Python Tutorials for Data Science
Data Science Basics, Tutorials and Functions
Python Basics
Introduction to Python
- Data Types - Built-in Functions - Type Converting - Getting Input from users
Data Structures
- Lists - Tuples - Dictionaries - Sets
Conditional Statements
- Boolean Expressions - Logical Operators - If-Else - Grade System User Interaction Example - Nested If - Odd or Even Example
Loops
- range() - In Operator - For Loop - Iterating in Strings - Iterating in two(2) dimensional Lists - continue - break - zip() - Iteration in a Dictionary - Iterating pair values - While Loop - While True
Functions
- Intro to Functions - return() - Number of Arguments - Arbitrary Arguments, *args - Arbitrary Keyword Arguments, **kwargs - Giving output with Information - Functions that have 2 parameters - Predefined Parameters in Functions - Local and Global Variables - Changing global variables in local area - Pass Statement
Nested Functions
Object Oriented Programming
- What is object oriented programming? - Defining Classes - Instantiation - Creating objects - Class and Instance Attributes - Instance(Object) Methods - Inheritance - Overriding - Extending the Functionality of a Parent Class - super() keywordNumpy
- What is Numpy? - Importing Numpy - Numpy arrays and Dimensions - Creating Numpy Arrays - Zero arrays - Ones arrays - Full arrays - Identify Matrixes - Linear Series - Distributions arrays - Random - Array Indexing - Subsets - reshape() function - Flattening the Arrays - Concatenation - Splitting - Sorting - Broadcasting - Array Math - Dot(Scalar) Product #### Pandas - What is Pandas? - Importing Pandas Library - Pandas Series - Pandas Dataframes - Filtering - Adding/Removing rows and columns - Merging Dataframes - Sorting - Aggregation Functions - Grouping - Apply - Pivot Tables - Missing values(NaN) - Working external files in Pandas(csv,excel) - Exploring Netflix Dataset(basic)
#### Data Preprocessing-Cleaning - Data Cleaning / Cleasing - Noisy Data - Missing Data Analysis - Outlier Detection - Data Standardization / Feature Scaling - Normalization(0-1 Scaling) - Standardization(Z Score Scaling) - Min-Max Scaling - Binary Transformation - Variable Transformation - Label Encoding - One Hot Encoding
#### Data Visualization
- Main Libraries for Data Visualisation - What is Exploratory data analysis(EDA)? - Importing Libraries - Matplotlib - Pyplot - Line Plot - Bar Plot - Pie Chart - Stack Plot - Histograms - Scatter Plot - Time Series Plotting - Box Plot - Heatmap - Seaborn - Pyplot - Line Plot - Bar Plot - Cat Plot - Histograms - Density Plots - Pair Plot - Scatter Plot - Time Series Plotting - Box Plot - Heatmap - Multi-plot Grids - Pandas - Basic Plots - Bar Plots - Histograms - Box Plots - Area Plots - Scatter Plots - Hexagonal Bin Plots - Pie Plots - Plotting Tools - Plotnine - ggplot - Line Plot - Bar Plot - Scatter Plot - Histograms - Density Plot - Box Plot - Violin Plot - Plotly - Line Plot - Bar Plot - Pie Charts - Bubble Charts - Scatter Plots - Filled area Plots - Gannt Charts - Sunburst Charts - Tables #### Linear Methods for Regression - What is Linear Regression? - Simple Linear Regression (Theory - Model- Tuning) - Multiple Linear Regression (Theory - Model- Tuning) - Least-Squares Regression(Ordinary Least Squares) (Theory - Model- Tuning) - Principal Component Analysis (PCA) - Principal component regression(PCR) (Theory - Model- Tuning) - Shrinkage(Regularization) Methods - Partial Least Squares (Theory - Model- Tuning) - Ridge Regression(L2 Regularization) (Theory - Model- Tuning) - Lasso Regression(L1 Regularization) (Theory - Model- Tuning) - Elastic Net Regression (Theory - Model- Tuning) #### Non-Linear Models for Regression
- K - Nearest Neighbors(KNN) (Theory - Model- Tuning) - Support Vector Regression(SVR) (Theory - Model- Tuning) - Non-Linear Support Vector Regression(SVR) (Theory - Model- Tuning) - Regression(Decision) Trees (CART) (Theory - Model- Tuning) - Ensemble Learning - Bagged Trees(Bagging) (Theory - Model- Tuning) - Ensemble Learning - Random Forests (Theory - Model- Tuning) - Gradient Boosting Machines(GBM) (Theory - Model- Tuning) - Light Gradient Boosting Machines(LGBM) (Theory - Model- Tuning) - XGBoost(Extreme Gradient Boosting) (Theory - Model- Tuning) - Catboost (Theory - Model- Tuning)
#### Unsupervised Learning - Clustering - Principal Components Analysis(PCA)
- Clustering - K-Means Clustering (Theory - Exploratory Data Analysis - Preprocessing - Model- Tuning) - Color - Image Quantization - Hierarchical Clustering (Theory - Model) - DBSCAN (Density-based spatial clustering) (Theory - Model- Tuning) - Principal Components Analysis(PCA) (Theory - Manual Implementation of PCA - Model) #### Classification
- Classification and Evaluation Metrics - Logistic Regression (Theory - Model- Tuning) - K - Nearest Neighbors(KNN) (Theory - Model- Tuning) - Support Vector Machines(SVC) - Linear Kernel (Theory - Model- Tuning) - Support Vector Machines(SVC) - Radial Basis Kernel (Theory - Model- Tuning) - Decision Tree Classification (Theory - Model- Tuning) - Ensemble Learning - Random Forests Classification (Theory - Model- Tuning) - Naive Bayes Classification (Theory - Model) - GBM(Gradient Boosting Machines) Classification (Model- Tuning) - XGBoost(Extreme Gradient Boosting) Classification (Theory - Model- Tuning) - LGBM(Light Gradient Boosting Machines) Classification (Theory - Model- Tuning) #### Deep Learning with Pytorch - What is Pytorch? - Importing Libraries - Basics of Pytorch - Tensors - Math Operations - Common Funtions - Variables - Autograd - Datasets & DataLoaders - Common Modules: Optim - nn - Extra - Useful Resources #### Model Deployment - What is Joblib Library? - Artificial Neural Networks(ANN) Model - Prediction - Model Tuning & Validation - Saving Model as pickle file - Loading Model #### Natural Language Proccessing - NLP Intuition - String Essentials : Creating String - String Essentials : Querying of Types - String Essentials : Reaching to Indexes - String Essentials : First and last characters - String Essentials : Splitting Characters - String Essentials : Case Conversions in String - String Essentials : Capitalizing and titles - String Essentials : Cropping Characters - String Essentials : Joining Strings - String Essentials : Replacing Characters - String Essentials : contains - Text Preprocessing : Converting string to other data types - Text Preprocessing : Case Conversion - Text Preprocessing : Handling with Punctuation - Text Preprocessing : Handling with Numbers - Text Preprocessing : Handling with Stopwords - Text Preprocessing : Handling with Frequnecies - Text Preprocessing : Tokenization - Text Preprocessing : Stemming - Text Preprocessing : Lemmatization - Object Standardization - Linguistic Features : N-Gram - Linguistic Features : Part of speech tagging (POS) - Linguistic Features : Chunking(Shallow Parsing) - Linguistic Features : Noun Chunks - Linguistic Features : Named Entity Recognition(NER) - Linguistic Features : Visualization in Spacy - Text Feature Engineering - Bag of Words - Text Visualisation : Bar Plot - Text Visualisation : Frequency Visualisation - Text Visualisation : WordCloud - Transformers, Encoders and Decoders - Different Models : Bert, HuggingFace, StanfordNLP, NLTK, LSTM etc. - Sentiment Analysis with Logistic Regression - Sentiment Analysis with Naive Bayes - Vector Space Models - Neural Machine Translation - Text Summarization - Classification with Bert ### Spark
- Spark Basics - MlLib
