Whitney

Data Analyst | Power BI | Excel | SQL

[Whitney @analyti_code]

I am excited to share that I’ve successfully completed my internship as a Data analyst Intern at Decodelabs and received my Certificate of Completion.
It’s been an incredible learning experience.
#Internship #DataAnalytics

[Whitney @analyti_code]

#SQL #DataAnalysis #DataAnalyst #LearningInPublic #AnalystLabAfrica

[Whitney @analyti_code]

Completed the second project from my third task as a data analyst intern at AnalystLab Africa on a Sales Data Analysis project using SQL Server, taking the analysis from database setup and core SQL queries to advanced SQL concepts, business problem-solving, and query optimization

[Whitney @analyti_code]

A very important lesson from this project was understanding the dataset structure: each row represents an order line rather than an entire order, making COUNT(DISTINCT ORDERNUMBER) important when calculating the number of orders.

[Whitney @analyti_code]

- Applying window functions such as RANK() and ROW_NUMBER()
- Identifying top-performing products and customers
- Analyzing monthly revenue trends
- Examining customer purchasing behavior
- Reviewing execution plans and creating indexes to improve query performance

[Whitney @analyti_code]

- Analyzing sales by product line and country using GROUP BY and aggregate functions
- Using AVG, MIN, and MAX to understand sales performance
- Applying self-joins to compare customer orders
- Using LEFT JOIN with a lookup table

[Whitney @analyti_code]

In this project, I worked with a denormalized SalesData table containing order, product, and customer information.
Some of the key areas I explored included:
- Filtering and sorting high-value orders using WHERE and ORDER BY

[Whitney @analyti_code]

#SQL #DataAnalysis #DataAnalytics #AnalystLabAfrica

[Whitney @analyti_code]

I recently completed my third task as Data Analyst at AnalystLab Africa with a SQL analysis project using the Chinook database in SQL Server.
The project gave me the opportunity to work through SQL concepts from basic queries to more advanced analysis.

[Whitney @analyti_code]

To finish the analysis, I reviewed execution plans and added indexes to foreign key columns used in the join-heavy queries.

This project helped me better understand how SQL can be used to move from raw database tables to meaningful business analysis.

[Whitney @analyti_code]

customers with no invoices.

I also looked at practical business questions such as identifying the top 10 tracks and artists by revenue, the highest-spending customers, and how revenue changed over time.

[Whitney @analyti_code]

• Monthly revenue trends
• Customer purchasing behavior
• Query optimization and indexing

One part I particularly worked on was using JOINs to connect related tables. For example, I used INNER JOIN to connect tracks with their albums and artists, and LEFT JOIN to identify…

[Whitney @analyti_code]

I worked with:

• SELECT, WHERE and ORDER BY
• GROUP BY and HAVING
• Aggregate functions such as AVG, MIN, MAX, COUNT and SUM
• INNER JOIN, LEFT JOIN and RIGHT JOIN
• Window functions using RANK() and ROW_NUMBER()
• Revenue analysis by tracks, artists and customers

[Whitney @analyti_code]

@ChuksEmma947401 I’m interested please

[Whitney @analyti_code]

Understanding the data, cleaning it properly, and making the right transformation decisions are what make the final insights reliable.
#DataAnalytics #SQL #PowerBI #DataCleaning #DataVisualization #Netflix #AnalystLabAfrica #DataAnalysis

[Whitney @analyti_code]

I recently completed my second task as an intern at Analystlab Africa using the Netflix Movies & TV Shows dataset, which contains 8,807 titles across 12 columns.

[Whitney @analyti_code]

The final Power BI dashboard was structured into two pages: a Catalog Overview and a Content Breakdown, with interactive filters for content type, rating, and date added.

Most importantly, this project reinforced that effective data analysis goes beyond simply creating visualiza

[Whitney @analyti_code]

- Most movies have runtimes within the 80–120 minute range, although runtime varies across different genres.

The analysis also revealed differences in content distribution across countries, genres, ratings, and content types.

[Whitney @analyti_code]

- Netflix content additions increased significantly between 2015 and 2020, reaching a peak in 2020 and declining in 2014 to 2008.

- TV-MA and TV-14 together account for approximately 61% of rated content.

[Whitney @analyti_code]

Some of the key findings from the analysis include:-

- 70.4% of the Netflix catalog consists of Movies, while 30.3% consists of TV Shows.

- The United States is the largest content source, with 3,205 titles, followed by India.

[Whitney @analyti_code]

dataset available for the rest of the dashboard.

After cleaning and exploring the data in SQL, I built the final interactive dashboard in Power BI, using Power Query, DAX, and custom visualizations.

[Whitney @analyti_code]

"Not Specified". Removing these records from the entire dataset would have affected other analyses, so I created a referenced query specifically for the country analysis. This allowed me to exclude the unwanted values from the country visualization while keeping the complete…