### Syllabus of Data Science

### Course Content:

**1. Python and Statistics for Data Science**

Basics of Python

Fundamentals of Statistics

Probability

Linear Algebra

Calculus

**2. Data Analysis and Visualization with Python**

Introduction to NumPy

NumPy Arrays

Mathematical operations in NumPy

NumPy Array manipulation

NumPy Array broadcasting

Data Manipulation with Pandas

Data Structures in Pandas - Series and Data Frames

Data cleaning in Pandas

**Data manipulation in Pandas**

Data Structures in Pandas - Series and Data Frames

Data cleaning in Pandas

**Data manipulation in Pandas**

Handling missing values in datasets

**Data Visualization**

Visualization with Python

Plotting basic charts in Python

Data visualization with Matplotlib

Statistical data visualization with Seaborn

**3. Machine Learning**

**Introduction to Machine Learning (ML)**

What is Machine Learning ?

Use Cases of Machine Learning

Types of Machine Learning - Supervised, Unsupervised, Reinforcement Machine Learning workflow

**Supervised Learning**

**Regression**

**Multi Linear Regression**

Introduction to Linear Regression

Use cases of Linear Regression

Fitting a Linear Regression model

Evaluating and interpreting results from Linear Regression models

**Classification**

**Logistic Regression**

Introduction to Logistic Regression

Logistic Regression use cases

Understand use of Sigmoid function to perform logistic regression.

**Model Evaluation Techniques**

Introduction to evaluation metrics and model selection in Machine Learning

Importance of Confusion matrix for predictions

Measures of model evaluation - Sensitivity, specificity, precision, recall & f-score

Use ROC curve to decide best model

**Decision trees & Random Forests**

Introduction to Decision Trees & Random Forest

Understanding criterion (Entropy & Information Gain) used in Decision Trees

Using Ensemble methods in Decision Trees Applications of Random Forest.

**Support vector machines (SVM)**

Introduction to SVM

Figure decision boundaries using support vectors

Identify hyperplane in SVM

Applications of SVM in Machine Learning

**Unsupervised Learning**

**Clustering**

**K-Means**

Introduction to K-means clustering

Decide clusters by adjusting centroids

Find optimal 'k value' in kmeans

Applications of clustering in Machine Learning

**Recommendation Systems**

**KNN (K- Nearest neighbors)**

Introduction to KNN

Calculate neighbors using distance measures

Find optimal value of K in KNN method

Advantage & disadvantages of KNN

**Dimensionality Reduction**

Introduction to Curse of Dimensionality

What is dimensionality reduction?

PCA to reduce dimensions

Applications of Principle component Analysis (PCA)

**4. Deep Learning Foundation**

**5. Introduction to Computer Vision**

**6. Introduction to Natural Language Processing**