About Workshop:

Data Science with python course will acquaint the participants with the nuts and bolts of the python programming condition and dataset including how to download and introduce python, expected basic python programming methods, and how to discover help with python programming questions. The course will likewise present information control and cleaning methods utilizing the famous python pandas information science library and present the reflection of the Data Frame as the focal information structure for information examination.

Prerequisites: Candidate must be aware of Python Programming

Attendee: Any UG/PG Students

Duration: 3/5 Days

Content:

Lesson 1: Data Science Overview

• Data Science
• Data Scientists
• Examples of Data Science
• Python for Data Science

Lesson 2: Python: Environment Setup and Essentials

• Introduction to Anaconda
• Insallation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
• Jupyter Notebook Installation
• Jupyter Notebook Introduction
• Variable Assignment
• Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
• Creating, accessing, and slicing tuples
• Creating, accessing, and slicing lists
• Creating, viewing, accessing, and modifying dicts
• Creating and using operations on sets
• Basic Operators: ‘in’, ‘+’, ‘*’
• Functions
• Control Flow
•  

Lesson 3: Mathematical Computing with Python (NumPy)

• NumPy Overview
• Properties, Purpose, and Types of ndarray
• Class and Attributes of ndarray Object
• Basic Operations: Concept and Examples
• Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Copy and Views
• Universal Functions (ufunc)
• Shape Manipulation
• Broadcasting
• Linear Algebra

Lesson 4: Scientific computing with Python (Scipy)

• SciPy and its Characteristics
• SciPy sub-packages
• SciPy sub-packages –Integration
• SciPy sub-packages – Optimize
• Linear Algebra
• SciPy sub-packages – Statistics
• SciPy sub-packages – Weave
• SciPy sub-packages – I O

Lesson 5: Data Manipulation with Python (Pandas)

• Introduction to Pandas
• Data Structures
• Series
• DataFrame
• Missing Values
• Data Operations
• Data Standardization
• Pandas File Read and Write Support
• SQL Operation

Lesson 6: Machine Learning with Python (Scikit–Learn)

• Introduction to Machine Learning
• Machine Learning Approach
• How Supervised and Unsupervised Learning Models Work
• Scikit-Learn
• Supervised Learning Models – Linear Regression
• Supervised Learning Models: Logistic Regression
• K Nearest Neighbors (K-NN) Model
• Unsupervised Learning Models: Clustering
• Unsupervised Learning Models: Dimensionality Reduction
• Pipeline
• Model Persistence
• Model Evaluation – Metric Functions

Lesson 7: Data Visualization in Python using Matplotlib

• Introduction to Data Visualization
• Python Libraries
• Plots
• Matplotlib Features:
• Line Properties Plot with (x, y)
• Controlling Line Patterns and Colors
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple Plots
• Subplots
• Types of Plots and Seaborn