Data analytics with python

Sacha Knowledge Exchange Session

Topic

Vijayalakshmi

kvijay62@ford.com

What is data analytics?

- Types:

• Descriptive Analytics: What happened? (Summarizes past data)
• Diagnostic Analytics: Why did it happen? (Analyzes cause and effect)
• Predictive Analytics: What will happen? (Uses statistical models and forecasts)
• Prescriptive Analytics: How can we make it happen? (Recommends actions)

Data Analytics Process

Data Collection

Data Cleaning

Data Analysis

Data Visualization

Data Interpretation

Applying statistical and

computational methods

Making sense of analyzed data

Python in Data Analytics

Key Python Libraries for Data Analytics

• Pandas: Data manipulation and analysis
• NumPy: Numerical computing
• Matplotlib/Seaborn: Data visualization
• Scikit-learn: Machine learning
• SciPy: Scientific computing
• Statsmodels: Statistical modeling

Pandas

Package overview

Data structure

Creating DataFrames

Data Selection and Indexing

• Selecting columns
• Selecting row by label
• select row by position

Data Cleaning

• Handling Missing Values
• Removing Duplicates
• Renaming Columns

Data Transformation

• Applying Functions
• Grouping Data
• Merging DataFrames

Data Aggregation

• Aggregating Data
• Pivot Tables

Time Series Analysis

• Handling Dates
• Resampling Data

Numpy

• Creating array
• Using Functions

Array Indexing and Slicing

• Indexing
• Slicing
• Multidimensional Arrays

Array Operations

• Arithmetic Operations
• Element-wise Operations

Statistical Operations

• Basic Statistics
• Aggregations

Linear Algebra with NumPy

Resharing

Any Queries?