Overview
Python for data science refers to the extensive libraries and tools the programming language offers for data and numerical analysis, as well as its capacity for machine learning tools that can improve analytics in general. In recent years Python has become an increasingly popular programming language due to its versatility and multi-purpose design.
As opposed to domain-specific languages and those that are designed for unique purposes, Python provides a range of tools that make it valuable not just as a data science platform, but also as a foundation to build more expansive applications that include data science tools.
Course Content
- Data Science
- Python Functions
- Python Types and Sequences
- Python more on Strings
- Python : Reading and Writing on CSV Files
- Python Dates and Times
- Advanced Python Objects and Maps
- Advanced Python Lambda and List Comprehensions
- Advanced Python Numerical Python Library (Numpy)
- Quiz: Assessment
- Series Data Structure
- Querying a Series
- The DataFrame Data Structure
- Querying a DataFrame
- Indexing a DataFrame
- Missing Values
- Programming Assessment
- Merging DataFrames
- Pandas Idioms
- Group by
- Scales
- Pivot Tables
- Data Functionality
- Goodhart’s Law
- Programming Assessment
- Introduction
- Distribution
- More Distributions
- Hypothesis Testing in Python
- Case Study Hypothesis Testing using NHANES Dataset
- Programming Assessment
- Confidence Interval
- Case Study Confidence Interval using NHANES Dataset
- Using Python for analysis Univariate Data
- Important Python Libraries
- Tables, Histogram, Boxplot in Python
- Case Study Tables, Histogram, Boxplot using NHANES dataset
- Multivariate Data Selection
- Multivariate Distribution
- Case Study Multivariate analysis using NHANES dataset
No reviews for this course