This is how I would learn PowerBi for 2025:

* Learn to Load Data
* Learn PowerQuery to transform data
* Learn the Star Schema
* Learn DAX to create metrics
* Learn Data Visualisation
* Learn Data Story telling

PowerBi is an intuitive tool when you learn these concepts.
Data Analytics & Visualization Resources: https://t.me/DataAnalysisResourcesTP

Data Visualization Tools

Skills you Need to Become a Data Analyst

There has never been a better time to become a data analyst.

Tackle the tools:

* Excel
* SQL
* PowerBI/Tableau
* Python/R

Sharpen these soft skills:

* Communication
* Storytelling
* Critical thinking
* Business acumen

And let your journey begin.

Learn Power BI in 2025: https://t.me/DataAnalysisResourcesTP/7

Tips For the Future

Data Analystics Resources: https://t.me/DataAnalysisResourcesTP

What does a data analyst do?

Data Analyst Checklist

This is a very COMMON issue that I observe in the projects of aspiring candidates

They download a DATASET from Kaggle or any other website

Export it to a Data Analysis TOOL

And START the project with data cleaning

After cleaning the data, they PLUG it into a dashboard

In the dashboard, they put EVERY column into the visuals

Also they APPLY the filters of topbottom 10

Once done, they crack their KNUCKLES

And put this project in a list of SUCCESSFULLY completed projects

Over time, I have REVIEWED so many portfolio projects

And I see this ISSUES almost every time

When I go to their portfolio, for every project there is a DASHBOARD

But WHAT should I do after seeing a dashboard

What is it trying to SAY

What should I do after SEEING top or bottom 10 cities, states or products

Every dashboard lacks CONTEXT

And why NOT

Because they DON'T even know the business problem or problem statement

So the dashboard you created is of NO use

Your job is not just to create DASHBOARDS

Your job would be to create DASHBOARDS to take out important INSIGHTS

And from those insights, you will build RECOMMENDATIONS

And these recommendations will be given to stakeholders as a SOLUTION to their business problem

If they implemented your IDEAS and the problem gets solved

Now you can say your work is DONE

If you are SHOWING bottom 10 states, then what

You should write the INSIGHTS too

For example, the sales of North India zone are FALLING

The insights can be used like this

Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states

And this might be the REASON why our North India sales are DROPPING so hard

This is just a RANDOM example showing how your charts become UNDERSTANDABLE

Well, everyone can EXTRACT insights from charts

Even a KID can do this after looking at the tallest and smallest bar

The real task is to give RECOMMENDATIONS to solve the BUSINESS problem

And I have NEVER seen this in anyone's portfolio

If you are doing this, then you are easily STANDING out in the crowd

In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations

Even in the bullet point of projects in my resume, I included RECOMMENDATIONS

Now this is what you can call a STRONG portfolio

Because your analysis skills are the SAME as those used in the real life by a Data Analyst

Data Analytics Resource Center👇👇
https://t.me/DataAnalysisResourcesTP

Like if it helps 😄

Essential Python topics for data analysts 😄👇

Python Topics:

Python Resources - https://t.me/PythonResourcesTP

1. Data Structures
   - Lists, Tuples, and Dictionaries
   - NumPy Arrays for numerical data

2. Data Manipulation
   - Pandas DataFrames for structured data
   - Data Cleaning and Preprocessing techniques
   - Data Transformation and Reshaping

3. Data Visualization
   - Matplotlib for basic plotting
   - Seaborn for statistical visualizations
   - Plotly for interactive charts

4. Statistical Analysis
   - Descriptive Statistics
   - Hypothesis Testing
   - Regression Analysis

5. Machine Learning
   - Scikit-Learn for machine learning models
   - Model Building, Training, and Evaluation
   - Feature Engineering and Selection

6. Time Series Analysis
   - Handling Time Series Data
   - Time Series Forecasting
   - Anomaly Detection

7. Python Fundamentals
   - Control Flow (if statements, loops)
   - Functions and Modular Code
   - Exception Handling
   - File

Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!

Share with credits: https://t.me/PythonResourcesTP

Hope it helps :)

WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

Python

Learn Data Science in 2025

𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚

Pareto's Law states that "that 80% of consequences come from 20% of the causes".

This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.

Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.

But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).

For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.

So, invest more time learning topics that provide immediate value now, not a year later.

𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡️

There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.

Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.

So, find a mentor who can teach you practical knowledge in data science.

𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️

If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.

Join https://t.me/DataScienceResourcesTP for more

ENJOY LEARNING 👍👍

WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

5 Game-Changing Habits to Master Your Data Science Journey

Read Here: https://dev.to/justdetermined/5-game-changing-habits-to-master-your-data-science-journey-2nd4

Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp

Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations.
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation.
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPT’s natural language processing capabilities .

Tg: https://t.me/AIResourcesTP

How to Choose Charts for Your Data

There has never been a better time to become a data analyst.

Tackle the tools:

- Excel
- SQL
- PowerBI/Tableau
- Python/R

Sharpen these soft skills:

- Communication
- Storytelling
- Critical thinking
- Business acumen

And let your journey begin.

Essential tools and skills required to become a data analyst 👇👇

### Data Analysis and Visualization:
1. Microsoft Excel: Essential for data manipulation, analysis, and basic modeling.
2. SQL (Structured Query Language): Crucial for querying databases and extracting data for analysis.
3. Tableau or Power BI: Powerful tools for creating interactive dashboards and visualizing data.

### Programming and Data Manipulation:(Optional)
4. Python: Used for data manipulation, scripting, and automation.
5. R: Useful for statistical computing, data visualization, and basic analytics.

### Statistical Analysis:
6. Statistical Software (SPSS, SAS): Tools for advanced statistical analysis and modeling.(Optional)
7. Advanced Excel Functions: Proficiency in pivot tables, VLOOKUP, statistical functions, and data cleaning techniques.

### Project Management and Collaboration:(Optional)
8. Jira or Trello: Tools for project management, task tracking, and collaboration.
9. Confluence or SharePoint: Platforms for documentation, collaboration, and knowledge sharing.

### Business Process Management:(Optional)
10. Business Process Modeling Tools (Visio, Lucidchart): Used for modeling, analyzing, and optimizing business processes.

### Additional Skills:
11. Google Analytics: Important for understanding website traffic and user behavior. (Optional)
12. CRM Systems (Salesforce, HubSpot): Knowledge of these systems aids in analyzing sales data and customer interactions.(Optional)
13. Version Control (Git): Helps manage changes in analytical projects and ensures versioning control. (Optional)

### Data Warehousing and Database Management:
14. Data Warehousing (Amazon Redshift, Google BigQuery): Knowledge of these platforms for handling large-scale datasets and optimizing queries. (Optional)

### Soft Skills:
15. Communication: Clear and concise communication of findings and recommendations.
16. Problem-Solving & Critical Thinking: Ability to analyze complex problems and derive actionable insights.

I know this list might seem extensive, so it's best to begin with mastering Excel, Power BI, and SQL. As you progress, you can gradually add other tools from the list based on specific project needs and requirements.

Here are some essential telegram channels with important resources:

❯ SQL ➟ t.me/sqlresourcestp
❯ Data Analysis ➟ t.me/dataanalysisresourcestp

Also, try building projects & data portfolio while learning these skills. Creating data analytics projects will help you in showcasing the skills while giving job interviews.

Join t.me/techpsyche for more resources

ENJOY LEARNING👍👍

Top 15 advanced Power BI interview questions

1. Explain the concept of row-level security in Power BI and how to implement it.

2. What are calculated tables in Power BI, and when would you use them?

3. Describe the differences between DirectQuery, Live Connection, and Import Data storage modes in Power BI.

4. How can you optimize the performance of a Power BI report or dashboard with large datasets?

5. What is the DAX language, and how is it used in Power BI? Provide an example of a complex DAX calculation.

6. Explain the role of Power Query in data transformation within Power BI. What are some common data cleansing techniques in Power Query?

7. What is the purpose of the Power BI Data Model, and how do relationships between tables impact report development?

8. How can you create custom visuals or extensions in Power BI? Provide an example of when you would use custom visuals.

9. Describe the steps involved in setting up Power BI Gateway and its significance in a corporate environment.

10. What are the differences between Power BI Desktop, Power BI Service, and Power BI Mobile? How do they work together in a typical Power BI workflow?

11. Discuss the process of incremental data refresh in Power BI and its benefits.

12. How can you implement dynamic security roles in Power BI, and why might you need them in a multi-user environment?

13. What are Power BI paginated reports, and when would you choose to use them over standard interactive reports?

14. Explain the concept of drill-through in Power BI, including its configuration and use cases.

15. How can you integrate Power BI with other Microsoft products, such as Azure Data Lake Storage or SharePoint?

Hope you'll like it

More Resources Here: https://t.me/DataAnalysisResourcesTP

Like this post if you need more resources like this 👍❤️

Why Tabular Data Reigns Supreme in the Real World of AI

Read Here: https://dev.to/justdetermined/why-tabular-data-reigns-supreme-in-the-real-world-of-ai-5ena

Top 15 Advanced Power BI Interview Questions

Link: https://dev.to/henryclapton/top-15-advanced-power-bi-interview-questions-2942

4 Most Useful Charts to Show Trends: Data Visualization

Link: https://dev.to/justdetermined/4-most-useful-charts-to-show-trends-data-visualization-3pdg