How to Fix “ModuleNotFoundError: No module named ‘pdfkit’” in Mr. Holmes (Kali Linux)

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How to Fix “ModuleNotFoundError: No module named ‘pdfkit’” in Mr. Holmes (Kali Linux) Introduction Mr. Holmes is a popular OSINT (Open-Source Intelligence) tool used in ethical hacking and cybersecurity learning. However, many beginners face an error while running the tool on Kali Linux: ModuleNotFoundError: No module named 'pdfkit' This error can be confusing, especially for new users. In this article, you will learn why this error occurs and how to fix it step by step in a clean and safe way . Error Description When trying to run Mr. Holmes using the command: sudo python3 MrHolmes.py You may see the following traceback ending with: ModuleNotFoundError: No module named 'pdfkit' Some users also try to activate a virtual environment and get: source .lib_venv/bin/activate source: no such file or directory Why This Error Happens This issue usually occurs due to one or more of the following reasons: 1. Virtual Environment Was Not Created You attempted to activate a virtual...
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📊 Data Scientist Roadmap for Beginners — Step-by-Step Guide to Start Your Career

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Whether you're from engineering, commerce, science, or complete non-IT background, you can become a data scientist — if you follow the right roadmap.

This blog gives you a clear beginner-friendly roadmap, required skills, tools, projects, and career guidance to become a job-ready data scientist.

🧠 What Is Data Science?

Data Science is the field where we analyze data, find patterns, build models, and make predictions to help companies make better decisions.

Example:
👉 Netflix recommending movies
👉 Amazon showing product suggestions
👉 Banks detecting fraud transactions

All these work because of Data Science + Machine Learning + AI.

🎯 Who Can Become a Data Scientist?

Anyone who has:

✔ Interest in numbers
✔ Logical thinking
✔ Curiosity to solve problems

You do NOT need a computer science degree.

🚀 Complete Data Scientist Roadmap (Beginner → Job Ready)

Follow this roadmap step-by-step:

⭐ Step 1: Learn the Foundations of Math (4–6 Weeks)

Focus on:

  • Statistics

    • Mean, Median, Mode

    • Variance, Standard Deviation

    • Probability, Distribution

    • Correlation vs Causation

  • Linear Algebra

    • Vectors, Matrices

    • Matrix operations (useful for ML)

  • Basic Calculus

    • Derivatives

    • Gradients (for optimization, neural networks)

📌 Tip: You don’t need advanced mathematics in the beginning — only ML-focused math.

⭐ Step 2: Learn Python for Data Science (1–2 Months)

Python is the most popular language in Data Science.

Learn:

  • Basic Syntax, Variables

  • Loops, Conditions

  • Functions

  • File Handling

  • OOP Basics

Then, learn special libraries:

Category Tools
Math NumPy
Data Handling Pandas
Visualization Matplotlib, Seaborn
Machine Learning Scikit-Learn

📌 Mini Projects:

  • Student result analyzer

  • Salary prediction using simple regression

  • Weather data visualization

⭐ Step 3: Learn Data Analysis & Data Cleaning (1 Month)

In real jobs, 80% of time is spent cleaning data, not building models.

Learn:

  • Handling missing values

  • Removing duplicates

  • Encoding categories

  • Scaling and normalization

  • Feature engineering

Tools to use: Pandas + NumPy

⭐ Step 4: Data Visualization (1 Month)

Learn how to convert data into meaningful charts:

Tools:

  • Matplotlib

  • Seaborn

  • Plotly (optional)

  • Power BI or Tableau (recommended for jobs)

📌 Projects:

  • COVID-19 Dashboard

  • Sales trends analysis

  • Population visualization

⭐ Step 5: Machine Learning (2–3 Months)

Learn Machine Learning step-by-step:

📌 Supervised Learning

  • Linear Regression

  • Logistic Regression

  • Decision Trees

  • Random Forest

  • SVM

  • KNN

  • Gradient Boosting

📌 Unsupervised Learning

  • Clustering (K-Means, Hierarchical)

  • Dimensionality Reduction (PCA)

Practice Concepts:

  • Accuracy, Precision, Recall, F1 Score

  • Cross Validation

  • Hyperparameter Tuning

⭐ Step 6: SQL + Databases (2–3 Weeks)

Companies want data scientists who can query databases.

Learn:

  • SELECT, WHERE, ORDER BY

  • GROUP BY, HAVING

  • JOINS

  • Window Functions (important for jobs)

⭐ Step 7: Deep Learning (Optional but Powerful)

If you want to grow into AI Engineer / ML Engineer, learn:

  • Neural Networks

  • TensorFlow or PyTorch

  • CNNs (for image data)

  • NLP (Natural Language Processing for text analysis)

⭐ Step 8: Build Projects & Portfolio (Very Important)

Your portfolio matters more than certificates.

Sample beginner → advanced projects:

Level Project
Beginner EDA on Titanic Dataset
Intermediate House Price Prediction Model
Intermediate Sentiment Analysis on Tweets
Advanced Face Recognition Model
Advanced Stock Price Prediction

Upload projects on:

✔ GitHub
✔ Kaggle
✔ LinkedIn Showcase

⭐ Step 9: Build a Resume & Apply for Jobs

Positions to apply:

  • Data Analyst

  • Machine Learning Intern

  • Business/Data Analyst

  • Junior Data Scientist

🗓 Suggested 6-Month Learning Plan

Month Focus Area
1 Math + Python Basics
2 Python + Pandas + NumPy
3 Visualization + SQL
4 Machine Learning Fundamentals
5 ML Advanced + Projects
6 Portfolio + Resume + Internship Applications

🎓 Free Platforms to Practice

  • Kaggle

  • Google Colab

  • Scikit-Learn Documentation

  • HackerRank (SQL, Python)

💡 Final Advice

✔ Be consistent
✔ Make real projects
✔ Participate in Kaggle competitions
✔ Keep improving your portfolio
✔ Network on LinkedIn

🔥 Data science is a marathon, not a sprint — but if you stay consistent, job opportunities will come.


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