Change-In-Other-Working-Capital Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates through Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberStock BTX, a bridging trader and exchange system platform that provides trading tools classes; and CyberStock ECOS, a stock broking solution which offers real time market information, place trades, and manage orders solution. In addition, the company provides CyberStock Mobile Trader, a mobile trading system that connects users smartphones to exchanges to manage trading activities; and CyberStock EDS, an exempt dealer system that provides advanced trading infrastructure and facilities for commercial banks. Further, it offers CyberStock SMF, a share margin financing system that enables financial institutions, brokerage firms, and banks to operate and manage margin financing services; and CyberStock CNS, a custodian and nominee system, which provides value-added services, such as trade settlement, cash balances investment, income collection, corporate actions processing, recordkeeping and reporting to custodian banks for domestic services. Additionally, the company provides CyberStock BOS, a back office system to manage enormous file and data; and offers network and security services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

This MS Excel data has been processed into line graphs to create time series line graphs and data tables which give insight into changing physiochemical water quality characteristics and influences. The study sets out to determine if climate change has had an influence on physiochemical water quality characteristics both within and between the Breede and Olifants estuaries over a nine year monitoring period. The data represents changes and comparisons between salinity, temperature and rainfall within and between the Olifants and Breede river estuaries in the Wester Cape Province of South Africa.

🚘 Electric Vehicle (EV) Sales Analysis & Forecasting

The Electric Vehicle (EV) Sales Analysis & Forecasting project explores India’s EV adoption trends from 2014 to 2024, combining BigQuery for data analytics and Power BI for visualization. This end-to-end data project demonstrates expertise in SQL-based data analysis, time-series forecasting using BigQuery ML (ARIMA+), and interactive dashboard design. This repository presents an in-depth data analysis and visualization of Electric Vehicle (EV) sales trends across India using Microsoft Excel, SQL, Python and Power BI.

The Electric Vehicle Sales Dashboard is an interactive and visual analytics solution built using Power BI, focused on analyzing EV adoption trends across India. It includes KPIs, comparative charts, and advanced metrics to assist stakeholders in understanding EV sales performance from 2014 to 2024.

⚙️ Tech Stack

Google BigQuery → Data storage, cleaning, and analysis using SQL

BigQuery ML (ARIMA_PLUS) → Time series forecasting for future EV sales prediction

Power BI → Interactive dashboard for visualization and storytelling

Pyhton → For eda and visualization

Excel / CSV → Initial dataset preparation and transformation

🔹 Key Features: ✅ Total EV Sales Trends: Year-over-year and month-over-month change

✅ State-wise EV Performance: Compare EV adoption across Indian states

✅ Vehicle Category Breakdown: 2W, 3W, 4W, Buses, Others

✅ Dynamic Filtering: Use slicers to filter by Year, Month, State.

✅ KPI Cards: Total Sales, Average Sales

📊 Dashboard Overview Components:

Section Description 🔹 KPI Cards Highlight key performance indicators like Total Sales, Average Sales. 📈 Trend Charts Year-wise and month-wise sales volume (bar/line charts) 🗺️ Geo Analysis State-wise sales with ranking and contribution 🚗 Vehicle Type Pie Distribution of sales by 2W, 3W, 4W, Bus, Others 📅 Time Series Filtering and Interactive slicers for Year, Month, and State. 📊 YOY / MOM Change Dedicated visual section with % growth/dip over time

Dashboard using Power BI

https://github.com/user-attachments/assets/eca3b9c3-b446-4447-a38a-d61f5572a712" alt="DashBoards">

Dashboard using Excel

https://github.com/user-attachments/assets/f7cc0949-4581-4cb3-b3ab-ea9242bce803" alt="Dashboard excel">

🗓️ Year-on-Year EV Sales (2014–2024) - EV sales have grown from 2.4K in 2014 to 1.5M in 2023. - Peak growth observed in: - 2021 → 2022: +209% YoY growth - 2020 → 2021: +165% YoY growth - 2024 saw a drop (as of current data): -90.61% https://github.com/user-attachments/assets/02965465-d6cf-45bf-b127-40feb6fe9963" alt="yoy">

📆 Month-on-Month (MoM) Analysis - Highest sales months: November, December, and January - Noticeable drop in February followed by recovery in March https://github.com/user-attachments/assets/83bce2dd-b2ae-43b9-bdb2-def5b9dbe37d" alt="mom">

📍 State-wise Performance - Top Performing States: - 🥇 Uttar Pradesh: ~730K units - 🥈 Maharashtra: ~400K units - 🥉 Karnataka: ~320K units - Lower adoption in northeastern and union territory regions - https://github.com/user-attachments/assets/1ee491b5-9016-4a1e-850f-34e8b9a9aab3" alt="state wise ev">

EV State wise Sale correlation https://github.com/user-attachments/assets/4c5fc55d-389d-4bed-a9bb-eebe8ce31a11" alt="EV corr">

Top 5 State KDE Plot https://github.com/user-attachments/assets/1b9e2f69-9ea0-4ac4-8c26-0350fb75bd75" alt="EV kde plot">

🚗 EV Sales by Vehicle Type - 2-Wheelers: 50.3% of total sales - 3-Wheelers: 45.1% - 4-Wheelers and Buses: Small but growing segment https://github.com/user-attachments/assets/2fc64426-4e3d-4a14-b058-f7c67040663b" alt="vtype wise ev">

📅 Year-wise EV Sales Trend EV adoption has grown significantly from 2,392 units in 2014 to over 1.5 million units in 2023

Fastest growth period: 2020–2022, with YoY gains of 165% and 209%

2024 shows a decline (-90.6%) due to possible data incompleteness https://github.com/user-attachments/assets/d60567fb-fc61-463f-b0e8-34ddd14f1b8d" alt="Year wise ev">

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