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

TOP CONCEPTS FOR INTERVIEW PREPARATION!!

🚀TOP 10 SQL Concepts for Job Interview

1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)


🚀TOP 10 Statistics Concepts for Job Interview

1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression


🚀TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming

Like ❤️ the post if it was helpful to you!!!

9 secrets about Data Storytelling every analyst should know (number 6 is a must):

1/ Start with the end in mind—what’s the key takeaway?

2/ Don’t just present numbers—explain the 'so what' behind them.

3/ Data should drive decisions—frame your analysis as a solution to a problem.

#DataAnalytics

4/ Visualise trends over time to tell a story.

5/ Add context to your data—it makes your insights relevant.

6/ Speak the language of your audience—simplify complex terms.

7/ Use metaphors or analogies to explain difficult concepts. Don't use professional jargon.

8/ Include both the big picture and the details—it appeals to different stakeholders.

9/ Conclude with a call to action—what should they do next?

How Data Analytics Helps to Grow Business to Best

Analytics are the analysis of raw data to draw meaningful insights from it. In other words, applying algorithms, statistical models, or even machine learning on large volumes of data will seek to discover patterns, trends, and correlations. In this way, the bottom line is to support businesses in making much more informed, data-driven decisions.

In simple words, think about running a retail store. You’ve got years of sales data, customer feedback, and inventory reports. However, do you know which are the best-sellers or where you’re losing money? By applying data analytics, you would find out some hidden opportunities, adjust your strategies, and improve your business outcome accordingly.

read more......

🚀Roadmap to Becoming a Data Analyst🚀

Start your journey with these key steps:-

1️⃣ SQL: Master querying and managing data from databases.
2️⃣ Python: Use Python for data manipulation and automation.
3️⃣ Visualization: Present data using Matplotlib/Seaborn.
4️⃣ Excel: Handle data and create quick insights.
5️⃣ Power BI/Tableau: Build interactive dashboards.
6️⃣ Statistics: Understand key concepts for data interpretation.
7️⃣ Data Analytics: Apply everything in real-world projects!

#DataAnalyst

Data Analyst: Analyzes data to provide insights and reports for decision-making.

Data Scientist: Builds models to predict outcomes and uncover deeper insights from data.

Data Engineer: Creates and maintains the systems that store and process data.

Don't make this mistake as a beginner data analyst:

Not learning SQL

There's a reason it's been around for 40+ years.

Get started with:

- SQL basics (syntax + structure)
- Data Manipulation (JOINs, GROUP BY etc)
- Aggregation Functions (SUM, AVG etc)

How to annoy a data analyst in 2024:


☑ Assume the analysis you're asking is "just a quick SQL thing."
☑ Ask to "tweak" a finished dashboard. It's never just a small change.
☑ Question why the numbers in their carefully crafted dashboard don't match your hastily pulled spreadsheet.
☑ Assume all data is clean, structured, and readily available. Spoiler: it's not.
☑ After receiving a detailed, interactive dashboard, ask, "Can I just get this as a printable PDF?" 🤦🏽♂️🤦🏽♂️

Hi guys,

Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.

For those of you who are new to this channel, here are some quick links to navigate this channel easily.

Data Analyst Learning Plan 👇
https://t.iss.one/sqlspecialist/752

Python Learning Plan 👇
https://t.iss.one/sqlspecialist/749

Power BI Learning Plan 👇
https://t.iss.one/sqlspecialist/745

SQL Learning Plan 👇
https://t.iss.one/sqlspecialist/738

SQL Learning Series 👇
https://t.iss.one/sqlspecialist/567

Excel Learning Series 👇
https://t.iss.one/sqlspecialist/664

Power BI Learning Series 👇
https://t.iss.one/sqlspecialist/768

Python Learning Series 👇
https://t.iss.one/sqlspecialist/615

Tableau Essential Topics 👇
https://t.iss.one/sqlspecialist/667

Best Data Analytics Resources 👇
https://heylink.me/DataAnalytics

You can find more resources on Medium & Linkedin

Like for more ❤️

Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.

Hope it helps :)

✅𝟓-𝐒𝐭𝐞𝐩 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐒𝐰𝐢𝐭𝐜𝐡 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐅𝐢𝐞𝐥𝐝✅

💁‍♀️𝐁𝐮𝐢𝐥𝐝 𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬: Focus on core skills—Excel, SQL, Power BI, and Python.

💁‍♀️𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Apply your skills to real-world data sets. Projects like sales analysis or customer segmentation show your practical experience. You can find projects on Youtube.

💁‍♀️𝐅𝐢𝐧𝐝 𝐚 𝐌𝐞𝐧𝐭𝐨𝐫: Connect with someone experienced in data analytics for guidance(like me 😅). They can provide valuable insights, feedback, and keep you on track.

💁‍♀️𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiter’s eye.

💁‍♀️𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.

Join this SQL WhatsApp Group 👇

https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v