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