Advanced Data Science Certification - United Kingdom

Advanced Data Science Certification​ Course Outline

Module 1: Python for Data Analysis - NumPy

• Introduction to NumPy
• NumPy Arrays
• Aggregations
• Computation on Arrays: Broadcasting
• Comparison, Boolean Logic and Masks
• Fancy Indexing
• Sorting Arrays
• NumPy’s Structured Arrays

Module 2: Python for Data Analysis – Pandas

• Installing Pandas
• Pandas Objects
• Data Indexing and Selection
• Operating on Data in Pandas
• Handling Missing Data
• Hierarchical Indexing
• Concat and Append
• Merge and Join
• Aggregations and Grouping
• Pivot Tables
• Vectorised String Operations
• Working with Time Series

Module 3: Python for Data Visualisation – Matplotlib

• Overview
• Object-Oriented Interface
• Two interfaces
• Simple Line Plots and Scatter Plots
• Visualising Errors
• Contour Plots
• Histograms, Binnings and Density
• Customising Plot Legends
• Customising Colour Bars
• Multiple Subplots
• Text Annotation
• Three Dimensional Plotting

Module 4: Python for Data Visualisation – Seaborn

• Installing Seaborn and Load Dataset
• Plot the Distribution
• Regression Analysis
• Basic Aesthetic Themes and Styles
• Distinguish between Scatter Plots, Hexbin Plots and KDE Plots
• Use Boxplots and Violin Plots

Capstone 1: Retrieving, Processing and Visualising Data with Python

Module 5: Machine Learning

• Introduction
• Importance
• Types
• Working
• Machine Learning Mathematics

Module 6: Natural Language Processing

• Introduction
• NLP Example
• Advantages
• NLP Applications

Module 7: Deep Learning

• Introduction
• Importance
• Working

Module 8: Big Data

• Big Data Analytics
• State of Practice in Analytics
• Main Roles for New Big Data Ecosystem
• Phases of Data Analytics Lifecycle

Capstone 2: Machine Learning Applications in Retail, Hospitality, Education and Insurance Sectors

Module 9: Working with Data in R

• Data Manipulation in R
• Data Clean Up
• Reading and Exporting Data
• Importing Data
• Charts and Graphs

Module 10: Regression in R

• Regression Analysis
• Linear Regression
• Logistic Regression
• Multiple Regression
• Normal Distribution
• Binomial Distribution

Capstone 3: Retrieving, Processing and Visualising Data with R

Module 11: Modelling Data in Power BI

• Power BI Data Model
• What are the Relationships
• Viewing Relationships
• Creating Relationships
• Cardinality

Module 12: Shaping and Combining Data using Power BI

• The Query Editor
• Shaping Data and Applied Steps
• Advanced Editor
• Formatting Data
• Transforming Data
• Combining Data

Module 13: Interactive Data Visualisations

• Page Layout and Formatting
• Multiple Visualisation
• Creating Charts
• Using Geographic Data
• Histograms

Capstone 4: Product- Sales Analysis using Power BI