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Complete Data Science Training with Python for Data Analysis
Introduction to the Data Science in Python Bootcamp
What is Data Science? (3:37)
Introduction to the Course & Instructor (11:34)
Data and Scripts for the Course
Introduction to the Python Data Science Tool (10:57)
For Mac Users (4:05)
Introduction to the Python Data Science Environment (19:15)
Some Miscellaneous IPython Usage Facts (5:25)
Online iPython Interpreter (3:26)
Conclusion to Section 1 (2:36)
Introduction to Python Pre-Requisites for Data Science
Different Types of Data Used in Statistical & ML Analysis (3:37)
Different Types of Data Used Programatically (3:46)
Python Data Science Packages To Be Used (3:16)
Conclusion to Section 2 (1:59)
Introduction to Numpy
Numpy: Introduction (3:46)
Create Numpy Arrays (10:51)
Numpy Operations (16:48)
Matrix Arithmetic and Linear Systems (7:34)
Numpy for Basic Vector Arithmetic (6:16)
Numpy for Basic Matrix Arithmetic (5:16)
Broadcasting for Numpy (3:52)
Solve for Equations (5:04)
Numpy For Statistics (7:23)
Conclusions to Section 3 (2:24)
Introduction to Pandas
What are Pandas? (12:06)
Read CSV Data in Python (5:42)
Read in Excel File (5:31)
Read HTML Data (12:06)
Read JSON Data (3:09)
Conclusions to Section 4 (2:06)
Data Pre-Processing/Wrangling
Rationale behind this section (4:19)
Remove NA Values (10:28)
Basic Data Handling: Starting with Conditional Data Selection (5:24)
Drop Column/Row (4:42)
Subset and Index Data (9:44)
Basic Data Grouping Based on Qualitative Attributes (9:47)
Crosstabulation (4:54)
Reshaping (9:26)
Pivotting (8:30)
Rank and Sort Data (8:03)
Concatenate (8:16)
Merge (10:47)
Conclusion to Section 5
Introduction to Data Visualization
What is Data Visualisation? (9:33)
Theory Behind Data Visualisation (6:46)
Histograms- Visualise the Distribution of Quantitative Variables (12:13)
Boxplot- Visualise the Data Summary (5:54)
Scatterplot- Visualise The Relationship Between Quantitative Variables (11:57)
Line Chart (12:31)
Barplot (22:25)
Pie Chart (5:29)
Conclusion to Section 6 (2:14)
Basic Statistical Data Analysis
What is Statistical Data Analysis? (10:08)
Some Pointers on Collecting Data for Statistical Studies (8:38)
Explore the Quantitative Data: Descriptive Statistics (9:05)
Group By Qualitative Categories (10:25)
Visualize Descriptive Statistics-Boxplots (5:28)
Common Terms Relating to Descriptive Statistics (5:15)
Data Distribution- Normal Distribution (4:07)
Check for Normal Distribution (6:23)
Standard Normal Distribution and Z-scores (4:10)
Confidence Interval-Theory (6:06)
Confidence Interval-Calculation (5:20)
Conclusion to Section 7 (1:28)
Statistical Inference & Relationship Between Variables
What is Hypothesis Testing? (5:42)
Test the Difference Between Two Groups (7:30)
Test the Difference Between More Than Two Groups (10:55)
Explore the Relationship Between Two Quantitative Variables (4:26)
Correlation Analysis (8:26)
Linear Regression-Theory (10:44)
Linear Regression-Implementation in Python (11:18)
Conditions of Linear Regression-Check in Python (12:03)
Polynomial Regression (3:53)
GLM: Generalized Linear Model (5:25)
Logistic Regression (11:10)
Conclusion to Section 8 (1:52)
Machine Learning for Data Science
How is Machine Learning Different from Statistical Data Analysis? (11:12)
What is Machine Learning (ML) About? Some Theoretical Pointers (5:32)
Unsupervised Learning
Some Basic Pointers (1:38)
kmeans-theory (2:31)
KMeans-implementation on the iris data (8:01)
Quantifying KMeans Clustering Performance (3:53)
kmeans clustering on real data (4:16)
How Do We Select the Number of Clusters? (5:38)
Theory of hierarchical clustering (4:10)
Implement hierarchical clustering (9:19)
Theory of Principal Component Analysis (PCA) (2:37)
Implement PCA (3:52)
Conclusion to Section 10 (2:08)
Data Preparation for Supervised Classification (9:47)
Classification accuracy evaluation (9:42)
Random Forest (RF) For Regression (9:20)
Supervised Learning
What is this section about? (10:10)
Logistic regression with classification (8:26)
Random Forest (RF) For Classification (12:02)
Linear Support Vector Machine (SVM) Classification (3:10)
Non-Linear Support Vector Machine (SVM) Classification (2:06)
Support Vector Regression (4:30)
kNN Classification (7:46)
kNN Regression (3:48)
Gradient Boosting Machine (GBM) Classification (5:54)
GBM Classification
Gradient Boosting Regression (GBR) (4:46)
Voting Classifier (4:00)
Conclusion to Section 11 (2:46)
Artificial Neural Networks (ANN) and Deep Learning
Introduction
Perceptrons for Binary Classification (4:27)
Getting Started with ANN-binary classification (3:26)
Multi-label classification with MLP (4:53)
Regression with MLP (3:48)
MLP with PCA on a Large Dataset (7:33)
Start With Deep Neural Network (DNN)
Start with H20 (4:14)
Default H2O Deep Learning Algorithm (3:20)
Specify the Activation Function (2:06)
Deep Learning Predictions (5:02)
Conclusion to section 12 (2:03)
KMeans-implementation on the iris data
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