Machine Learning Assignment: How Digital Technology Is Helpful For Business Development?

Question

Task: What are the various digital analysis tools helpful in business growth and development?

Answer

Introduction
Do you own a business or are planning to set up a business in future? Well, be informed through this machine learning assignment that Artificial intelligence is capable of helping you make a well-versed decision through analysis of historical data based on which future trends can be determined to make understandable reports (Tableau, 2020). Raw data is generated during every operation but few stakeholders would be able to read the data in its raw state. This is due to raw data being stored in numerical format and the brain not being able to organize the data in sequence to extract logic from the data. With the advances in computer and digital technology, raw data can today be filtered, sorted and analysed to reveal trends hidden in the data which can then be converted to graphical representations helping more people understand the data trends.

Data Analysis Tools
The advances in digital technology and computers have led to development of powerful data analysis software. The software is capable of analysing and converting or filtering data and producing graphical representations. Multiple data analysis tools range from the simple Microsoft Excel to more complex software like Tableau and R, each of which has a unique feature or characteristic, thus the data analysis’s tool choice will depend on certain parameters such as the data set format, location and most importantly the size of the data set (Chou, 2019). Software like Rapid Miner and Ms Excel are powerful but have a limited number of observations that can be analysed while others like Python and R have huge data set capacity, and therefore opted for by data analysts. A brief description of the data sets would be provided to better understand the features and criteria based on which data analysis software should be selected.

Small and Medium Data Set Analysis
Data sets fall into two main categories namely small & medium and large data sets. Software is designed to analyse a limited number of entries making it important for the data to filters and number of Raw and line variable entries considered to determine the most suitable tool. For small and medium-sized data sets, Excel and Rapid miner are recommended as they are commonly available and used which makes them among the most popular data analysis tools (Maheswari, 2018).

Microsoft Excel
Microsoft Windows is the most common computer operating system globally but Microsoft has also developed powerful office package software which gives users access to a wide verity of tools. A very important tool used by many business professionals with relation to machine learning and statistics is the MS Excel. This software is designed to perform a wide verity of numeric calculations and analysis and commonly used by people as it is easy to use. The tool also offers a wide verity of functionalities that can be used to analyse data and produce high-quality visuals. While MS Excel is the most common data analysis tool, it has a range limit thus only suitable for small and medium-sized data sets (WallStreetMojo, n.d.).

Rapid Miner
Rapid Miner mentioned in this machine learning assignment is popular data analysis software mainly due to it being very simple to use. Rapid miner has two variants one free (range limited) and the other the Paid version. The tool comes with a readily built-in the algorithm which filter and analyses data thus making the tool user-friendly for people who may lack the experience and knowledge required to mine important information and trends from Raw data. Rapid miner offers high-quality results and will produce a script file which can be shared among rapid miner users to gain access to the algorithms used to prepare the solution. Rapid miner is recommended for the novice who has no background experience of Data mining and more comfortable simply uploading data and clicking filters to produce data visualizations (RapidMiner, n.d.).

Big Dataset Analysis
Big Data analysis requires the use of special data analysis tools which are specially designed to handle big data sets. Big Data sets comprise of tens of thousands or even millions of individual entries, variables making them too large for some programs to run. While MS Word is capable of handling several thousand entries, when fed with over 50,000 entries the software begins hanging and unable to load the data correctly. For these large data sets, special data analysis tools such as Tableau, R and Jupiter Python are used. Each has similar functionalities but produces different visuals making each unique and often compared to determine which software produces the most appealing results as per your requirement (Eddy, 2001).

R/ R Studio
R/R-Studio is classified as being among the friendliest and most powerful big data analysis tool. The main benefits linked to R, are that the software is lights and open licence thus allowing any person to download and use the software to analyse data (cran.r-project, n.d.). Unlike Excel and Rapid miner discussed in this machine learning assignment which have an inbuilt algorithm that can be used to convert raw data to visuals, R requires scripts to be added to the command line. These commands will revive or discover the data based on which further algorithm would be used to filter and extract important information from the data. R Studio has grown popular due to more people opting for this program as it is free and open licence thus allowing some adventurous people to customize the program to perform specific tasks. R also is linked to various free modules which can be downloaded to handle unique activities and functions, something which is not possible on programs like Ms excel and Rapid minor. While R is considered as a favourite among data analysts, the data mining software has some limitations such as being pure script and code-driven thus making it difficult to use for people without coding knowledge.

Jupiter Python
Similar to R, Python is also a code-driven data mining tool which requires for the user to enter code to import, Analyse and report the data findings. Python can analyse data directly using the python interface but can also be used to analyse data using a popular python data analysis interface known as Jupiter I python or anaconda (Driscoll, n.d.). Python data analysis has also grown in popularity among data analyst simply due to it being free data analysis software and its ability to handle big data sets. Another important benefit linked to jumpier python data mining, the sample scripts and data samples are easily available on the internet which allows users to replicate the codes and analyse new data sets. This is important as it minimizes the need to master python coding which is a basic requirement to mine big data and product usage trends and information. With the help of readily available scripts, a data analyst can filter complex data and reveal important trends hidden within the data in both 2 and 3D which opens a new dimension linked to data analyses as opposed to traditional data analysis techniques. In many situations analysing 3 or more variables help identify important links or connections within large data sets. With points of reference identified in this machine learning assignment, further analysis can be performed using more filters to help real more trends and patterns within the data.

Data filtering (variables)
Both small and big data sets come with a myriad of data variables (categories) making it critical for the data to the first filter and sorted before the data can be mined for trends. This is an important step of machine learning which requires the data analyst to first review the available variables based on which the most suitable criterion can be selected and used to visualize the data. This is very important as visualizing the data helps combine the different variable data points which would make it easier for a person to detect a trend which can be investigated. Data is saved in rows and Columns with columns being dedicated to saving variable types and rows saving different series or entries falling within the given variable column (PERNSLEY, n.d.). This helps organize the data thus allowing data analyses tool to review the data by filtering it using the specified variable thus allowing the tools to convert the data into visual depictions. Below is a sample image of a dataset with the above discussed variable and entries.

As observed from the above data set, their data is entered to the sheet and distributed on individual cells falling in different rows and columns which makes it teacher for the data to be mapped. Each cell is recognized by the data mining software thus helping read and convert the data variables to a visual which makes it easier for the data set to be understood. Besides excel and CSV, data formats, data can also be stored on other data formats each special to data mining software. Much software is capable of reading various data formats but require for the data analyst to specify the type of data that would be accessed to load, read and analyse the data correctly.

How this machine learning assignment discuss Data Visualization Quality?
Converting data from numeric entries to data is mainly done to produce visual illustrations of the data which are considerably easier for the data analyst to read. Locating patterns on raw numeric data sets can be impossible for a common man but when the data is converted into a 2D or 3D visual the patterns within the data can easily be detected visually thus prompting for further research and analysis to be performed on the identified areas of interest (Zoss, n.d.). This makes the quality and ability to manipulate and view the data visuals from different angles very important to assess the data more accurately for effective business strategy development. Below are some data visuals produced using different data analysis tools and software where each ones visualization quality can be observed.

Microsoft Excel 2/3D visuals
Microsoft is the most popular data analysis tool due to being simple to use. The software is powerful and capable of producing high-quality visuals of data which can be used by data scientists to detect important trends. Below in this machine learning assignment are 2D and 3D visual produced on MS Excel which demonstrates the quality of images that can be produced from the software with the use of correct algorithms?

Excel 2D Visual

From the above 2D data visualization in this machine learning assignment, the software has produced a clear vision which can generate various images of the data by classifying them into quarterly and country name variables. This delivers a clear image of the data which makes it considerably easier for any person to determine the trend hiding within the numeric data set.

Excel 3D Visual
Microsoft Excel also has a powerful 3D data analysis and visualizing tool. The below image helps depict the power of the 3D tool and how it can be used to read data and produce high definition 3D models which can be used to assess complex data sets.

The complexity of the data set can easily be noticed on the 3D model but at the same time, the model helps communicates a vision of the data thus helping raise question relating to the data which can be further investigated. Excel 3D modelling is not commonly used as the program requires special plugins to be added but also has a data entry limit. Microsoft Excel continues to be the preferred data analysis tool as most people do not require to review large data sets thus MS Excel is sufficient for their daily use.

Rapid Miner2D/3D Visuals
Rapid miner is preferred by many people due it not requiring special algorithms to be used to generate models. The software uploads the data and reads the variables automatically before making suggestions linked to data analysis variables to be considered. Another major benefit of Rapid miner is its self-analyst and variable recommendation option. This is important as it analyses the data and proposes combinations which can be considered for closer review. This can is observed on the below image within the machine learning assignment which shows recommended data variables identified by Rapid miner for further exploration.

Rapid Miner 2D Visual
One major advantage Rapid miner has over excel is that the graphs are generated using bright colours automatically which makes the data easy to read. In this machine learning assignment, below is a simple 2D graphs produced on Rapid miner but the visual distinguish by colour coding different variable which helps the rapid miner graphs stand out.

From the above graph, colour codding the results helps the data analyst determine important data variables which can be used to prepare reports on the data. This is important as it helps display the patterns on the database helping the analyst make more informed decisions

Rapid Miner 3D Visual
Some data sets may require being to be analysed in a 3-dimensional view for anomalies and trends to be identified. Below is a 3D Scatter plot generated on Rapid miner which displays how the data has been classified and placed in different positions of a 3 mineral space.

This is important as it helps gain a 360 perspective of the data points based on which complex trends may be identified. Complex data sets are in many situations better analysed using 3D views as the data placement becomes noticeable based on which informed decisions can be made on the same.

R/R Studio
R studio mentioned in this machine learning assignment is by Far the most popular bog data analysis tool due to it being powerful and most important it being an open-source licence software. This means developers can make many different packages to analyse different data sets and produce a wide verity of models which help increase data analysis accuracy and make predictions for future movement more accurate. R also generates 2D and 3D visuals.

R 2D Visual
Due to the wide verity of add on and algorithms being developed for R, its problem to generate attractive 2D visuals of Data which incorporate various data. This is observed on the below visual where it the data has been displayed on a graph where different nations are placed at different locations with the bubble size depicting their populations thus helping deliver various data at the same time.

R 3D Visual
R is not only recognized for analysing big data sets but is also capable of producing HD diagrams which help detail point to point thus allowing doe refined analysis. This can be seen clearly on the below image which clearly outlines the level of Detail that an R 3D diagram can be programmed to generate.

Conclusion
From the above information explored in machine learning assignment, it is clear that data analysis tool help mine data and convert the information to visual diagrams which make the data much easier to understand. This quality has made data analysis and mining and important tool for businesses to use as it helps convert their performance from numeric data to visual which helps reveal important trends and movement which can be used as a reference of requests for further analysis made to help produce more accurate reports. Machine learning assignments are being prepared by our information technology assignment help experts from top universities which let us to provide you a reliable best assignment help service.

Reference List
[1]Chou, L., 2019. Top 6 Data Analytics Tools in 2019. [Online] Available at: https://towardsdatascience.com/top-6-data-analytics-tools-in-2019-4df815ebf82c [Accessed 25 January 2020].

[2]cran.r-project, n.d. An Introduction to R. [Online] Available at: https://cran.r-project.org/doc/manuals/r-release/R-intro.html [Accessed 25 January 2020].

[3]Driscoll, M., n.d. Jupyter Notebook: An Introduction. machine learning assignment [Online] Available at: https://realpython.com/jupyter-notebook-introduction/ [Accessed 25 January 2020].

[4]Eddy, W.F., 2001. Large Data Sets in Statistical Computing. [Online] Available at: https://www.sciencedirect.com/topics/computer-science/large-datasets [Accessed 25 January 2020].

[5]Maheswari, J.P., 2018. Breaking the curse of small datasets in Machine Learning: Part 1. [Online] Available at: https://towardsdatascience.com/breaking-the-curse-of-small-datasets-in-machine-learning-part-1-36f28b0c044d [Accessed 25 January 2020].

[6]PERNSLEY, T., n.d. Sorting and Filtering Data With Excel. [Online] Available at: https://www.learnexcelnow.com/sorting-and-filtering-data-with-excel/ [Accessed 25 January 2020].

[7]RapidMiner, n.d. RapidMiner Studio Feature List. [Online] Available at: https://rapidminer.com/products/studio/feature-list/ [Accessed 25 January 2020].

[8]Tableau, 2020. Data visualization beginner's guide: a definition, examples, and learning resources. [Online] Available at: https://www.tableau.com/learn/articles/data-visualization [Accessed 25 January 2020].

[9]WallStreetMojo, n.d. Product Excel Function. [Online] Available at: https://www.wallstreetmojo.com/product-excel-function/ [Accessed 25 January 2020].

[10]Zoss, A., n.d. Data Visualization: Visualization Types. [Online] Available at: https://guides.library.duke.edu/datavis/vis_types [Accessed 25 January 2020].