├── .gitignore ├── Assignment ├── README.md └── titles.ipynb ├── Data Visualization ├── Matplotlib │ ├── DV2021EN-0101-Basic Plotting.ipynb │ ├── DV2021EN-0102-Basic Color and Line Styles.ipynb │ ├── DV2021EN-0103-Simple Line Plots with Line Styles.ipynb │ ├── DV2021EN-0104-Simple Scatter Plots.ipynb │ ├── DV2021EN-0105-Errorbars.ipynb │ ├── DV2021EN-0106-Density_and_Contour_Plots.ipynb │ ├── DV2021EN-0107-Histograms_and_Binnings.ipynb │ ├── DV2021EN-0108-Customizing_Legends.ipynb │ ├── Hough-Circle-Transform-Opencv.py │ ├── MP0101- Matplotlib Complite .ipynb │ ├── README.md │ ├── Scatter plot.ipynb │ ├── barh.py │ ├── img │ │ └── Text.png │ ├── pieplot.py │ ├── plot_camera_numpy.py │ ├── polar_scatter.ipynb │ ├── sample_plots.ipynb │ ├── water Mark.ipynb │ └── watermark_image.py ├── README.md └── SeaBorn │ └── README.md ├── Data ├── Boston │ ├── README.md │ ├── housing.data.txt │ └── housing.names.txt ├── README.md ├── iris │ ├── README.md │ └── iris.csv └── titles.csv ├── LICENSE ├── Life Cycle Process of Data Science In Real World project ├── DSPD0101ENT-Business Understanding(Problem)-to-Analytic Approach.ipynb ├── DSPD0101ENT-Business Understanding.ipynb ├── DSPD0102ENT-Understanding-to-Preparation.ipynb ├── DSPD0103ENT-Requirements-to-Collection-py.ipynb ├── DSPD0104ENT-Modeling-to-Evaluation-py.ipynb ├── IBMOpenSource_FoundationalMethologyforDataScience.PDF └── README.md ├── Modeling ├── README.md ├── Semi Supervised Learning │ └── README.md ├── Supervised Learning │ └── README.md └── Unsupervised Learning │ └── README.md ├── Numpy ├── README.md └── img │ ├── README.md │ └── Where we use numpy.png ├── Pandas ├── DSPD0100ENT-Business Understanding.ipynb ├── DSPD0101EN-Introduction-to-Pandas.ipynb ├── DSPD0102EN-Pandas Series,Data Frame and Index.ipynb ├── DSPD0103EN-Data Collection & Data Source.ipynb ├── DSPD0104EN-Data_Loading.ipynb ├── DSPD0105EN-Missing-Values.ipynb ├── DSPD0106EN - Loading data from SQL databases.ipynb ├── DSPD0107EN-Operations-in-Pandas.ipynb ├── DSPD0108EN-Working-With-Strings.ipynb ├── DSPD0109EN-Hierarchical-Indexing.ipynb ├── DSPD0110EN-Merge-and-Join.ipynb ├── DSPD0111-Handling Missing Values with Numpy and Pandas.ipynb ├── Predicting_Credit_Risk_Model_Pipeline.ipynb └── README.md ├── README.md └── Statistics ├── DS0101-summary-stats.ipynb ├── DS0102-Exploratory Data Analysis(EDA).ipynb ├── DS0103-Probability Density Functions(pdf).ipynb ├── DS0104-Probability Mass Functions.ipynb ├── DS0105-Hypothesis-Testing.ipynb ├── DS0106-Bootstrapping.ipynb ├── DS0107-Covariance and Correlation.ipynb ├── DS0108-Linear-Reqression-LeastSquares.ipynb ├── DS0109-Data Distributions.ipynb ├── DS0110-Probability distributions.ipynb ├── Data └── README.md ├── Practice ├── 01 - Day 0 - Mean, Median, and Mode.py ├── 02 - Day 0 - Weighted Mean.py ├── 03 - Day 1 - Quartiles.py ├── 04 - Day 1 - Interquartile Range.py ├── 05 - Day 1 - Standard Deviation.py ├── 06 - Day 2 - Basic Probability.py ├── 07 - Day 2 - More Dice.py ├── 08 - Day 2 - Compound Event Probability.py ├── 09 - Day 3 - Conditional Probability.py ├── 10 - Day 3 - Cards of the Same Suit.txt ├── 11 - Day 3 - Drawing Marbles.py ├── 12 - Day 4 - Binomial Distribution I.py ├── 13 - Day 4 - Binomial Distribution II.py ├── 14 - Day 4 - Geometric Distribution I.py ├── 15 - Day 4 - Geometric Distribution II.py ├── 16 - Day 5 - Poisson Distribution I.py ├── 17 - Day 5 - Poisson Distribution II.py ├── 18 - Day 5 - Normal Distribution I.py ├── 19 - Day 5 - Normal Distribution II.py ├── 20 - Day 6 - The Central Limit Theorem I.py ├── 21 - Day 6 - The Central Limit Theorem II.py ├── 22 - Day 6 - The Central Limit Theorem III.py ├── 23 - Day 7 - Pearson Correlation Coefficient I.py ├── 24 - Day 7 - Spearman's Rank Correlation.py ├── 25 - Day 8 - Least Sqaure Regression Line.py ├── 26 - Day 8 - Pearson Correlation Coefficient II.txt ├── 27 - Day 9 - Multiple Linear Regression.py └── Readme.md └── README.md /.gitignore: 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