Duration
5 days
Overview
The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science.
Chapter 1 – What is Data Science?
- Why Data Science?
- Prerequisite for Data Science
- Data Science Skills
- What Does a Data Scientist Do?
- Must-Know Machine Learning Algorithms
Chapter 2 – Statistics
- Describing Single Set of Data
- Correlation
- Simpson’s Paradox
- Correlation and Causation
Chapter 3 – Probability
- Dependence and Independence
- Conditional Probability
- Bayes’s Theorem
- Random Variables
- Continuous Distribution
- The Normal Distribution
- The Central Limit Theorem
Chapter 4 – Hypothesis and Inference
- Statistical Hypothesis Testing
- p-values
- Confidence Intervals
- p-Hacking
- Bayesian Interface
Chapter 5 – Gradient Descent
- The Idea Behind the Gradient Descent
- Estimating the Gradient
- Using the Gradient
- Choosing the Right Step Size
- Using Gradient Descent to Fit Models
Chapter 6 – Getting Data
- stdin and stdout
- Reading Files
- Scraping the Web
- Using APIs
Chapter 7 – Working with Data
- Exploring Your Data
- Using NamedTuples
- Dataclasses
- Cleaning and Munging
- Manipulating Data
- Rescaling
Chapter 8 – Machine Learning
- Modeling
- What is Machine Learning?
- Overfitting and Underfitting
- Correctness
- The Bias-Variance Tradeoff
- Feature Extraction and Selection
Chapter 9 – k-Nearest Neighbour
- The Model
- Example: The Iris Dataset
Chapter 10 – Simple Linear Regressions
- The Model
- Using Gradient Descent
- Maximum Likelihood Estimation
Chapter 11 – Logistic Regression
- The Problem
- The Logistic Function
- Applying the Model
- Goodness of Fit
- Support for Vector Machines
Chapter 12 – Decision Trees
- What is a Decision Tree?
- Entropy
- The Entropy of a Partition
- Creating a Decision Tree
- Putting It All Together
- Random Forests
Chapter 13 – Clustering
- The Idea
- The Model
- Choosing k
- Bottom-Up Hierarchical Clustering
Chapter 14 – Data Ethics
- What Is Data Ethics?
- The Importance of Data Ethics
- Building Bad Data Products
- Trading Off Accuracy and Fairness
- Collaboration
- Interpretability
- Recommendations
- Biased Data
- Data Protection
Programme scope, delivery arrangements and suitability are confirmed during enquiry.