𝗣𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱? 𝗛𝗲𝗿𝗲’𝘀 𝗬𝗼𝘂𝗿 𝗦𝘁𝗲𝗽-𝗯𝘆-𝗦𝘁𝗲𝗽 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗖𝗿𝗮𝗰𝗸 𝗣𝗿𝗼𝗱𝘂𝗰𝘁-𝗕𝗮𝘀𝗲𝗱 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀!😍

Landing your dream tech job takes more than just writing code — it requires structured preparation across key areas👨‍💻

This roadmap will guide you from zero to offer letter! 💼🚀

𝐋𝐢𝐧𝐤👇:-

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This plan works if you stay consistent💪✅️

Power BI Cheat Sheet ✍

This Power BI cheatsheet is designed to be your quick reference guide for creating impactful reports and dashboards. Whether you’re a beginner exploring the basics or an experienced developer looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting Data
- Import Data: *Home > Get Data > Select Data Source*
- Direct Query: *Home > Get Data > Select Data Source > Direct Query*

2. Data Transformation
- Power Query Editor: *Home > Transform Data*
- Remove Columns: *Transform > Remove Columns*
- Split Columns: *Transform > Split Column by Delimiter*
- Replace Values: *Transform > Replace Values*

3. Data Modeling
- Create Relationships: *Model > Manage Relationships > New*
- Edit Relationships: *Model > Manage Relationships > Edit*

4. DAX Calculations
- New Measure: *Modeling > New Measure*
- Common DAX Functions:
- SUM: SUM(table[column])
- AVERAGE: AVERAGE(table[column])
- IF: IF(condition, true_value, false_value)
- COUNTROWS: COUNTROWS(table)
- CALCULATE: CALCULATE(expression, filter)

5. Creating Visuals
- Select Visualization: *Visualizations Pane > Select Visual Type*
- Bar Chart: *Bar Chart Icon*
- Pie Chart: *Pie Chart Icon*
- Map Visual: *Map Icon*

6. Formatting Visuals
- Change Colors: *Format > Data Colors*
- Customize Titles: *Format > Title > Text*
- Adjust Axis: *Format > Y-Axis / X-Axis*

7. Filters
- Visual Level Filter: *Filter Pane > Add Filter for Selected Visual*
- Page Level Filter: *Filter Pane > Add Filter for Entire Page*
- Report Level Filter: *Filter Pane > Add Filter for Entire Report*

8. Slicers
- Add Slicer: *Visualizations > Slicer Icon*
- Customize Slicer: *Format > Edit Interactions*

9. Drillthrough
- Add Drillthrough: *Pages > Right Click on Field > Drillthrough*
- Back Button: *Insert > Button > Back Button*

10. Publishing & Sharing
- Publish Report: *Home > Publish > Select Workspace*
- Share Report: *File > Share > Publish to Web or Power BI Service*

11. Dashboards
- Create Dashboard: *Power BI Service > New Dashboard*
- Pin Visuals: *Pin Icon on Visual > Pin to Dashboard*

12. Export Options
- Export to PDF: *File > Export > PDF*
- Export Data: *Visual Options > Export Data*

Complete Checklist to become a Data Analyst: https://dataanalytics.beehiiv.com/p/data

You can refer these Power BI Interview Resources to learn more
👇👇
https://t.iss.one/DataSimplifier

Like this post if you need more useful resources 👍♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗧𝗵𝗮𝘁 𝗚𝗲𝘁𝘀 𝗬𝗼𝘂 𝗛𝗶𝗿𝗲𝗱?😍

If you’re just starting out in data analytics and wondering how to stand out — real-world projects are the key📊

No recruiter is impressed by “just theory.” What they want to see? Actionable proof of your skills👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

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Show recruiters that you don’t just “know” tools — you use them to solve problems✅️

Python Programming Interview Questions for Entry Level Data Analyst

1. What is Python, and why is it popular in data analysis?

2. Differentiate between Python 2 and Python 3.

3. Explain the importance of libraries like NumPy and Pandas in data analysis.

4. How do you read and write data from/to files using Python?

5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.

6. What are list comprehensions, and how do you use them in Python?

7. Explain the concept of object-oriented programming (OOP) in Python.


8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.

9. How do you handle missing or NaN values in a DataFrame using Pandas?

10. Explain the difference between loc and iloc in Pandas DataFrame indexing.

11. Discuss the purpose and usage of lambda functions in Python.

12. What are Python decorators, and how do they work?

13. How do you handle categorical data in Python using the Pandas library?

14. Explain the concept of data normalization and its importance in data preprocessing.

15. Discuss the role of regular expressions (regex) in data cleaning with Python.

16. What are Python virtual environments, and why are they useful?

17. How do you handle outliers in a dataset using Python?

18. Explain the usage of the map and filter functions in Python.

19. Discuss the concept of recursion in Python programming.

20. How do you perform data analysis and visualization using Jupyter Notebooks?

Python Interview Q&A: https://topmate.io/coding/898340

Like for more ❤️

ENJOY LEARNING 👍👍

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?😍

Whether you’re a student, job seeker, or just hungry to upskill — these 5 beginner-friendly courses are your golden ticket🎟️

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𝐋𝐢𝐧𝐤👇:-

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Enjoy Learning ✅️

Machine Learning isn't easy!

It’s the field that powers intelligent systems and predictive models.

To truly master Machine Learning, focus on these key areas:

0. Understanding the Basics of Algorithms: Learn about linear regression, decision trees, and k-nearest neighbors to build a solid foundation.


1. Mastering Data Preprocessing: Clean, normalize, and handle missing data to prepare your datasets for training.


2. Learning Supervised Learning Techniques: Dive deep into classification and regression models, such as SVMs, random forests, and logistic regression.


3. Exploring Unsupervised Learning: Understand clustering techniques (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE).


4. Mastering Model Evaluation: Use techniques like cross-validation, confusion matrices, ROC curves, and F1 scores to assess model performance.


5. Understanding Overfitting and Underfitting: Learn how to balance bias and variance to build robust models.


6. Optimizing Hyperparameters: Use grid search, random search, and Bayesian optimization to fine-tune your models for better performance.


7. Diving into Neural Networks and Deep Learning: Explore deep learning with frameworks like TensorFlow and PyTorch to create advanced models like CNNs and RNNs.


8. Working with Natural Language Processing (NLP): Master text data, sentiment analysis, and techniques like word embeddings and transformers.


9. Staying Updated with New Techniques: Machine learning evolves rapidly—keep up with emerging models, techniques, and research.



Machine learning is about learning from data and improving models over time.

💡 Embrace the challenges of building algorithms, experimenting with data, and solving complex problems.

⏳ With time, practice, and persistence, you’ll develop the expertise to create systems that learn, predict, and adapt.

Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

#datascience

𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗝𝘂𝘀𝘁 𝗥𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻’𝘁 𝗠𝗶𝘀𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

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𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗙𝗮𝘀𝘁: 𝗟𝗲𝗮𝗿𝗻 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁-𝗕𝗮𝘀𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯𝟬 𝗗𝗮𝘆𝘀!😍

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Whether you’re a beginner, student, or planning a career switch, this platform offers project-based courses👨‍💻✨️

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𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁’𝘀 𝗙𝗥𝗘𝗘 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 – 𝗟𝗲𝗮𝗿𝗻 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗪𝗼𝗿𝗸𝘀😍

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This course is your launchpad into the future of artificial intelligence✅️