Day 18 – Ds project – Amazon Recommendation system
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*21 DAYS AGENDA FOR THE DATA SCIENCE*:
Day 1: Introduction, Data collection
Day 2: Pandas Library
Day 3: NumPy and data concepts
Day 4: Matplotlib, Plotly
Day 5: PowerBi project – Population dataset
Day 6: Data analysis project on Netflix data
Day 7: Statistics 1 – Sampling, Randomization, Frequency histogram and
distribution, time series, bar and pie graphs
Day 8: Statistics 2 – Frequency table and stem and leaf, central tendency,
variation measure, percentile and box – whisker plot, scatter diagram
Day 9: Statistics 3 – Linear correlation, normal distribution, empirical rule,
z-score and probabilities, central limit theorem
Day 10: SQL for Data Science
Day 11: Machine learning for data science – pathway – preprocessing (scaling,
encoding, null handling)
Day 12: ML – outlier detection & handling, feature selection, feature extraction
Day 13: ML – train_test_split, hyperparameter optimization, Sklearn pipeline
Day 14: ML – performance comparison, A/B testing
Day 15: ML – Classification project – Customer churn prediction
Day 16: ML – Regression project – Supply Chain Optimization for a FMCG
Company
Day 17: ML – clustering algorithms and project – Retail Customer Purchase
Behavior clustering
Day 18: Ds project – Amazon Recommendation system
Day 19: Ds project – Detecting Malicious URLs project.
Day 20: Ds project – Amazon product review
Day 21: Ds project – Time series analysis on stock data
Data Science is the convergence of computer science, mathematics, and business expertise and helps entrepreneurs predict, diagnose, and solve their problems. Learn the basics of the NumPy library in this tutorial for beginners. It provides background information on how NumPy works and how it compares to Python’s Built-in lists.
This masterclass provides you with insights into the tools, methodologies, and competencies employed by data scientists daily to tackle real-world challenges. Discover the secrets behind data analysis, machine learning, and so much more, led by industry experts. 🧠💼
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