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- KNIME
Konstanz Information Miner or most commonly known as KNIME is a free and open-source data analytics, reporting, and integration platform built for analytics on a GUI-based workflow. The first version of KNIME Analytics Platform was released in July 2006 with a mission to make data analytics available and affordable to every data scientist in the world.
KNIME provides the following two software:
- KNIME Analytics Platform – This is open-source and used to clean & gather data, make reusable components accessible to everyone, and create Data Science workflows.
- KNIME Server – This is a platform used by enterprises for the deployment of Data Science workflows, team collaboration, management, and automation.
Companies such as Siemens, Novartis, Deutsche Telekom, Continental use KNime to make sense of their data and leverage meaningful insights.
- Pros
- Access to all the new developments in data science, machine learning, AI
- Blend data from any source databases and data warehouses to integrate data
- Make data science available on all operating systems - not just on Windows
- Builds end to end data science workflows
- Cons
- Does not offer a free trial
- Less suitable option for large complex workflows.
- Partitioning ability is limited for dataset
- RapidMiner
The development began in 2001 with the name YALE and in 2007, the name of the software was changed to RapidMiner. It is a powerful integrated data science platform developed by the same company that performs predictive analysis and other advanced analytics like data mining, text analytics, machine learning, and visual analytics without any programming.
RapidMiner can incorporate with any data source types, including Access, Excel, Microsoft SQL, Tera data, etc. RapidMiner provides all the technology users need to integrate, clean, and transform data before they run predictive analytics and statistical models. Users can perform nearly all of this through a simple graphical interface.
RapidMiner can also be extended using R and Python scripts, and numerous third-party plugins are available through the company’s marketplace. However, the product is heavily optimized for its graphical interface so that analysts can prepare data and run models on their own.
Companies such as BMW, Hewlett Packard Enterprise, EZCater, Sanofi use RapidMiner for their Data Processing and Machine Learning models.
- Pros
- Available for free for educational use
- Over 1500 methods for data integration, transformation, analysis, and modeling
- Robust features and user-friendly interface
- Provides analytics based on real-life data transformation settings so users have more control over data
- Cons
- More of a backstage tool than a data analytics tool for analyst
- Takes a lot of CPU processing power, even for a small process on a small data set
- Limited partitioning abilities for data sets to training and testing data sets
- R and Python
R and Python are open-source languages and are used extensively in data sciences.
R Language is used for machine learning algorithms, linear regression, time series, statistical inference, etc. It was designed by Ross Ihaka and Robert Gentleman in 1993. It has a steep learning curve and needs some amount of working knowledge of coding. However, it is a great language when it comes to syntax and consistency.
Python is a widely-used general-purpose, high-level programming language. It was created by Guido van Rossum in 1991 and further developed by the Python Software Foundation. It is a powerful Data Analysis tool and has a great set of friendly libraries for any aspect of scientific computing. Its library Pandas was built over NumPy, which is one of the earliest libraries in Python for data science.
Companies like Facebook, Google, Twitter & Uber generally use R for behavior analysis, data visualization, semantic clustering, advertising effectiveness, and economic forecasting. Top Companies that use Python for data analysis are Spotify, Netflix, NASA, Google and CERN, and many more.
- Pros
- R is considered the best tool for data visualization
- R is great for statistical analysis, machine learning, and data science
- R is good for ad-hoc analysis and exploring datasets
- Data cleaning is easier with Python by adding new functions and layers to take apart your data
- Data scientists favor Python to get the output as desired with a defined number of steps
- Python is evolving with time, leading to more open-source code and solutions
- Cons
- R is difficult for users with no programming knowledge
- If the code is written poorly, deriving solutions with R can be slow
- Python requires rigorous testing as the errors show up in runtime
- Python is still considered weak on mobile computing platforms
- Power BI
Power BI is yet another powerful business analytics solution by Microsoft. It was originally conceived by Thierry D’Hers and Amir Netz. Initially named Project Crescent, it was later unveiled by Microsoft in 2013 as Power BI for Office 365. was first released to the general public in 2015.
Power BI comes in three versions – Desktop, Pro, and Premium. The desktop version is free for users; however, Pro and Premium are priced versions. It allows you to bring your data to life with live dashboards and reports. You can visualize your data connected to many data sources and share the outcomes across your organization.
Power BI integrates with other tools, including Microsoft Excel, so you can get up to speed quickly and work seamlessly with your existing solutions. The top companies using Power BI are Nestle, Tenneco, Ecolab, and more.
- Pros
- Affordable and relatively inexpensive - desktop version is free of cost
- Offers a wide range of custom visualizations
- Use as a data analysis tool and import data from a wide range of data sources
- Easy drag-and-drop functionality to add different visualizations in a report
- Prompt upgrades from Microsoft every month
- Cons
- Cannot handle complex relationships between tables
- The user interface is found crowded and bulky by the users
- Rigid DAX formulas are not the easiest language to work with
- The limit of ingesting data at a time is approximately 2 GBs
- Apache Spark
Apache Spark started as a research project at the UC Berkeley AMPLab in 2009 and was open-sourced in early 2010. It is 100% open-source, hosted at the vendor-independent Apache Software Foundation and a wide range of developers contribute to its development.
Spark Is an integrated analytics engine for Big Data processing designed for developers, researchers, and data scientists. It is a high-performance tool and works well for batch and streaming data. Learning Spark is easy, and you can use it interactively from the Scala, Python, R, and SQL shells too.
Spark can run on any platform such as Hadoop, Apache Mesos, standalone, or in the cloud. It can access diverse data sources. Uber, Slack, Shopify, and many other companies use Apache Spark for data analytics.
- Pros
- Speed - it currently holds the world record for large-scale on-disk sorting
- Easy-to-use APIs for operating on large datasets
- Massive open-source community
- Standard libraries increase developer productivity to create complex workflows
- Handle several analytics challenges with well-built libraries
- Cons
- Doesn’t have any automatic code optimization process
- Depends on other platforms for file management system
- Not capable of handling more users' concurrency
- Unsupportive of record-based window criteria - offers time-based window criteria
What Is The Data Analysis Process and Relevant Techniques?
- Cluster analysis
Clustering can be explained as grouping the elements of a data set based upon their similar attributes where consequently each group is different from the other. Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.
Cluster analysis is an exploratory technique that seeks to identify structures within a dataset. The goal of cluster analysis is to sort different data points into groups or clusters that are internally homogeneous and externally heterogeneous. This means that data points within a cluster are similar to each other and dissimilar to data points in another cluster. Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other algorithms.
For instance, if we look at it from a business perspective, in a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service. But let’s face it, with a large customer base, it is impossible to do that. That is where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.
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- Cohort analysis
A cohort is a group of people who share a common characteristic during a given period. For example, students who enrolled at university in 2020 may be referred to as the 2020 cohort. Customers who purchased something from your online store via the app in December may also be considered a cohort.
The cohort analysis method uses historical data to examine and compare the characteristics of different segments. By using this methodology, it’s possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group. As a result, it helps you understand the impact of your campaigns on specific groups of customers.
To understand, imagine you send an email campaign encouraging customers to sign up to your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.
- Regression analysis
Regression analysis is used to estimate the relationship between a set of variables. It uses historical data to understand how a dependent variable’s value is affected when one or more independent variables change or stay the same. By understanding each variable’s relationship and how they developed in the past, you can anticipate possible outcomes and make better decisions in the future.
There are many different types of regression analysis, and the model you use depends on the type of data you have for the dependent variable. For instance, you work for an e-commerce company and you want to examine the relationship between (a) how much money is spent on social media marketing and (b) sales revenue. In this case, sales revenue is your dependent variable. It is the factor you are most interested in predicting and boosting. Social media spend is your independent variable; you want to determine if it has an impact on sales and, ultimately, whether it’s worth increasing, decreasing, or keeping the same.
Using regression analysis, you’d be able to see if there’s a relationship between the two variables. A positive correlation would imply that the more you spend on social media marketing, the more sales revenue you make. No correlation at all might suggest that social media marketing has no bearing on your sales. Understanding the relationship between these two variables would help you to make informed decisions about the social media budget going forward.
- Time series analysis
As its name suggests, the time series analysis is used to analyze a set of data points collected over a specified period. It also allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the result.
In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a duration and forecast different future events.
When conducting time series analysis, the main patterns you’ll be looking out for in your data are:
- Trends: Stable, linear increases or decreases over an extended period
- Seasonality: Predictable fluctuations in the data due to seasonal factors over a short time
- Cyclic patterns: Unpredictable cycles where the data fluctuates
A great example that puts time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise in a specific period. You might see a peak in swimwear sales in summer around the same time every year. These insights allow you to predict demand and prepare production accordingly.
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- Sentiment analysis
When you think of data, your mind probably automatically goes to numbers and spreadsheets. Many companies overlook the value of qualitative data, but in reality, there are untold insights to be gained from what people write and say about you. So how do you go about analyzing textual data?
One highly useful qualitative technique is sentiment analysis. It belongs to the broader category of text analysis. With sentiment analysis, the goal is to interpret and classify the emotions conveyed within textual data. From a business perspective, this allows you to ascertain how your customers feel about various aspects of your brand, product, or service.
Each with a slightly different focus, there are several different types of sentiment analysis models:
- Fine-grained sentiment analysis - focuses on opinion polarity (positive, neutral, or negative) in-depth
- Emotion detection - uses complex machine learning algorithms to pick out various emotions from your textual data
- Aspect-based sentiment analysis - identifies what specific aspects the emotions or opinions relate to, such as a certain product feature or a new ad campaign
In a nutshell, sentiment analysis uses various Natural Language Processing (NLP) systems and algorithms that are trained to associate certain inputs with specific outputs. For example, the input “annoying” would be recognized and tagged as “negative”. Sentiment analysis is crucial to understanding how your customers feel about you and your products, for identifying areas for improvement, and even for averting PR disasters in real-time.
What is Data Analysis: Methods, Process and Types Explained
Analyzing the data progressively helps you stay organized. Here is a rundown of the 5 essential steps of data analysis:
- Identify - The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers?
- Collect - Here you start collecting the needed data. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, focus groups, among others
- Clean - Not all the data you collect will be useful, so you need to clean it before proceeding for analysis. Remove duplicate records or formatting errors
- Analyze - At this stage, you find trends, correlations, variations, and patterns that will help you answer the questions you formulated at the identify stage
- Interprete - The researcher comes up with courses of action based on the findings. You may also find some limitations here and work on them