Simulation exercise for TMP

Think of it like this: have you ever purchased something just to discover that it did not operate as expected? That is exactly what I am trying to avoid as a business analyst. I ensure that, when you use a product, it always does what it is supposed to do! In this simulation, I will demonstrate how it is done. I'll look at the different types of tests performed and how to ensure that everything is running smoothly. Plus, I'll offer some simulated results about the 'product' to make it even better.

The Rossmann Store Sales dataset (accessible on Kaggle) offers insights into the sales performance of Rossmann establishments over time. This exploratory study seeks to identify patterns, trends, and linkages in the data in order to inform strategic decision-making and improve corporate operations. This exploration aims to better understand the factors driving sales at Rossmann outlets. I aim to discover major drivers of sales performance and areas for development by investigating numerous elements such as sales distribution, customer behaviour, promotional efficacy, holiday occurrences, shop kinds, and assortment methods. Benefits of the Analysis: Optimised Inventory and Staffing: By analysing sales data over time, i can detect peak sales periods, notably those around holidays and promotions. This intelligence enables me to better organise inventory and staffing to match consumer demand during peak traffic periods, resulting in increased operational efficiency and customer satisfaction. Tailored Product Offerings: By understanding customer preferences depending on store type and product assortment, i can tailor product offerings to fit their requirements and preferences. I can increase sales and customer satisfaction by tailoring our product offerings to their interests. Promotional efficacy: Analysing promotional events allows me to assess the efficacy of promotional strategy for increasing sales. Understanding how sales differ during promotional periods versus non-promotional periods allows me to fine-tune our promotional efforts to maximise effect and return on investment. Insights into Seasonal tendencies: Investigating holiday occurrences and school holiday patterns reveals seasonal tendencies that may affect store sales. Anticipating changes in customer behaviour during holidays and school breaks allows me to better organise marketing campaigns and promotional activities to capitalise on seasonal opportunities. Store Performance Analysis: By analysing store types and sales growth rates, i can evaluate individual stores' performance and find areas for improvement. By identifying stores with high or low sales growth rates, i can implement focused methods to improve sales performance and promote business growth.

This detailed guide will take you on a path through SQL and R integration. Learn how to connect to databases, conduct SQL queries, and get data using RMarkdown. Discover three distinct approaches for querying databases, ranging from writing SQL statements directly to using dplyr functions. Get hands-on experience with real-world applications and master database queries with RMarkdown. Whether you're a newbie or an experienced R user, this lesson will teach you how to fully utilise SQL in your RMarkdown documents.

This analysis explores the IBM HR Analytics Employee Attrition & Performance dataset to get insight into employee turnover and performance determinants. This dataset, which includes a rich repository of employee data such as demographics, job roles, satisfaction measures, and performance evaluations, provides an opportunity to find significant trends and drivers of attrition rates and job performance inside an organisation.

This document analyses engagement metrics and sentiment for two YouTube videos: "Black Music Honours '18" and "BET Awards '23". I used R syntax to obtain video metadata, visualise engagement indicators such as likes and comments, calculate total comments, analyse sentiment in video descriptions and comments, and investigate the relationship between likes and comments. This research seeks to provide insights into audience engagement, sentiment, and preferences, as well as vital information for content creators and marketers on how to optimise video content strategies for maximum audience effect.

Limitations of YouTube Lofi Dataset for Comprehensive Analysis: After rigorous analysis, I discovered that the dataset only contains one observation. This constraint severely limits the depth of analysis that may be undertaken and prevents the extraction of useful findings. Furthermore, while the dataset contains sentiment analysis results, the lack of multiple observations makes it unsuitable for drawing significant conclusions or identifying trends. The sentiment analysis data provided lacks the sample size required to undertake detailed analyses or visualisations. Given these constraints, it is evident that further research and analysis of this dataset are not practical. As a result, I recognise the necessity to discover another robust YouTube dataset with many observations. This would allow me to conduct more in-depth analysis, investigate engagement metrics, sentiment patterns, and other pertinent insights more efficiently.

I looked into a decade's worth of Amazon review data, which includes more than half a million evaluations over a wide range of product categories. Using R programming and complex data analysis tools, we investigate customer sentiment trends, identify patterns, and gain actionable insights to inform strategic decision-making for Amazon platform firms. Join us on this trip as we navigate the convoluted labyrinth of Amazon reviews in search of vital insights and business success.

Welcome to the enlightening world of customer churn analysis with R! In this RPubs publication, I look into the interesting world of customer behaviour research, specifically churn prediction within a telecommunications corporation. Churn analysis is an important task for firms in many industries because knowing why customers leave or stay may have a substantial impact on strategic decision-making, revenue generation, and customer retention efforts. Using the capabilities of R programming and data visualization with ggplot2, I investigated the factors influencing customer attrition and discovered significant insights that may inspire targeted business strategy. Join me on this exploration as I analyze customer demographics, contract types, monthly prices, total charges, and other factors to acquire a thorough understanding of customer turnover behaviour. Whether you're a data enthusiast, business analyst, or industry professional, this magazine contains essential insights and effective techniques for improving client retention and driving business success. Let's dig in and discover the secrets hidden in the data!

Welcome to the fascinating realm of consumer analytics, where the heartbeat of retail is interpreted via the prism of data. In this exploratory voyage, I took a deep dive into a large dataset supplied by the UCI Machine Learning Repository. This 'Online Retail II' data set contains all the transactions occurring for UK-based and registered non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. With almost 525,461 observations spanning eight variables, this dataset holds a wealth of information waiting to be discovered. Each entry in this dataset represents a distinct interaction between the retailer and its broad customer base, collecting a mosaic of transactional information, product features, and consumer demographics. My goal with advanced data analytics and visualization approaches is to extract actionable insights that illuminate strategic paths for retail organizations. My research began with rigorous preprocessing of the dataset, which includes handling missing data, detecting and filtering out cancelled orders, and improving variable forms for consistency and correctness. Moving on, I embarked on an enlightening voyage of exploratory data analysis (EDA), using a variety of visualization tools to uncover the distribution patterns of crucial variables such as quantity, price, and invoice date. I use histograms, time series graphs, and other visual aids to decipher complex purchasing behaviours and identify hidden trends in customer interaction. The next round of my analysis dug further into the essence of customer value by calculating RFM (Recency, Frequency, Monetary) indicators. Using complex K-means clustering algorithms, I divided customers into various cohorts based on their RFM ratings, shedding light on nuanced customer groupings such as high-value customers, regular customers, and inactive customers. Furthermore, I used 'association rule mining' algorithms like Apriori to identify detailed patterns in customer transactions, with an effort to reveal unique insights about product relationships and purchasing habits. Throughout this captivating trip, I provided insightful comments and actionable recommendations at each stage, equipping retail stakeholders with essential insights for refining marketing tactics, improving consumer experiences, and driving long-term business growth. Conclusively, this immersive exploration provides a strong framework for understanding customer behaviour using R in the online retail ecosystem. My analysis acts as a light of information for retail practitioners, providing a road map for navigating the complicated terrain of consumer analytics and realizing the latent potential of online retail.

This study focuses on forecasting the Wholesale Price Index (WPI) with an ARIMA model in R. It begins with exploratory data analysis, which includes WPI time-series visualizations. The ARIMA model is then applied to the WPI time series data to forecast future price fluctuations. Diagnostic plots are used to assess the model's adequacy, and projected values are created for the following time periods. These estimates give vital insights for organisations, allowing firms to anticipate and adjust to possible wholesale pricing changes, which improves strategic decision-making and planning.

The Glassdoor Job Posting 2024 dataset has a variety of data categories that necessitate different cleaning and processing approaches: Numeric data: salary_avg_estimate, company_rating, and ratings columns (for example, career_opportunities_rating). Categorical data include company, job title, location, employment type, industry, sector, and state. Text data: Job description, responsibilities, and skills. Date/Time information: Company_founded. Location is a spatial data type. Cleaning and modifying various data formats entails dealing with missing values, outliers, formatting issues, and special considerations such as encoding categorical variables, extracting features from text, handling dates, and even geocoding places.