Rahat Maksud Pranto

Performed a pizza sales Report challenge from Maven Analytics.I'm supposed to be a business intelligence consultant employed by Pizza Company, a New Jersey pizza shop with a Greek-inspired. The restaurant hired me to assist in using data to boost operations.
◼ Here are my insights 📊:
1) $817.9K Total Revenue, 49.6K Total Quantity, 21.4K Total Orders, $138.5 Avg. Daily Pizza Sold, 2.3 Avg. Pizza Per Order.
2) Total Revenue & Orders by Month.
3) Total Orders and Revenue by Month.
4) Top pizzas sold by Category.
5) Revenue by pizzas size & category.
6) Top Used Ingredients.
7) Revenue by Weekday, Business Hour & Weekday VS Weekend Revenue.

◼ Here are my answers to the challenge questions:
1) What days and times do we tend to be busiest? 12 pm & 1 pm Lunch and Evening 5 pm-6 pm are Peak times in terms of Order Volume. Days: Thursday, Friday & Saturday (See ‘Orders by Hour’).
2) How many pizzas are we making during peak periods? During peak time (Thursdays at noon) we are making 2520 pizzas (See ‘Orders by Hour’).
3) What are our best and worst-selling pizzas? Best: The Classic Delux Pizza. Worst: The Brie Carre Pizza. (See' Top 5 Pizzas by Quantity Sold’ and ‘5 Worst Pizzas by Quantity Sold’).
4) What's our average order value? It’s $38.3.
◼Tools Used - Microsoft Power BI, MSSQL
◼ Steps -
- Query(search Answer by Querying in MSSQL & Compared with Final Insight)
- ETL (Power Query).
- Data Modelling.
- Creating DAX measures.
- Data Visualization.
🔑 Recommend 7 key business improvements.

During the course of "Microsoft Power BI Desktop for Business Intelligence" certification, I was given the opportunity to participate in a bonus project known as "Maven Market Analysis" I've constructed this Dashboard 📊 utilizing "Maven Market" datasets, in an effort to gain insightful knowledge regarding.

Task performed: Connecting & Shaping the Data, Creating the Data Model, Adding DAX Measures, Building the dashboard.

◼1st page(Overall Summary)- It contains👉 Total sales, total profit, total transaction, Profit margin, revenue, returns, weekday transactions vs. weekend transactions, revenue vs. target, weekly revenue trending, total sales by store country, state & city, total sales, Top product by sales and profit.

◼2nd page(Product Details)- It contains👉 current month profit vs. target, current month sold vs. target, current month returns vs. prev month, monthly transactions, weekly profit and weekly returns, Return rate, Top 10 brand and product by profit.

◼3rd page(Customer details)- It contains👉 Most profitable customer, customer revenue, customer orders, total sold,profit & revenue followed by each customer name, orders by occupation, gender & member card, orders & revenue by month, Product sold by age.

A comprehensive exploration of music tracks that were released during a variety of years is presented in this dashboard on analysis. My goal is to provide useful details about how music has changed over time, to highlight standout tracks, and to find trends that have influenced the industry of music.
🔑 Key insights 📊 :
◼ used HTML to show off lively covers of the most streamed songs.
◼ Focused on different timeframes, observe the evolution of song popularity throughout the year.
◼ Uncoverd the distinctive musical vibe that filled these tracks.
◼ utilized the dynamic TopN slicer to recognize the dominant streamers of the charts over different time period.
⚙ Tool Used: Microsoft Power BI
🗝 Key Learning: Background making through PowerPoint, Implemented a Deneb Heatmap, Deneb Unitchart, HTML Visual, Dynamic Slicer Selection with parameter.

Certified in Udemy's “Microsoft Power BI Desktop for Business Intelligence” course. This practical project was completed as part of this course. Eleven hours were devoted to practical instruction on how to use an all-encompassing business intelligence solution and an Adventure Works dashboard equipped with AI visuals to monitor key performance indicators (KPIs) such as revenue, sales, profit, and return, as well as to evaluate and compare regional performance, product level trends, and forecasts. Followed data cleansing and shaping techniques such as Dax Measures, calculated columns, and data modeling.

As personal learning, Prepared a dashboard from sales data. Task performed Loading data, Combining tables, Data modelling. Performed measures using DAX, Trend analysis, Forecasting, Add new data Utilizing interactive visualization.

Explored Power BI project of Simpson's script data over 27 years. Performed power query, measure creation, data cleaning and data Visualization. Connect the script's characters, locations, and episodes. The dashboard is visualized as a table, cards, map, slicer.