Machine Learning Algorithm cheat sheet

If you're serious about getting into Data Science with Python, follow this 5-step roadmap.

Each phase builds on the previous one, so don’t rush.

Take your time, build projects, and keep moving forward.

Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.

✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).

Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.

✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning script for a messy CSV file. Add comments to explain every step.

Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.

✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.title(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.

Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.

✅ What to learn:
Descriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.

Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.

✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()

– Final Checkpoint:

Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub

Don’t just complete tutorialsm create things.

Explain your work.
Build your GitHub.
Write a blog.

That’s how you go from “learning” to “landing a job

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

All the best 👍👍

Roadmap to become Data Scientist

🚀 PowerBI Interview Questions Recently Asked at an MNC:

1️⃣ What are the limitations of using Direct Query connection mode reports?

Direct Query connects your Power BI report directly to the live data source, but it comes with some limitations. Here’s a simplified explanation:

➡️ Slower Performance
Every report interaction sends a query to the data source, causing delays.
Example: Imagine asking a librarian for every book you need, instead of having the books already with you.

➡️ Limited Features
Some advanced Power BI features aren’t supported in Direct Query mode.
Example: A basic calculator can’t perform complex scientific functions like specialized software.

➡️ Dependent on Source
Report performance depends entirely on the data source’s speed and availability.
Example: If the library (data source) is slow or closed, you can’t access your books (data).

➡️ Complex Queries
Handling complex calculations can be difficult or slow.
Example: Solving advanced math on a basic calculator takes time and effort.

➡️ Security and Access Issues
Direct Query relies on the data source’s security settings, which may limit access.
Example: If the library restricts access to rare books, you’ll face similar limitations.

💡 Key Takeaway: Direct Query ensures real-time data but can be slower, less flexible, and depends heavily on the data source’s performance and security.

#PowerBIInterview

10 Simple Habits to Boost Your Data Science Skills 🧠📊

1) Practice data wrangling daily (Pandas, dplyr)
2) Work on small end-to-end projects (ETL, analysis, visualization)
3) Revisit and improve previous notebooks or scripts
4) Share findings in a clear, story-driven way
5) Follow data science blogs, newsletters, and researchers
6) Tackle weekly datasets or Kaggle competitions
7) Maintain a notebooks/journal with experiments and results
8) Version control your work (Git + GitHub)
9) Learn to communicate uncertainty (confidence intervals, p-values)
10) Stay curious about new tools (SQL, Python libs, ML basics)

💬 React "❤️" for more! 😊

🗄️ SQL Developer Roadmap

📂 SQL Basics (SELECT, WHERE, ORDER BY)
∟📂 Joins (INNER, LEFT, RIGHT, FULL)
∟📂 Aggregate Functions (COUNT, SUM, AVG)
∟📂 Grouping Data (GROUP BY, HAVING)
∟📂 Subqueries & Nested Queries
∟📂 Data Modification (INSERT, UPDATE, DELETE)
∟📂 Database Design (Normalization, Keys)
∟📂 Indexing & Query Optimization
∟📂 Stored Procedures & Functions
∟📂 Transactions & Locks
∟📂 Views & Triggers
∟📂 Backup & Restore
∟📂 Working with NoSQL basics (optional)
∟📂 Real Projects & Practice
∟✅ Apply for SQL Dev Roles

❤️ React for More!

Follow this to optimise your linkedin profile 👇👇

Step 1: Upload a professional (looking) photo as this is your first impression

Step 2: Add your Industry and Location. Location is one of the top 5 fields that LinkedIn prioritizes when doing a key-word search. The other 4 fields are: Name, Headline, Summary and Experience.

Step 3: Customize your LinkedIn URL. To do this click on “Edit your public profile”

Step 4: Write a summary. This is a great opportunity to communicate your brand, as well as, use your key words. As a starting point you can use summary from your resume.

Step 5: Describe your experience with relevant keywords.

Step 6: Add 5 or more relevant skills.

Step 7: List your education with specialization.

Step 8: Connect with 500+ contacts in your industry to expand your network.

Step 9: Turn ON “Let recruiters know you’re open”

Generate Barcode using Python 👆

Since many of you got the last question incorrect, let's understand Confusion Matrix in detail

A Confusion Matrix is used to evaluate how well a classification model performs by comparing actual vs predicted outcomes.

🔍 Structure:
• Actual Positive, Predicted Positive → ✅ True Positive (TP)
• Actual Positive, Predicted Negative → ❌ False Negative (FN)
• Actual Negative, Predicted Positive → ❌ False Positive (FP)
• Actual Negative, Predicted Negative → ✅ True Negative (TN)

📘 Key Terms:
• TP: Predicted Positive & Actually Positive
• TN: Predicted Negative & Actually Negative
• FP: Predicted Positive but Actually Negative
• FN: Predicted Negative but Actually Positive

🧮 Formulas:
• ×Accuracy× = (TP + TN) / Total
• ×Precision× = TP / (TP + FP)
• ×Recall× = TP / (TP + FN)
• ×F1 Score× = 2 × (Precision × Recall) / (Precision + Recall)

💡 Analogy: Spam Email Detector
• TP: Spam email marked as spam
• TN: Real email marked as not spam
• FP: Real email marked as spam
• FN: Spam email marked as real

💬 React with ❤️ for more such tutorials!

Advanced Questions Asked by Big 4

📊 Excel Questions
1. How do you use Excel to forecast future trends based on historical data? Describe a scenario where you built a forecasting model.
2. Can you explain how you would automate repetitive tasks in Excel using VBA (Visual Basic for Applications)? Provide an example of a complex macro you created.
3. Describe a time when you had to merge and analyze data from multiple Excel workbooks. How did you ensure data integrity and accuracy?

🗄 SQL Questions
1. How would you design a database schema for a new e-commerce platform to efficiently handle large volumes of transactions and user data?
2. Describe a complex SQL query you wrote to solve a business problem. What was the problem, and how did your query help resolve it?
3. How do you ensure data integrity and consistency in a multi-user database environment? Explain the techniques and tools you use.

🐍 Python Questions
1. How would you use Python to automate data extraction from various APIs and combine the data for analysis? Provide an example.
2. Describe a machine learning project you worked on using Python. What was the objective, and how did you approach the data preprocessing, model selection, and evaluation?
3. Explain how you would use Python to detect and handle anomalies in a dataset. What techniques and libraries would you employ?

📈 Power BI Questions
1. How do you create interactive dashboards in Power BI that can dynamically update based on user inputs? Provide an example of a dashboard you built.
2. Describe a scenario where you used Power BI to integrate data from non-traditional sources (e.g., web scraping, APIs). How did you handle the data transformation and visualization?
3. How do you ensure the performance and scalability of Power BI reports when dealing with large datasets? Describe the techniques and best practices you follow.


💡 Tips for Success:
Understand the business context: Tailor your answers to show how your technical skills solve real business problems.
Provide specific examples: Highlight your past experiences with concrete examples.
Stay updated: Continuously learn and adapt to new tools and methodologies.

Hope it helps :)

20 essential Python libraries for data science:

🔹 pandas: Data manipulation and analysis. Essential for handling DataFrames.
🔹 numpy: Numerical computing. Perfect for working with arrays and mathematical functions.
🔹 scikit-learn: Machine learning. Comprehensive tools for predictive data analysis.
🔹 matplotlib: Data visualization. Great for creating static, animated, and interactive plots.
🔹 seaborn: Statistical data visualization. Makes complex plots easy and beautiful.
Data Science
🔹 scipy: Scientific computing. Provides algorithms for optimization, integration, and more.
🔹 statsmodels: Statistical modeling. Ideal for conducting statistical tests and data exploration.
🔹 tensorflow: Deep learning. End-to-end open-source platform for machine learning.
🔹 keras: High-level neural networks API. Simplifies building and training deep learning models.
🔹 pytorch: Deep learning. A flexible and easy-to-use deep learning library.
🔹 mlflow: Machine learning lifecycle. Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
🔹 pydantic: Data validation. Provides data validation and settings management using Python type annotations.
🔹 xgboost: Gradient boosting. An optimized distributed gradient boosting library.
🔹 lightgbm: Gradient boosting. A fast, distributed, high-performance gradient boosting framework.

🔍 Best Data Analytics Roles Based on Your Graduation Background!

Thinking about a career in Data Analytics but unsure which role fits your background? Check out these top job roles based on your degree:

🚀 For Mathematics/Statistics Graduates:
🔹 Data Analyst
🔹 Statistical Analyst
🔹 Quantitative Analyst
🔹 Risk Analyst

🚀 For Computer Science/IT Graduates:
🔹 Data Scientist
🔹 Business Intelligence Developer
🔹 Data Engineer
🔹 Data Architect

🚀 For Economics/Finance Graduates:
🔹 Financial Analyst
🔹 Market Research Analyst
🔹 Economic Consultant
🔹 Data Journalist

🚀 For Business/Management Graduates:
🔹 Business Analyst
🔹 Operations Research Analyst
🔹 Marketing Analytics Manager
🔹 Supply Chain Analyst

🚀 For Engineering Graduates:
🔹 Data Scientist
🔹 Industrial Engineer
🔹 Operations Research Analyst
🔹 Quality Engineer

🚀 For Social Science Graduates:
🔹 Data Analyst
🔹 Research Assistant
🔹 Social Media Analyst
🔹 Public Health Analyst

🚀 For Biology/Healthcare Graduates:
🔹 Clinical Data Analyst
🔹 Biostatistician
🔹 Research Coordinator
🔹 Healthcare Consultant

✅ Pro Tip:

Some of these roles may require additional certifications or upskilling in SQL, Python, Power BI, Tableau, or Machine Learning to stand out in the job market.

Like if it helps ❤️