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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 dat

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