Data Science With Python (Basic)

This course will introduce you to what data science is and what data scientists do. You'll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions, as well as build machine learning models and deploy data science solution to a webpage.

Objectives:

• Use principles of statistics and probability to design and execute A/B tests and recommendation engines to assist businesses in making data-automated decisions
• Build Machine Learning Models & make predictions
• Deploy a data science solution to a web app
• Manipulate and analyse distributed datasets using Apache Spark
• Communicate results effectively to stakeholders

Course Outline:

• Module 1: Introduction to Data Science
• The Data Science Process
• Communicating to Stakeholders
• Module 2: Linear Algebra
• Vectors
• Linear Combination
• Linear Transformation and Matrices
• Module 3: Practical Statistics
• Data Types
• Measures of centre (mean, median, mode)
• Standard Deviation, Variance, Outliers
• Probability
• Binomial Distribution
• Conditional Probability
• Bayes Rule
• Normal Distribution theory
• Sampling distributions and the Central Limit
• Theorem
• Confidence Intervals
• Hypothesis Testing
• Type I and type II errors
• P-values
• Null Hypothesis, Alternate Hypothesis
• Module 4: Data Engineering
• ETL pipelines
• Extract - CSV, JSON, XML, SQL databases
• Transform - combing, cleaning, encoding, missing data, duplicate data
• Dummy data, Outlier data, scaling data
• Feature Engineering
• Load
• Module 5: Database Programming
• Module 6: Data Preparation, Data Wrangling
• Module 7: Data Visualisation