Coding Community | Data Science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves the use of techniques from statistics, machine learning, and computer science to analyze and interpret data, and can be applied in a wide range of industries, including finance, healthcare, and retail. Data scientists use these tools to make predictions, identify patterns, and inform decision-making.

                                              

 Introduction

• Introduction to Data Science
• What is Data?
• Python for Data Science
• Python Pandas
• Python Numpy
• Python Scikit-learn
• Python Matplotlib

Python Basics

•  Read Python Tutorial - Click here

Data Processing

• Understanding Data Processing
• Python: Operations on Numpy Arrays
• Overview of Data Cleaning
• Slicing, Indexing, Manipulating and Cleaning Pandas Dataframe
• Working with Missing Data in Pandas

Pandas and CSV

• Python | Read CSV
• Export Pandas dataframe to a CSV file

Pandas and JSON

• Pandas | Parsing JSON Dataset
• Exporting Pandas DataFrame to JSON File
• Working with excel files using Pandas

Python Relational Database

• Connect MySQL database using MySQL-Connector Python
• Python: MySQL Create Table
• Python MySQL – Insert into Table
• Python MySQL – Select Query
• Python MySQL – Update Query
• Python MySQL – Delete Query
• Python NoSQL Database
• Python Datetime
• Data Wrangling in Python
• Pandas Groupby: Summarising, Aggregating, and Grouping data
• What is Unstructured Data?
• Label Encoding of datasets
• One Hot Encoding of datasets

Data Visualization

• Data Visualization using Matplotlib
• Style Plots using Matplotlib
• Line chart in Matplotlib
• Bar Plot in Matplotlib
• Box Plot in Python using Matplotlib
• Scatter Plot in Matplotlib
• Heatmap in Matplotlib
• Three-dimensional Plotting using Matplotlib
• Time Series Plot or Line plot with Pandas
• Python Geospatial Data
• Other Plotting Libraries in Python
• Data Visualization with Python Seaborn
• Using Plotly for Interactive Data Visualization in Python
• Interactive Data Visualization with Bokeh

Statistics

• Measures of Central Tendency
• Statistics with Python
• Measuring Variance
• Normal Distribution
• Binomial Distribution
• Poisson Discrete Distribution
• Bernoulli Distribution
• P-value
• Exploring Correlation in Python
• Create a correlation Matrix using Python
• Pearson’s Chi-Square Test

Machine Learning

1. Supervised learning

• Types of Learning – Supervised Learning
• Getting started with Classification
• Types of Regression Techniques
• Classification vs Regression

Linear Regression

• Introduction to Linear Regression
• Implementing Linear Regression
• Univariate Linear Regression
• Multiple Linear Regression
• Python | Linear Regression using sklearn
• Linear Regression Using Tensorflow
• Linear Regression using PyTorch
• Pyspark | Linear regression using Apache MLlib
• Boston Housing Kaggle Challenge with Linear Regression

Polynomial Regression

• Polynomial Regression ( From Scratch using Python )
• Polynomial Regression
• Polynomial Regression for Non-Linear Data
• Polynomial Regression using Turicreate

Logistic Regression

• Understanding Logistic Regression
• Implementing Logistic Regression
• Logistic Regression using Tensorflow
• Softmax Regression using TensorFlow
• Softmax Regression Using Keras

Naive Bayes

• Naive Bayes Classifiers
•  Naive Bayes Scratch Implementation using Python
• Complement Naive Bayes (CNB) Algorithm
• Applying Multinomial Naive Bayes to NLP Problems

Support Vector

• Support Vector Machine Algorithm
• Support Vector Machines(SVMs) in Python
• SVM Hyperparameter Tuning using GridSearchCV
• Creating linear kernel SVM in Python
• Major Kernel Functions in Support Vector Machine (SVM)
• Using SVM to perform classification on a non-linear dataset

Decision Tree

• Decision Tree
• Implementing Decision tree
• Decision Tree Regression using sklearn

Random Forest

• Random Forest Regression in Python
• Random Forest Classifier using Scikit-learn
• Hyperparameters of Random Forest Classifier
• Voting Classifier using Sklearn
• Bagging classifier

K-nearest neighbor (KNN)

• K Nearest Neighbors with Python | ML
• Implementation of K-Nearest Neighbors from Scratch using Python
• K-nearest neighbor algorithm in Python
• Implementation of KNN classifier using Sklearn
• Imputation using the KNNimputer()
• Implementation of KNN using OpenCV

2. Unsupervised Learning

• Types of Learning – Unsupervised Learning
• Clustering in Machine Learning
• Different Types of Clustering Algorithm
• K means Clustering – Introduction
• Elbow Method for optimal value of k in KMeans
• K-means++ Algorithm
• Analysis of test data using K-Means Clustering in Python
• Mini Batch K-means clustering algorithm
• Mean-Shift Clustering
• DBSCAN – Density based clustering
• Implementing DBSCAN algorithm using Sklearn
• Fuzzy Clustering
• Spectral Clustering
• OPTICS Clustering
• OPTICS Clustering Implementing using Sklearn
• Hierarchical clustering (Agglomerative and Divisive clustering)
• Implementing Agglomerative Clustering using Sklearn
• Gaussian Mixture Model

Deep Learning

• Introduction to Deep Learning
• Introduction to Artificial Neutral Networks
• Implementing Artificial Neural Network training process in Python
• A single neuron neural network in Python

Convolutional Neural Networks

• Introduction to Convolution Neural Network
• Introduction to Pooling Layer
• Introduction to Padding
• Types of padding in convolution layer
• Applying Convolutional Neural Network on mnist dataset

Recurrent Neural Networks

• Introduction to Recurrent Neural Network
• Recurrent Neural Networks Explanation
• seq2seq model
• Introduction to Long Short Term Memory
• Long Short Term Memory Networks Explanation
• Gated Recurrent Unit Networks(GAN)
• Text Generation using Gated Recurrent Unit Networks

GANs – Generative Adversarial Network

• Introduction to Generative Adversarial Network
• Generative Adversarial Networks (GANs)
• Use Cases of Generative Adversarial Networks
• Building a Generative Adversarial Network using Keras
• Modal Collapse in GANs

Natural Language Processing

• Introduction to Natural Language Processing
• Text Preprocessing in Python | Set – 1
• Text Preprocessing in Python | Set 2
• Removing stop words with NLTK in Python
• Tokenize text using NLTK in python
• How tokenizing text, sentence, words works
• Introduction to Stemming
• Stemming words with NLTK
• Lemmatization with NLTK
• Lemmatization with TextBlob
• How to get synonyms/antonyms from NLTK WordNet in Python?