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Susan Wairimu
Susan Wairimu

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Data Science for Beginners: A 12-Month Complete Roadmap

Embarking on the path of data science is an exciting and rewarding adventure. Over the next 12 months, you'll delve into the world of data, algorithms, and insights. Let's break down your roadmap into manageable monthly milestones.

Month 1-2: Lay the Foundation

Week 1-2: Introduction to Data Science

Explore the fundamentals of data science.
Understand key concepts: data, variables, and basic statistical measures.

Week 3-4: Learn Python and Jupyter Notebooks

Python is the go-to language for data science.
Familiarize yourself with Jupyter Notebooks, a popular environment for data analysis.

Month 3-4: Dive into Data Manipulation and Visualization

Week 5-6: Pandas Library

Master data manipulation with Pandas, a powerful Python library.
Practice handling data frames and series.

Week 7-8: Matplotlib and Seaborn

Learn data visualization using Matplotlib and Seaborn.
Create insightful plots to understand patterns in data.

Month 5-6: Statistical Foundations

Week 9-10: Descriptive Statistics

Explore measures of central tendency, dispersion, and skewness.
Learn how to summarize and interpret data.

Week 11-12: Inferential Statistics

Dive into hypothesis testing, confidence intervals, and p-values.
Understand the basics of statistical inference.

Month 7-8: Machine Learning Basics

Week 13-14: Introduction to Machine Learning

Explore the concepts of supervised and unsupervised learning.
Understand the difference between regression and classification.

Week 15-16: Scikit-Learn Library

Get hands-on experience with Scikit-Learn, a machine learning library in Python.
Implement simple models for classification and regression.

Month 9-10: Advanced Machine Learning

Week 17-18: Feature Engineering

Learn to preprocess data and create meaningful features.
Understand the importance of feature selection.

Week 19-20: Model Evaluation and Hyperparameter Tuning

Dive deeper into model evaluation metrics.
Explore techniques for tuning hyperparameters.

Month 11-12: Real-World Projects and Specializations

Week 21-22: Kaggle Competitions

Participate in Kaggle competitions to apply your skills.
Learn from the Kaggle community and gain real-world experience.

Week 23-24: Choose a Specialization

Decide on a data science specialization (e.g., natural language processing, computer vision).
Explore advanced topics in your chosen area.

Congratulations on completing your 12-month data science roadmap! Remember, the key to success is consistent practice and curiosity. As you celebrate your first year in data science, reflect on your achievements and look forward to continued growth in this dynamic and ever-evolving field. Happy learning!

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