Complete Roadmap to Learn Data Science in 2 months | by Data Analytics | May, 2024
## Week 1-2: Foundations and Python Programming
Day 1-3: Introduction to Data Science
– Understand what data science is, its applications, and its importance.
– Learn about the data science lifecycle: data collection, cleaning, exploration, modeling, and interpretation.
Day 4-7: Python Programming
– Learn Python basics: syntax, data types, and structures (lists, dictionaries, sets, and tuples).
– Practice control flow: if statements, for and while loops.
– Introduction to functions and modules.
Day 8-14: Python for Data Science
– Libraries: NumPy (arrays, mathematical functions), Pandas (data manipulation and analysis), Matplotlib/Seaborn (data visualization).
– Work on mini-projects to apply what you’ve learned: data cleaning and visualization projects using real datasets (e.g., from Kaggle).
## Week 3-4: Statistics and Data Wrangling
Day 15-18: Statistics Basics
– Descriptive statistics: mean, median, mode, variance, standard deviation.
– Probability basics: distributions (normal, binomial, Poisson).
– Inferential statistics: hypothesis testing, confidence intervals.
Day 19-21: Data Wrangling
– Advanced Pandas techniques: merging, grouping, pivoting, and reshaping data.
– Handling missing data, outliers, and data transformation.
Day 22-28: Exploratory Data Analysis (EDA)
– Visualizing data distributions and relationships using Seaborn and Matplotlib.
– Summarizing data insights through visualizations and statistical summaries.
– Mini-project: Perform EDA on a new dataset and summarize key findings.
## Week 5-6: Machine Learning Basics
Day 29-32: Introduction to Machine Learning
– Understanding supervised vs. unsupervised learning.
– Overview of common algorithms: linear regression, logistic regression, decision trees, K-nearest neighbors, and clustering.
Day 33-37: Supervised Learning
– In-depth study of regression and classification algorithms.
– Hands-on practice with Scikit-Learn: building, training, and evaluating models.
– Mini-project: Implement a regression and classification problem.
Day 38-42: Unsupervised Learning
– Clustering techniques: K-means, hierarchical clustering.
– Dimensionality reduction: PCA (Principal Component Analysis).
– Mini-project: Apply clustering to a dataset.
## Week 7: Model Evaluation and Advanced Topics
Day 43-45: Model Evaluation and Improvement
– Metrics for evaluation: accuracy, precision, recall, F1 score, ROC curve.
– Cross-validation, hyperparameter tuning, and model selection techniques.
Day 46-49: Advanced Machine Learning Topics
– Introduction to ensemble methods: bagging, boosting (e.g., Random Forest, XGBoost).
– Basics of neural networks and deep learning (overview, not deep dive).
Day 50-51: Time Series Analysis (Optional)
– Basics of time series data, moving averages, and ARIMA models.
## Week 8: Capstone Project and Review
Day 52-56: Capstone Project
– Select a comprehensive dataset (e.g., from Kaggle).
– Apply the entire data science process: data cleaning, EDA, model building, and evaluation.
– Document your findings and create a presentation.
Day 57-60: Review and Consolidation
– Review key concepts and techniques.
– Practice with additional datasets and problems.
– Prepare for interviews if you are job-seeking: practice common data science interview questions.
Best Resources to learn Data Science 👇👇
developers.google.com/machine-learning/crash-course
This roadmap is for people who have prior understanding with programming and statistics. But if that’s not the case, then it may take more time for you to cover up some topics.
Don’t worry even if it’s taking time. You’ll become good with it as you remain consistent and dedicated. Every step forward, no matter how small, brings you closer to your goal. Keep pushing, stay patient, and trust the process—success is built on perseverance.
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ENJOY LEARNING👍👍
