# Data Science Full Course – Learn Data Science in 10 Hours | Data Science For Beginners

Data Science Master Program
This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners tutorial video :

Introduction to Data Science
Data Analysis at Walmart
What is Data Science?
Who is a Data Scientist?
Data Science Skill Set
Data Science Job Roles
Data Life Cycle
Statistics & Probability
Categories of Data
Qualitative Data
Quantitative Data
What is Statistics?
Basic Terminologies in Statistics
Sampling Techniques
Random Sampling
Systematic Sampling
Stratified Sampling
Types of Statistics
Descriptive Statistics
Range
Inter Quartile Range
Variance
Standard Deviation
Confusion Matrix
Probability
What is Probability?
Types of Events
Probability Distribution
Probability Density Function
Normal Distribution
Standard Deviation & Curve
Central Limit Theorem
Types of Probability
Marginal Probability
Joint Probability
Conditional Probability
Use-Case
Bayes Theorem
Inferential Statistics
Hypothesis Testing
Basics of Machine Learning
Need for Machine Learning
What is Machine Learning?
Machine Learning Definitions
Machine Learning Process
Supervised Learning Algorithm
What is Regression?
Linear vs Logistic Regression
Linear Regression
Where is Linear Regression used?
Understanding Linear Regression
What is R-Square?
Logistic Regression
Logistic Regression Curve
Logistic Regression Equation
Logistic Regression Use-Cases
Demo
Implement Logistic Regression
Import Libraries
Analyzing Data
Data Wrangling
Train & Test Data
Implement Logistic Regression
SUV Data Analysis
Decision Trees
What is Classification?
Types of Classification
Decision Tree
Random Forest
Naive Bayes
KNN
What is Decision Tree?
Decision Tree Terminologies
CART Algorithm
Entropy
What is Entropy?
Random Forest
Types of Classifier
Why Random Forest?
What is Random Forest?
How Random Forest Works?
Random Forest Algorithm
K Nearest Neighbor
What is KNN Algorithm?
KNN Algorithm Working
kNN Example
What is Naive Bayes?
Bayes Theorem
Bayes Theorem Proof
Naive Bayes Working
Types of Naive Bayes
Support Vector Machine
What is SVM?
How does SVM work?
Introduction to Non-Linear SVM
SVM Example
Unsupervised Learning Algorithms – KMeans
What is Unsupervised Learning?
Unsupervised Learning: Process Flow
What is Clustering?
Types of Clustering
K-Means Clustering
K-Means Algorithm Working
K-Means Algorithm
Fuzzy C-Means Clustering
Hierarchical Clustering
Association Clustering
Association Rule Mining
Apriori Algorithm
Apriori Demo
What is Reinforcement Learning?
Reinforcement Learning Process
Markov Decision Process
Understanding Q – Learning
Q-Learning Demo
The Bellman Equation
What is Deep Learning?
Why we need Artificial Neuron?
Perceptron Learning Algorithm
Activation Function
Single Layer Perceptron
What is Tensorflow?
Demo
What is a Computational Graph?
Limitations of Single Layer Perceptron
Multi-Layer Perceptron
What is Backpropagation?
Backpropagation Learning Algorithm
Multi-layer Perceptron Demo
Data Science Interview Questions

### Course Information

Estimated Time: 1 Week

Difficulty: Beginner

Categories: ,

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