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.ipynb_checkpoints
.ipynb_checkpoints
 
 
Article Published
Article Published
 
 
Data
Data
 
 
Day01 - Anaconda Installation
Day01 - Anaconda Installation
 
 
Day02 - Variable and String
Day02 - Variable and String
 
 
Day03 - Numeric, Boolean and Operator
Day03 - Numeric, Boolean and Operator
 
 
Day04 - List and Tuple
Day04 - List and Tuple
 
 
Day05 - Sets and Dictionary
Day05 - Sets and Dictionary
 
 
Day06 - If-Then-Else and Loop
Day06 - If-Then-Else and Loop
 
 
Day07 - Functions and Lambda Functions
Day07 - Functions and Lambda Functions
 
 
Day08 - Pandas Intro, Read and Write Dataframe
Day08 - Pandas Intro, Read and Write Dataframe
 
 
Day09 - Index, Selection and Assignment
Day09 - Index, Selection and Assignment
 
 
Day10 - Iteration and Sorting
Day10 - Iteration and Sorting
 
 
Day11 - Aggregation and GroupBy
Day11 - Aggregation and GroupBy
 
 
Day12 - Missing Values and Handling Them
Day12 - Missing Values and Handling Them
 
 
Day13 - Rename and Replace
Day13 - Rename and Replace
 
 
Day14 - Merging, Joining and Combine
Day14 - Merging, Joining and Combine
 
 
Day15 - Summary, Crosstab and Pivot
Day15 - Summary, Crosstab and Pivot
 
 
Day16 - Date, Categorical and Sparse Data
Day16 - Date, Categorical and Sparse Data
 
 
Day17 - Pandas Visualizations
Day17 - Pandas Visualizations
 
 
Day18 - Numpy Introduction
Day18 - Numpy Introduction
 
 
Day19 - Indexing, Slicing, Join and Split
Day19 - Indexing, Slicing, Join and Split
 
 
Day20 - Iteration, manipulation, Radom and Distribution
Day20 - Iteration, manipulation, Radom and Distribution
 
 
Day21 - NumPy Operations
Day21 - NumPy Operations
 
 
Day22 - Sort, Search and Filter
Day22 - Sort, Search and Filter
 
 
Day23 - Visualization Part 1 - Basics matplotlib
Day23 - Visualization Part 1 - Basics matplotlib
 
 
Day24 - Visualization Part 2 - Matplotlib
Day24 - Visualization Part 2 - Matplotlib
 
 
Day25 - Visualization Part 3 - Seaborn and Interactive Charts
Day25 - Visualization Part 3 - Seaborn and Interactive Charts
 
 
Day26 - Intro to Stats and Sampling
Day26 - Intro to Stats and Sampling
 
 
Day27 - Descriptive Stats
Day27 - Descriptive Stats
 
 
Day28 - Relationship Stats and Distribution
Day28 - Relationship Stats and Distribution
 
 
Day29 - Central Limit Theorem
Day29 - Central Limit Theorem
 
 
Day30 - Inferential Stats
Day30 - Inferential Stats
 
 
Day36 - DB Concepts - Normalization and ER Design
Day36 - DB Concepts - Normalization and ER Design
 
 
Day37 - Table Creation and Data Loading
Day37 - Table Creation and Data Loading
 
 
Day38 - Basic Queries and Filtering
Day38 - Basic Queries and Filtering
 
 
Day39 - Join and Union
Day39 - Join and Union
 
 
Day40 - SubQueries - Sequencing and Other Functionalities
Day40 - SubQueries - Sequencing and Other Functionalities
 
 
Day41-43 - EDA and Feature Engineering using Titanic Dataset
Day41-43 - EDA and Feature Engineering using Titanic Dataset
 
 
Day44 - SweetViz
Day44 - SweetViz
 
 
Day45 - D-Tale
Day45 - D-Tale
 
 
Day46 - Log Transformation
Day46 - Log Transformation
 
 
Day47 - One Hot Encoding
Day47 - One Hot Encoding
 
 
Day48 - Scaling
Day48 - Scaling
 
 
Day49 - Binning
Day49 - Binning
 
 
Day50 - EDA and Feature Engineering Wrap-up
Day50 - EDA and Feature Engineering Wrap-up
 
 
Day51-52 - Linear Regression Concept and Implementation
Day51-52 - Linear Regression Concept and Implementation
 
 
Day53-54 - Logistic Regression Concepts and Implementation
Day53-54 - Logistic Regression Concepts and Implementation
 
 
Day55-56 - Decision Tree Implementation
Day55-56 - Decision Tree Implementation
 
 
Day57-58 - SVM Concept and Implementation
Day57-58 - SVM Concept and Implementation
 
 
Day59-60 - Random Forest Concept and Implementation
Day59-60 - Random Forest Concept and Implementation
 
 
Day61-62 - Measuring Model Accuracy
Day61-62 - Measuring Model Accuracy
 
 
Day63 - K Fold Cross Validation
Day63 - K Fold Cross Validation
 
 
Day64-65 - Bagging and Boosting
Day64-65 - Bagging and Boosting
 
 
Day66-67 - K Means Clustering
Day66-67 - K Means Clustering
 
 
Day68-69 - Hierarchical Clustering
Day68-69 - Hierarchical Clustering
 
 
Day70-71 - Fuzzy C-Means
Day70-71 - Fuzzy C-Means
 
 
Day72-73 - Market Basket Analysis
Day72-73 - Market Basket Analysis
 
 
.DS_Store
.DS_Store
 
 
README.md
README.md
 
 

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Learn_Data_Science_in_100Days

A step-by-step tutorial to learn Data Science

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A step-by-step tutorial to learn Data Science

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