** Data Science with Python**

- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
- Central Tendency
Mean

Median

Mode

Skewness

Normal Distribution

- Probability Basics
What does mean by probability?

Types of Probability

ODDS Ratio?

- Standard Deviation
Data deviation & distribution

Variance

- Bias variance Trade off
Underfitting

Overfitting

- Distance metrics
Euclidean Distance

Manhattan Distance

- Outlier analysis
What is an Outlier?

Inter Quartile Range

Box & whisker plot

Upper Whisker

Lower Whisker

catter plot

Cook’s Distance

- Missing Value treatments
What is a NA?

Central Imputation

KNN imputation

Dummification

- Correlation
Pearson correlation

Positive & Negative correlation

- Error Metrics
Classification

Confusion Matrix

Precision

Recall

Specificity

F1 Score

- Regression
MSE

RMSE

MAPE

**Module 8: Machine Learning****Module 9: Supervised Learning**- Linear Regression
Linear Equation

Slope

Intercept

R square value

- Logistic regression
ODDS ratio

Probability of success

Probability of failure

ROC curve

Bias Variance Tradeoff

- Linear Regression
- K-Means
- K-Means ++
- Hierarchical Clustering
- K – Nearest Neighbour
- Naïve Bayes Classifier
- Decision Tree – CART
- Decision Tree – C50
- Random Forest

**Module 1: Introduction to Data Science**

**Module 2: Introduction to Python **

**Module 3: Python Basics **

**Module 4: Python Packages **

**Module 5: Importing data **

**Module 6: Manipulating Data **

**Module 7: Statistics Basics **

**Module 10: Unsupervised Learning **

**Module 11: Other Machine Learning algorithms **