| 1 - How Python Thinks Execution Flow & Memory Management.mp4 | 162.1 MB | ||
| 1 - Introduction to Data Visualization.mp4 | 9.2 MB | ||
| 1 - Introduction to EDA.mp4 | 65.9 MB | ||
| 1 - Introduction to Feature Engineering.mp4 | 44.7 MB | ||
| 1 - Introduction to Statistics.mp4 | 11.2 MB | ||
| 1 - Introduction to Vectors.mp4 | 37 MB | ||
| 1 - Introduction.mp4 | 9.3 MB | ||
| 1 - Thank You.mp4 | 532.6 KB | ||
| 10 - Common Statistical Terms You Should Know.mp4 | 41 MB | ||
| 10 - Data Analysis.mp4 | 116.8 MB | ||
| 10 - MICE Imputation.mp4 | 48.4 MB | ||
| 10 - Python File Handling.mp4 | 122.1 MB | ||
| 10 - Visualizing Categorical Data with Count Plots.mp4 | 12.8 MB | ||
| 10 - What is the Chain Rule.mp4 | 9.2 MB | ||
| 11 - Feature Engineering in a Dataset.mp4 | 46.4 MB | ||
| 11 - Finding Patterns in a Dataset Using Data Visualization.mp4 | 46.5 MB | ||
| 11 - How to Detect Outliers in a Dataset.mp4 | 41.7 MB | ||
| 11 - Python Multiprocessing.mp4 | 96.8 MB | ||
| 11 - Z-Scores in Statistics.mp4 | 40.4 MB | ||
| 12 - Asyncio in Python.mp4 | 182.9 MB | ||
| 12 - Data Leakage Explained.mp4 | 32.9 MB | ||
| 12 - Feature Construction Explained.mp4 | 7.4 MB | ||
| 12 - Non-Gaussian Distribution Explained.mp4 | 91.8 MB | ||
| 13 - Binomial and Poisson Distribution.mp4 | 62.7 MB | ||
| 13 - List & Dictionary Comprehension in Python.mp4 | 91.7 MB | ||
| 14 - Decorator & Generator in Python.mp4 | 110.7 MB | ||
| 14 - What is Central Limit Theorem.mp4 | 8.9 MB | ||
| 15 - Confidence Interval in Statistics.mp4 | 22 MB | ||
| 15 - Lambda Function in Python.mp4 | 16.4 MB | ||
| 16 - Hypothesis Testing.mp4 | 78 MB | ||
| 16 - Map, Filter & Reduce in Python.mp4 | 25 MB | ||
| 17 - Python Virtual Environment Setup.mp4 | 109.3 MB | ||
| 17 - T-Test and ANOVA.mp4 | 57.9 MB | ||
| 2 - EDA vs Feature Engineering.mp4 | 33.7 MB | ||
| 2 - Levels of Measurement in Statistics.mp4 | 25 MB | ||
| 2 - Project Setup.mp4 | 18.3 MB | ||
| 2 - Python Installation.mp4 | 15.2 MB | ||
| 2 - Types of Vectors.mp4 | 16.9 MB | ||
| 2 - What is Standardization.mp4 | 44.7 MB | ||
| 3 - Bar Chart Explained.mp4 | 34.2 MB | ||
| 3 - Mean, Median & Mode.mp4 | 26.8 MB | ||
| 3 - Project Setup.mp4 | 22.6 MB | ||
| 3 - Python Variables.mp4 | 59.2 MB | ||
| 3 - Vector Addition.mp4 | 9.8 MB | ||
| 3 - What is Normalization.mp4 | 19.5 MB | ||
| 4 - Downloading Datasets from Kaggle.mp4 | 32.9 MB | ||
| 4 - Exploring the Dataset.mp4 | 53.5 MB | ||
| 4 - Ordinal Encoding Explained.mp4 | 19.1 MB | ||
| 4 - Python Loops.mp4 | 41.6 MB | ||
| 4 - What is Probability Distribution Function.mp4 | 33.8 MB | ||
| 4 - What is Vector Span.mp4 | 15.5 MB | ||
| 5 - Changing Data Types in a Dataset.mp4 | 29.8 MB | ||
| 5 - Mean, Median and Model.mp4 | 19.7 MB | ||
| 5 - One Hot Encoding (OHE) Explained.mp4 | 46.3 MB | ||
| 5 - Python Data Structures.mp4 | 97 MB | ||
| 5 - Vector Multiplication.mp4 | 44.2 MB | ||
| 5 - What is Probability Mass Function.mp4 | 31.4 MB | ||
| 6 - Histogram and KDE Plot.mp4 | 44.9 MB | ||
| 6 - How to Perform Imputation on a Dataset.mp4 | 63.9 MB | ||
| 6 - How to import module in Python.mp4 | 49.2 MB | ||
| 6 - Small Correction in PMF.mp4 | 4.1 MB | ||
| 6 - Vectors Explained with a Housing Price Dataset.mp4 | 40.7 MB | ||
| 6 - What is Variable Transformation.mp4 | 33.2 MB | ||
| 7 - How to Handle Mixed Variable in a Dataset.mp4 | 14 MB | ||
| 7 - Imputation on Discreate Features.mp4 | 26.4 MB | ||
| 7 - Python Methods.mp4 | 46.8 MB | ||
| 7 - Scatter Plot.mp4 | 26.9 MB | ||
| 7 - Understanding Derivatives.mp4 | 32.3 MB | ||
| 7 - What is Probability Density Function.mp4 | 24.2 MB | ||
| 8 - Data Cleaning in EDA.mp4 | 50.1 MB | ||
| 8 - How to Handle Missing Data in a Dataset.mp4 | 88.9 MB | ||
| 8 - Python Handling Errors.mp4 | 44.1 MB | ||
| 8 - The Bell Curve Exploring the Normal Distribution.mp4 | 49.8 MB | ||
| 8 - Visualizing Relationship with Pairplots.mp4 | 27 MB | ||
| 8 - What is a Partial Derivative.mp4 | 13.1 MB | ||
| 9 - Bar Plot Explained.mp4 | 24.1 MB | ||
| 9 - Detecting Outliers in a Dataset.mp4 | 76.5 MB | ||
| 9 - Introduction to Gradients in Calculus.mp4 | 15 MB | ||
| 9 - KNN Imputation.mp4 | 48.9 MB | ||
| 9 - Object Oriented Programming in Python.mp4 | 222.6 MB | ||
| 9 - Population and Sample in Statistics.mp4 | 12.8 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Streaming.csv | 307.2 KB | ||
| eda-project.ipynb | 356.5 KB | ||
| requirements.txt | 102.4 B | ||
| ▲ 86 total files | |||
Data Science Basics: Python,Stats,Feature Engineering & EDA
https://WebToolTip.com
Published 11/2025
Created by Aritra Basak
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 81 Lectures ( 10h 58m) | Size: 3.64 GB
Learn Python, statistics, data visualization, Feature Engineering & EDA —perfect for beginners in data science & AI.
What you'll learn
Master Python fundamentals, data structures, OOP, and asynchronous programming for real-world data tasks.
Understand vectors, matrices and derivatives — the mathematical backbone of machine learning.
Create compelling plots and dashboards using Python libraries to communicate insights effectively.
Clean, transform, and create meaningful features that improve model accuracy and interpretability.
Use statistics and visualizations to uncover patterns, detect outliers, and generate insights.
Requirements
No prior programming or math experience needed — this course is designed for absolute beginners.
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