Let’s build a Better Data Analytics Portfolio — 2022

𝗧𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗯𝗲𝘁𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 :

Data Analyst is an easier and an effective way to land on the ecosystem of data science

It requires very few standard tools to be learned. Having proficiency in tools like SQL, PowerBI, Excel, and Python is not sufficient to build a job-winning portfolio.

By building interesting projects, we can make that happen. here are some project ideas to build a strong portfolio

1. 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐂𝐨𝐯𝐢𝐝 𝐨𝐧 𝐔𝐒 𝐑𝐞𝐭𝐚𝐢𝐥 𝐓𝐫𝐚𝐝𝐞 — This is to measure the impact of pandemic on retail trade across US , the required dataset can be downloaded from the US census website ( link in comment ). By performing Pre-Post Analysis and Significance testing through the A/B test, Regression analysis, the impact can be measured and identified.

2. 𝐀𝐬𝐬𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐈𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐔𝐬𝐞𝐫 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 — A/B testing is a great way to improve any sort of user experience in a system. To assess the new features added to the system, organizations still prefer A/ B testing. In the given link, you can find the dataset for measuring user behavior in two different scenarios. By implementing A/B testing, we can show the scenario in which users had a better experience.

3. 𝐒𝐚𝐥𝐞𝐬 𝐃𝐫𝐢𝐯𝐞𝐫 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 — This one project will give you the space and opportunity to explore many statistical methods. from the link, you can download the sales, promotion, and competitor data of a Rossman store, using which you can test many hypotheses around sales and factors affecting sales. Regression analysis is a great way to assess the impact and come up with a waterfall chart on various drivers

4. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 — 𝗠𝘂𝘁𝗶 𝗧𝗼𝘂𝗰𝗵 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 — Customer Journey is an important use case that can be analyzed in an e-commerce domain. In the comment, I have attached a dataset that captured customer touch points ( logs ) data using which the patterns in the customer journey and the factors influencing them can be analyzed.

5. 𝘿𝙚𝙨𝙞𝙜𝙣 𝙔𝙤𝙪𝙧 𝙊𝙬𝙣 𝙀𝙭𝙥𝙚𝙧𝙞𝙢𝙚𝙣𝙩 𝙖𝙣𝙙 𝘼𝙣𝙖𝙡𝙮𝙯𝙚 𝙞𝙩 — This is going to be a crazy and time-taking activity. this is going to be an analysis activity of our daily routine for which data collection is important. by collecting the right data, we can understand our physical and behavioral changes of ourselves.

Few data points

1. Collect Daily movements data from your #googlefit / #smarband
2. Record / collect your food order / consumption data ( you can use #swiggy / #zomato data )
3. Record your sleeping time and wake-up time, exercises related to information
4. . If possible, Start recording your Work-related info like meetings attended, tasks completed ,

Links to the respective datasets

1. US retail data: https://www.census.gov/retail/index.html

2. Rossman sales: https://www.kaggle.com/c/rossmann-store-sales

3. Customer Journey: https://customer-journey.me/datasets/

4. User experience: https://scholarworks.montana.edu/xmlui/handle/1/3507

Check my LinkedIn Profile : https://www.linkedin.com/in/prabakaranchandrantheds/

Hope this helps you!

#dataanalysis #dataanalaysis #datastorytelling #sql #python #data #work #powerbi #sales #datascience #statistics #machinelearning #deeplearning #dataengineering