Data Science with Python Certification Course

Description

In this course, you will learn :

• Basics of Python Programming
• Command Line Parameters and Flow Control in Python
• Taking input from the user and performing operations on it
• Data types in Python
• Object Oriented Concepts
• Python Functions, Standard Libraries and Modules
• Handling Exceptions in Python
• Basic Functionalities of the NumPy library in Python
• Basic Functionalities of the Pandas library in Python
• Basic Functionalities of the Matplotlib library in Python
• Performing data manipulation using various functionalities of the Pandas library in Python
• Machine Learning concepts
• Machine Learning types
• Linear Regression Implementation
• Supervised Learning concepts
• Implementing different types of Supervised Learning algorithms
• Evaluating model output
• Implementing Dimensionality Reduction Technique
• Supervised Learning concepts
• Implementing different types of Supervised Learning algorithms
• Evaluating model output
• Unsupervised Learning
• Implementation of Clustering – various types
• Data Mining using python
• Recommender Systems using Python
• Implement Reinforcement Learning using Python
• Developing Q Learning model in Python
• TSA in Python
• Model Selection
• Boosting algorithm using python
• Performing EDA on the dataset(s) in Python
• Data Distribution using various charts in Tableau
• Combining Data using Joins, Unions and Data Blending
• Sorting, Filtering and Grouping Techniques
• Table Calculations in Tableau
• Advanced visualization techniques in Tableau
• Building Dashboards and Stories in Tableau

Syllabus :

• Introduction to Python
• Sequences and File Operations
• Deep Dive – Functions, OOPs, Modules, Errors and Exceptions
• Introduction to NumPy, Pandas and Matplotlib
• Data Manipulation
• Introduction to Machine Learning with Python
• Supervised Learning - I
• Dimensionality Reduction
• Supervised Learning - II
• Unsupervised Learning
• Association Rules Mining and Recommendation Systems
• Reinforcement Learning
• Time Series Analysis
• Model Selection and Boosting
• Statistical Foundations (Self-Paced)
• Data Connection and Visualization in Tableau (Self-paced)
• Advanced Visualizations (Self-paced)