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.
Explore the fundamentals of data science.
Understand key concepts: data, variables, and basic statistical measures.
Python is the go-to language for data science.
Familiarize yourself with Jupyter Notebooks, a popular environment for data analysis.
Master data manipulation with Pandas, a powerful Python library.
Practice handling data frames and series.
Learn data visualization using Matplotlib and Seaborn.
Create insightful plots to understand patterns in data.
Explore measures of central tendency, dispersion, and skewness.
Learn how to summarize and interpret data.
Dive into hypothesis testing, confidence intervals, and p-values.
Understand the basics of statistical inference.
Explore the concepts of supervised and unsupervised learning.
Understand the difference between regression and classification.
Get hands-on experience with Scikit-Learn, a machine learning library in Python.
Implement simple models for classification and regression.
Learn to preprocess data and create meaningful features.
Understand the importance of feature selection.
Dive deeper into model evaluation metrics.
Explore techniques for tuning hyperparameters.
Participate in Kaggle competitions to apply your skills.
Learn from the Kaggle community and gain real-world experience.
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!