Feeling the pull of data science? This dynamic field offers exciting opportunities to unlock insights, solve problems, and drive innovation. But where do you start, especially as a complete beginner? Don’t worry – this comprehensive Data Science in 100 Days Roadmap will guide you step-by-step, equipping you with the essential skills and knowledge to embark on your data science journey.
Let’s dive deep…🏊
Contents
Laying the Foundation (Days 1-20)
- Master Python: Dive into the versatile language powering data science. Codecademy’s Learn Python 3 and Coursera’s Python for Everybody Specialization are excellent resources.
- Embrace Essential Libraries: NumPy, pandas, and Matplotlib/Seaborn are your allies. Master data manipulation and visualization with DataCamp’s Python for Data Science course and interactive tools like Google Colaboratory and Jupyter Notebook.
- Get Hands-On: Practice is key! Solve coding exercises, explore personal projects, and join communities like Kaggle and Reddit for support and collaboration. Resources like Automate the Boring Stuff with Python and Python Crash Course can help you navigate this initial phase.
Data Wrangling and Exploration (Days 21-40)
- Tame Your Data: Understand data types (categorical, numerical, etc.) and delve into various sources like CSV, Excel, APIs, and SQL databases. Kaggle and UCI Machine Learning Repository are treasure troves for real-world datasets.
- Clean Up Your Act: Tackle missing values, inconsistencies, and formatting issues with confidence. DataCamp’s Intro to Data Science and edX’s Data Cleaning with Python course equip you with the necessary tools.
- Uncover Hidden Gems: Use descriptive statistics and visualizations (histograms, boxplots, scatter plots) to explore patterns and relationships within your data. Python Data Science Handbook and Data Cleaning with Python offer valuable guidance.
Statistical Thinking and Probability (Days 41-60)
- Become a Stats Whiz: Master key concepts like mean, median, standard deviation, and hypothesis testing. Khan Academy Statistics and edX’s Statistics Fundamentals course solidify your understanding.
- Unlock the Probability Puzzle: Grasp event occurrence, conditional probability, and Bayes’ theorem with resources like Think Stats and Introduction to Probability (MIT OpenCourseware).
- Apply Your Knowledge: Analyze real-world problems, perform hypothesis testing, and interpret results with confidence. DataCamp’s Probability for Data Science course can be a valuable addition.
Machine Learning: Unveiling the Magic (Days 61-80)
- Supervised Learning Demystified: Explore regression (linear, logistic, decision trees), classification (nearest neighbors, support vector machines), and model evaluation metrics. Google’s Machine Learning Crash Course and Hands-On Machine Learning by Aurélien Géron provide solid foundations. Also check Stanford’s Machine Learning course (CS229)
- Unsupervised Learning: See the Unseen: Understand clustering (k-means, hierarchical), dimensionality reduction (PCA), and their applications. Deepen your knowledge with the same Udemy course and Introduction to Machine Learning (MIT OpenCourseware).
- Get Practical: Put theory into action! Implement algorithms in Python using scikit-learn, train models, analyze results, and test your skills in online competitions.
Deep Learning: Dive into the Cutting Edge (Optional: Days 81-100)
- Demystify Neural Networks: Grasp the fundamentals of neurons, layers, activation functions, and loss functions. Fast.ai Practical Deep Learning for Coders and TensorFlow tutorials can guide your exploration.
- Choose Your Weapon: Gain exposure to popular frameworks like TensorFlow, PyTorch, or Keras for building neural networks.
- Challenge Yourself: Tackle beginner-friendly Deep Learning projects (image recognition, text classification) using your chosen framework. Dive into Deep Learning provide solid foundations
Remember
- This is your roadmap, tailor it to your needs and pace.
- Consistency is key. Dedicate focused time daily or weekly to learning and practice.
- Embrace mistakes. They are stepping stones to growth.
- Seek help. Don’t hesitate to ask questions in online communities or find a mentor.
- Showcase your skills. Build a portfolio of personal projects and share them on GitHub or portfolio websites.
- Network with others in the field. Collaboration and support are invaluable.
- Stay up-to-date. The field evolves rapidly, so keep learning and exploring new trends.
For More Data Science Related Posts and Resources Check out: https://deepakjosecodes.com/category/data-science/
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