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All methods and resources required for the collecting and analysis of critical data are referred to as data analytics. Analytics is a broad term that encompasses a variety of data processing methods. Statistical instruments or applications, as well as quality of life surveys in the medical profession, are examples of analytical procedures that are both qualitative and objective. The method gathers and transforms useful data from worthless data, then analyses it to produce numerical statistics and trends that might lead to economic improvements. allowing them to foresee customer patterns and behaviours. They reveal if the company is on the correct course or whether anything needs to be done to get it back on track (Brandt and Brandt, 1998). System aids in the identification of market trends and demands, as well as the investigation of why some products or services perform successfully while others lag behind. Most businesses engage a qualified digital marketer to collect and evaluate the data that their website generates. Alternatively, if it is a small-scale business, you can take a data analytics course from a reputable digital marketing firm and do the work yourself. Because data and analytics are frequently the most closely guarded secrets of any company.
Business information can assist in making critical judgments. Market expansion, product or service diversification, pricing practises, and customer service are just a few examples (BBerthol, 2003). A corporation that bases its decisions on data and analysis usually has an advantage over its competitors. Because it can make conclusions based on facts rather than speculation or unclear data. Benefit from exponential data expansion, but smaller businesses are more able to act on data-derived insights quickly and efficiently than larger companies, which are often less nimble and hampered by bulky, legacy IT infrastructure. All that is required is someone in the company who is familiar with two crucial concepts: data analytics and data science. While a business can be established on a combination of creativity and hard work, managing.From anticipating and minimising churn to securing new business. The ability to keep supply chains optimised and future-ready is critical for organisations to continue operating and expanding globally. Data has aided supply chains in this endeavour more than anything else (Freeland et al., 1998).
Analytical approach
The digital world, which is data-driven, provides consumers and businesses with a plethora of new alternatives and tools. We may even argue that there are too many options to choose from in many cases. People are sometimes paralysed by an abundance of options, as they get overwhelmed and take no action in the face of so many options. While this is bothersome and inconvenient for the regular citizen, it is disastrous for the commercial sector, particularly when it comes to putting together a good business plan.
SMART way Define the business goal
This may seem self-evident, but I've lost track of how many consulting projects I've worked on where the business purpose has been inadequately expressed or not specified at all. When the corporate aim is overly broad, such as "I want to improve earnings" or "I want to increase productivity because I believe our staff are not working hard enough," the problem occurs. When discussing business difficulties, managers and consultants should keep in mind that senior executives are frequently in "System 1" mode. They're reacting based on emotions and instinct - something in their gut tells them something isn't right, but they haven't fully considered the situation (Freelan et al., 1998).
Identify the Outcome Indicators
While having a "SMART" objective is a good start, you should work fast to discover the most important key performance indicators for the company goal. Senior executives may occasionally present a long list of KPIs that have little to do with the business goal. Other times, you discover that the company does not collect data on the performance metric at all, or that the data is of poor quality. These are concerns that must be taken into account and addressed early in the problem-solving process. If the present metrics aren't relevant to the business aim, you may need to agree on proxy measures first, or perhaps implement a data-collection/cleaning procedure (Berthold, at,el., 2003).
Understand the Drivers and Relationships
We need to build a concept of what influences or drives changes in the Dependent Variable (or Outcome Indicator) now that you've established it. We typically use the MECE framework (mutually exclusive, collectively exclusive) in consulting to identify all probable change drivers. This basically entails the establishment of broad categories of drivers/relationships that would most likely account for the majority of the changes in the Outcome Indicator. These groups should ideally be mutually exclusive because it aids in thinking organisation as well as communicating (Berthold et al., 2003).
Analysis
Steps to clean the data set
To clean the data, we are using Python (Jupiter notebook) using PANDAS library.
Dropping Columns in a Data Frame
Changing the Index of a Data Frame
Tidying up Fields in the Data
Combining str Methods with NumPy to Clean Columns
Cleaning the Entire Dataset Using the apply map Function
Renaming Columns and Skipping Rows
Python Data Cleaning: Recap and Resources
The drop () function in Pandas makes it easy to remove unneeded columns or rows from a Data Frame. Let's have a look at a simple example of removing a few columns from a Data Frame. A Pandas Index extends NumPy arrays' capability to allow for more flexible slicing and labelling. In many circumstances, it is advantageous to use the data's index as a uniquely valued identifying field. So far, we've deleted extraneous columns and altered our Data Frame’s index to a more sensible value. To gain a better grasp of the dataset and ensure consistency, we will clean certain columns and convert them to a standard format in this section. Date of Publication and Place of Publication will be cleaned in particular. All of the data types appear to be the object type, which is roughly equivalent to str in native Python. Any field that cannot be neatly accommodated as numerical or categorical data is included in this category. This makes sense because we're dealing with data that starts out as a jumble of strings
Monitor errors
Keep track of the patterns that lead to most of your errors. This will make detecting and correcting inaccurate or faulty data much easier. If you're integrating other solutions with your fleet management software, keeping records is extremely vital so that your mistakes don't clutter up the work of other departments.
Standardize your process
Standardize the point of entry to help reduce the risk of duplication.
Validate data accuracy
Validate the accuracy of your data after you've cleaned up your existing database. Investigate and invest in data-cleaning solutions that can be used in real time. Some solutions even employ artificial intelligence (AI) or machine learning to improve accuracy testing (Rice et al., 2004).
Scrub for duplicate data
To save time when examining data, look for duplication. Research and invest in alternative data cleaning solutions that can examine raw data in bulk and automate the process for you to avoid repeating data (Rice et al., 2004).
Analyse data
Use third-party sources to augment your data after it has been standardised, vetted, and cleansed for duplicates. Reliable third-party sources can collect data straight from first-party sites, clean it up, and assemble it for business intelligence and analytics.
Extract the given data file in Jupiter notebook using panda library
We have successfully extracted the formative data set.
We have successfully Summative Data set
The machine learning pipeline is the workflow of the machine learning process, commencing with the definition of our business challenge and ending with the deployment of the model. The data preparation step in the Machine Learning pipeline is the most complex and time-consuming because the data is in an unstructured format and requires cleaning. In this article, we'll take a closer look at the Data Analysis section utilising statistics.
Python Libraries used in Data Analysis
SciPy
SciPy is the collection of open-source libraries, which helps to organize our data for analysis.
There are various libraries used namely,
NumPy
Used for scientific computing such as Numerical Analysis, Linear algebra, and metric computation.
It is essential for Machine Learning (ML) implementation
Using the library matplotlib we will plot the graphical representation of the csv file extracted using panda’s module. Pandas
Pandas is an open-source library with High performance, easy-to-use Data Structures, and Analysis tools for Python. Data Science works like Calculating statistics, cleaning data, etc…
It is highly used in Data Mining and Preparation but less in Data Modelling & Analysis (Carpineto et al., 2004).
Data analysis necessitates the use of visualisation. It acts as a first line of defence, revealing complicated data structure that can't be absorbed any other way. Unforeseen impacts are discovered, and anticipated ones are challenged (Carpineto et al., 2004).
Data is invisible in and of itself, consisting of bits and bytes stored in a file on a computer hard disc. We must visualise data in order to be able to see and understand it. In this chapter, I'll employ a broader definition of “visualising,” which encompasses even pure textual data representations. For example, data visualisation can be as simple as putting a dataset into a spreadsheet programme. The info that was before invisible becomes visible (McKinney et al., 2012).
From the graph we can see that the sale value of the Bracelet is more than that of other items and it is around 2500000 which is a more than that of any other items.
From the graph we can see that the sale volume of the Bracelet is more than that of other items and it is around 6000 which is a more than that of any other items.
Sales market in Japan is more than that of UK and USA. So, the product is reliable in Asian country rather than that of European country.
All the above analysis are done on the basis of summative data sheet and it conclude that bracelet is the most demanding item in the market especially in the Japan or Asian market.
The link between a group of variables is estimated using regression analysis. When you perform a regression analysis, you're looking for a correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors which may have an impact on the dependent variable). The goal of regression analysis is to figure out how one or more factors may influence the dependent variable in order to spot trends and patterns. This is very important for projecting future trends and generating forecasts (Sáenz et al., 2002).
As the market in UK is less than Japan and USA to improve the market, we have to review the market details in UK.
From the above graphical analysis, we can see that in the UK market bracelet are the most selling item. To capture the market in the UK we must be focused on the bracelet and their different kinds which may increase the market of the UK from the current position. If we make a comparison with another country the item except bracelet makes a standpoint in the market but in the UK those items are not so popular. So, to make a market in the UK we must increase the popularity of those items by making them as per their requirement.
Conclusion and Next steps
Evaluate
As we can see from our analysis using the summative data sheet in every aspect the bangles are more popular among the consumer and received the highest sale value in the market. But in the market of Japan. USA market is in the second position among the three countries (Thomas et al., 2019). So, using the detail data analysis we can find out why people of USA are not liking the bangles and what makes the people of Japan buying the product so we can apply the same strategy to improve the market condition of the USA. It also provides the data of people those who are working in USA market should improve themselves to improve the market condition, new haring can be made to improve the condition. Data analysis helped to find the area where we have to focus to solve the issue (Thomas et al., 2019). The type of data analysis that can be done is determined by the data collection methods used. Any of the main data collection techniques: interviews, questionnaires, and observation can be used to obtain qualitative and quantitative data. Calculating percentages and averages is a common part of quantitative data analysis for interaction design. Averages are divided into three categories: mean, mode, and median (Bernhardt, 2017). Patterns, outliers, and the overall view of the data can all be identified using graphical representations of quantitative data. Theories can be used to frame qualitative data analysis. Grounded theory, activity theory, and distributed cognition are three such theories.
Board Summary
Reference
Bernhardt, V.L., 2017. Data analysis: for continuous school improvement. Routledge.
Berthold, M. and Hand, D.J., 2003. Intelligent data analysis (Vol. 2). Berlin: Springer.
Brandt, S. and Brandt, S., 1998. Data analysis. Springer-Verlag.
Carpineto, C. and Romano, G., 2004. Concept data analysis: Theory and applications. John Wiley & Sons.
Freeland, S.L. and Handy, B.N., 1998. Data analysis with the SolarSoft system. Solar Physics, 182(2), pp.497-500.
Idris, I., 2014. Python data analysis. Packt Publishing Ltd.
McKinney, W., 2012. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. “ O'Reilly Media, Inc.”.
Rice, J.A., 2006. Mathematical statistics and data analysis. Cengage Learning.
Sáenz, J., Zubillaga, J. and Fernández, J., 2002. Geophysical data analysis using Python. Computers & geosciences, 28(4), pp.457-465.
Thomas, D.M. and Mathur, S., 2019, June. Data analysis by web scraping using python. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 450-454). IEEE.
Berthold, M. and Hand, D.J., 2003. Intelligent data analysis (Vol. 2). Berlin: Springer.
