integrated code editor, it allows Data engineers to modify the created models.
RapidMiner
RapidMiner is a Data analytics software that caters to all the technology users’ needs, from integration, cleaning to Data transformation before they run predictive analytics and build statistical models. Nearly all this is done by the users through a simple graphical interface. RapidMiner can also be expanded by using R and Python and various third-party plugins available on the organization’s marketplace.
Oracle Analytics Cloud
Oracle Analytics Cloud is another suite of Cloud-based business intelligence and data analytics tools. It focuses on helping big corporations to transform their legacy systems into a digital cloud platform. Users leverage its wide range of analytical features, from basic visualizations to Machine Learning algorithms for deriving Data insights.
Microsoft Excel
Excel at a glance:
- Type of tool: Spreadsheet software.
- Availability: Commercial.
- Mostly used for: Data wrangling and reporting.
- Pros: Widely-used, with lots of useful functions and plug-ins.
- Cons: Cost, calculation errors, poor at handling big data.
Excel: the world’s best-known spreadsheet software. What’s more, it features calculations and graphing functions that are ideal for data analysis. Whatever your specialism, and no matter what other software you might need, Excel is a staple in the field. Its invaluable built-in features include pivot tables (for sorting or totaling data) and form creation tools. It also has a variety of other functions that streamline data manipulation. For instance, the CONCATENATE function allows you to combine text, numbers, and dates into a single cell. SUMIF lets you create value totals based on variable criteria, and Excel’s search function makes it easy to isolate specific data. It has limitations though. For instance, it runs very slowly with big datasets and tends to approximate large numbers, leading to inaccuracies. Nevertheless, it’s an important and powerful tool, and with many plug-ins available, you can easily bypass Excel’s shortcomings.
Python
Python at a glance:
- Type of tool: Programming language.
- Availability: Open-source, with thousands of free libraries.
- Used for: Everything from data scraping to analysis and reporting.
- Pros: Easy to learn, highly versatile, widely-used.
- Cons: Memory intensive—doesn’t execute as fast as some other languages.
A programming language with a wide range of uses, Python is a must-have for any data analyst. Unlike more complex languages, it focuses on readability, and its general popularity in the tech field means many programmers are already familiar with it. Python is also extremely versatile; it has a huge range of resource libraries suited to a variety of different data analytics tasks. For example, the NumPy and pandas libraries are great for streamlining highly computational tasks, as well as supporting general data manipulation. Libraries like Beautiful Soup and Scrapy are used to scrape data from the web, while Matplotlib is excellent for data visualization and reporting. Python’s main drawback is its speed—it is memory intensive and slower than many languages. In general though, if you’re building software from scratch, Python’s benefits far outweigh its drawbacks.
Microsoft Excel
Microsoft Excel is a platform that will help you get better insights into your data. Being one of the most popular tools for Data Analytics, Microsoft Excel provides the users with features such as sharing workbooks, working on the latest version for real-time collaboration, and adding data to Excel directly from a photo, and so on.
Products
Microsoft Excel offers products in the following three categories:
- For Home
- For Business
- For Enterprises
Few of the versions are available for free for 1 month. All these products have various versions which differ by features and their pricing options.
Almost all organizations use Microsoft Excel on a daily basis to gather meaningful insights from the data. A few of the popular names are McDonald’s, IKEA, Marriot.
The recent advancements vary on the basis of the platform. A few of the recent advancements in the Windows platform are as follows:
- You can get a snapshot of your workbook with Workbook Statistics
- You can give your documents more flair with backgrounds and high-quality stock images absolutely for free
Programming Languages: R & Python
R and Python are the top programming languages used in the Data Analytics field. R is an open-source tool used for Statistics and Analytics whereas Python is a high-level, interpreted language that has an easy syntax and dynamic semantics.
Products
Both R and Python are completely free and you can easily download both of them from their respective official websites.
Companies such as ANZ, Google, Firefox use R, and other multinational companies such as YouTube, Netflix Facebook use Python.
Python and R are developing their features and functionalities to ease the process of Data Analysis with high speed and accuracy. They are coming up with various releases on a frequent basis with their updated features
Power BI
Business intelligence tools, like Microsoft Power BI, are extremely important in the data analysis process because they make it easy for businesses to spot trends, patterns, and insights across large sets of data.
Microsoft Power BI allows users to import data from hundreds of sources, and drag and drop elements, to create real-time dashboards and reports. Equipped with AI, an Excel integration, and pre-built and custom data connectors, you can gain valuable insights and easily share them with the rest of your team.
Pricing options for self-service BI or a premium service for advanced analytics.
Tableau
Tableau is a powerful analytics and data visualization platform that allows you to connect all your data and create compelling reports and interactive dashboards that update in real-time. It’s easy to use, supports large amounts of data, and can be run on-premise or in the cloud.
There’s a free trial available and different plans for individual users and organizations.
Conclusion:
Everyone wants to get their hands on the quantitative data. But the qualitative data is the one that helps us to dig deeper into the real insights. It gives us an opportunity to know about customer/user behavior in non-numeric terms. David Kirkpatrick once said, “Qualitative research does not value quantity; it values quality.”
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