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Data Science with Python

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01

Edxera Introduction

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02

Environment Setup

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03

Python Basics Variables and Types

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04

Python Operators

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05

Control Flow Conditionals

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Control Flow Loops

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Python Lists

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Python Dictionaries

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09

Tuples and Sets

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10

Python Functions

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Advanced Functions

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12

List Comprehensions

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File Handling in Python

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Error and Exception Handling

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Modules and Packages

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Introduction to Linear Algebra

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Statistics Fundamentals

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18

Introduction to NumPy

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NumPy Indexing and Slicing

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NumPy Operations and Broadcasting

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Introduction to Pandas

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Data Exploration in Pandas

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Pandas Filtering and Sorting

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Handling Missing Data

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Data Grouping

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Merging and Joining DataFrames

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Applying Functions to DataFrames

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Date and Time Handling

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Advanced Pandas Techniques

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30

Introduction to Data Visualization

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Matplotlib Basics

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Matplotlib Customization

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Advanced Matplotlib

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Introduction to Seaborn

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Seaborn Categorical Plots

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Seaborn Distribution Plots

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Correlation and Heatmaps

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Interactive Visualizations

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Exploratory Data Analysis Part 1

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Exploratory Data Analysis Part 2

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Outlier Detection and Treatment

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Feature Engineering Basics

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Categorical Encoding

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Feature Scaling

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Text Data Processing in Pandas

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Introduction to Machine Learning

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Supervised Learning Concepts

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48

Model Evaluation Metrics

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49

Data Splitting Techniques

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Introduction to Scikit-Learn

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51

Simple Linear Regression

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Multiple Linear Regression

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Polynomial Regression

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Logistic Regression Basics

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Advanced Logistic Regression

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K-Nearest Neighbors Classification

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Decision Trees for Classification

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Decision Trees for Regression

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Support Vector Machines Basics

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Advanced Support Vector Machines

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Ensemble Learning Basics

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Random Forests Classification

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Random Forests Regression

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Gradient Boosting Machines

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XGBoost Introduction

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Hyperparameter Tuning

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Advanced Tuning Techniques

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Cross-Validation Strategies

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Introduction to Unsupervised Learning

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K-Means Clustering

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The Elbow Method and Silhouette Score

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Hierarchical Clustering

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DBSCAN Clustering

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Principal Component Analysis

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Advanced PCA Applications

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Association Rule Learning

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Recommendation Systems Basics

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Collaborative Filtering

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Introduction to Neural Networks

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Neural Network Training

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Introduction to Keras and TensorFlow

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Deep Learning Classification

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Preventing Overfitting in Neural Networks

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Introduction to Computer Vision

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Convolutional Neural Networks

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Image Classification with CNNs

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Introduction to Natural Language Processing

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88

Text Vectorization

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89

Sentiment Analysis Project

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Time Series Forecasting Basics

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91

Introduction to R Programming

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92

Data Structures in R

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93

Data Manipulation with Dplyr

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Data Visualization with Ggplot2

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Statistical Modeling in R

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96

Building a Data Science Portfolio

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Capstone Project Planning

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98

Data Acquisition and Cleaning

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Capstone EDA and Feature Engineering

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Capstone Model Selection

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Capstone Model Tuning

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102

Building a Data Dashboard

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103

Advanced Dashboard Features

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104

Communicating Data Insights

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105

Resume Optimization for Data Science

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106

LinkedIn Profile Development

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107

Navigating the Job Market

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108

Technical Interview Preparation

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109

Statistical Interview Questions

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110

Machine Learning Interview Questions

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111

Behavioral Interviews

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112

Cloud Computing Fundamentals

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113

Introduction to Git and Version Control

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114

Introduction to Docker

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115

Model Deployment via API

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116

Ethical Data Science

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117

Continuous Learning Strategies

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118

Advanced NLP and Transformers Overview

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119

Advanced Deep Learning Applications

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120

Final Course Review and Next Steps

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