Don't waste your lot of time when learning data analysis.

Here's how you may start your Data analysis journey

1️⃣ - Avoid learning a programming language (e.g., SQL, R, or Python) for as long as possible.

This advice might seem strange coming from a former software engineer, so let me explain.

The vast majority of data analyses conducted each day worldwide are performed in the "solo analyst" scenario.

In this scenario, nobody cares about how the analysis was completed.

Only the results matter.

Also, the analysis methods (e.g., code) are rarely shared in this scenario.

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2️⃣ Use Microsoft Excel for as long as possible.

Again, on the surface, strange advice from someone who loves SQL and Python.

When I first started learning data analysis, I ignored Microsoft Excel.

I was a coder, and I looked down on Excel.

I was 100% wrong.

Over the years, Excel has become an exceedingly powerful data analysis tool.

For many professionals, it can be all the analytical tooling they need.

For example, Excel is a wonderful tool for visually analyzing data (e.g., PivotCharts).

You can use Excel to conduct powerful Diagnostic Analytics.

The simple reality is that many professionals will never hit Excel's data limit - especially if they have a decent laptop.

#dataanalysis

3️⃣ Microsoft Excel might be your hammer, but not every problem is a nail.

Please, please, please use Excel where it makes sense!

If you reach a point where Excel doesn't make sense, know that you can quickly move on to technologies that are better suited for your needs....

#dataanalysis

4️⃣ SQL is your friend.

If you're unfamiliar, SQL is the language used to query databases.

After Microsoft Excel, SQL is the world's most commonly used data technology.

SQL is easily integrated into Excel, allowing you to leverage the power of the database server to acquire and wrangle data.

The results of all this goodness then show up in your workbook.

Also, SQL is straightforward for Excel users to learn.

#dataanalysis

5️⃣ Python in Excel.

Microsoft is providing you with just what you need to scale beyond Excel limitations.

At first, you use Python in Excel because it's the easiest way to scale and tap into a vast amount of DIY data science goodness.

As 99% of the code you write for Python in Excel translates to any tool, you now have a path to move off of Excel if needed.

For example, Jupyter Notebooks and VS Code.

#dataanalysis

🔥 Step-by-step Data Analysis Projects with SQL



Below are popular data projects from Kaggle, GitHub and Medium and YouTube. They will:

- Help you gain skills in working with real data
- Introduce you to SQL for data analysis
- Inspire you to undertake your own data analysis projects



🗺 Real World Fake Data Analysis

🏠 Housing sales in Nashville

🛒 Walmart Sales Analysis SQL Project

🧳 Alex the Analyst SQL Project

🤑 Superstore Sales Analysis using SQL

💸 International Debt Analysis using SQL

⚽️ Soccer Game Analysis using SQL

🌍 World Population Analysis 2015 using SQL

📉 SQL Project for Data Analysis

🚍 Public Transportation Data Analysis using SQL

📸 Instagram User Data Analysis using SQL

🙌 HR Data Analysis using SQL

🎬 Data Analyst Project: Step-by-step analysis with SQL

🎼 Music Store Data Analysis Project Using SQL

✅ Top 10 SQL Projects with Datasets

✅ Roadmap to Master SQL


#DataAnalyst #DataAnalytics #DataAnalysis #data_analyst #sql

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