Duration
3 days
What You will Learn
- Improve your understanding of descriptive statistics and apply them over a dataset.
- Data cleaning or cleansing as a pre-processing step towards making the data more consistent and high quality before training predictive models.
- Learn how to deal with missing data and outliers to resolve data inconsistencies.
- Explore various visualization techniques for bivariate and multivariate analysis.
- Learn what type of visualization is suitable for your story.
Course Outline
Planning and Preproduction: Aligning your Audience, Stakeholders, and Data
- What is a Story?
- Introduction to Planning and Preproduction for Visualizations
- A Design Checklist of the Who, What, Why, and How
- Start with Your Stakeholders
- Finding the Story
- Prioritizing, Optimizing, and Designing the Data Story
- Case Study - "The Tale of 100 Entrepreneurs" Introduction to Key Metrics, Indicators, and Decision Triggers
- Key Performance Indicators (KPIs)
- Thresholds and Alerts6m
- Data Quality
Key Metrics, Indicators, and Decision Triggers
Introduction to the Data Science in Python
- What is Data Science?
- Introduction to the Python Data Science Tool
- Introduction to the Python Data Science Environment
- Introduction to Python Pre-Requisites for Data Science
- Different Types of Data Used in Statistical & ML Analysis
- Different Types of Data Used Programmatically
- Python Data Science Packages To Be Used
Exploratory Data Analysis
- What is Exploratory Data Analysis?
- Exploratory Data Analysis vs Data Analysis
- Exploratory Data Analysis Techniques
- Exploratory Data Analysis Process
- Types of Exploratory Data Analysis
Introduction to Numpy
- Numpy: Introduction
- Create Numpy Arrays
- Numpy Operations
- Matrix Arithmetic and Linear Systems
- Numpy for Basic Vector Arithmetric
- Numpy for Basic Matrix Arithmetic
- Broadcasting with Numpy
- Solve Equations with Numpy
- Numpy for Statistical Operation
Introduction to Pandas
- Data Structures in Python
- Read in Data
- Read in CSV Data Using Pandas
- Read in Excel Data Using Pandas
Data Visualizations and Story Telling
- What is Data Visualization?
- Some Theoretical Principles Behind Data Visualization
- Histograms-Visualize the Distribution of Continuous Numerical Variables
- Boxplots-Visualize the Distribution of Continuous Numerical Variables
- Scatter Plot-Visualize the Relationship Between 2 Continuous Variables
- Barplot
- Pie Chart
- Line Chart
Data wrangling and Feature Engineering
Introduction
- Types of Variables
- Variable Characteristics
- Missing Data Imputation
- Multivariate Imputation
- Categorical Encoding
- Variable transformation
- Discretisation
- Outlier Engineering
- Feature Scaling
- Mixed Variables
- Engineering Date-Time
Programme scope, delivery arrangements and suitability are confirmed during enquiry.