Machine Learning and Data Analytics using Python

Bridging Business and Digital Transformation with Machine Learning and Data Analytics to accelerate competitive advantage.

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Course Dates

STARTS ON

27 September 2024

Course Duration

DURATION

8 Months, Online
6-8 hours per week, 1 Day Campus Immersion (Optional)

Course Fee

PROGRAMME FEE

US$4,299 and get US$430 off with a referral US$125 application fee

Course Information Flexible payment available
Course Information Special group enrolment pricing

Unlock Your Potential

Embark on a journey of continuous learning to enhance your skills and gain a competitive edge.

Emeritus is collaborating with NUS School of Computing to help you unlock transformative career growth. Enrol before 19 July, 2024 using this code UNLOCK200AL7149 and get USD 200 program fee benefit. Limited seats to success available. Claim yours now.

What Will This Programme Do For You?

  • Explain the usage of data from insight generation and visualisation to fitting machine learning models using Python
  • Understand the data structures in Python
  • Extract data from database using SQL
  • Write custom functions and codes in Python, and use relevant libraries in Python such as pandas and Numpy to manipulate data
  • Perform ETL (Extract Transform Load) processing using data analysis expressions (DAX) to perform calculations
  • Align and apply the supervised and unsupervised learning models in Python
  • Develop dashboards using Power BI
  • Optimise neural networks by using normalisation to identify best parameters settings
  • Perform exploratory data analysis (EDA) and data wrangling on various data sources
  • Identify challenges and demonstrate best practices in implementing machine learning models

Note: The programme highlights mentioned above are subject to change based on faculty availability and the desired outcomes of the programme.
*This programme is primarily self-paced online with some live sessions conducted by programme faculty. The availability of post-session video recordings is at the discretion of the faculty members, and Emeritus or the institute cannot guarantee their availability. We have a curated panel of distinguished industry practitioners who will conduct weekly live doubt-clearing sessions.
**Assignments will be graded by industry practitioners who are available to support participants in their learning journey, and/or by the Emeritus grading team. The final number of quizzes, assignments, and discussions will be confirmed closer to the start of the programme.
***Participants are responsible for arranging their own travel and accommodation for the on-campus networking sessions.

Programme Highlights

No prior coding experience

No prior coding experience

Programme designed for professionals without prior coding experience

Premium school experience

Premium school experience

Certificate from Asia’s Leading University

Industry relevant curriculum

Industry relevant curriculum

In-depth and comprehensive understanding of Machine Learning, Case studies developed by NUS School of Computing faculty and Emeritus

Distinguished faculty

Distinguished faculty

Learn via video lectures from renowned faculty at NUS School of Computing

Flexible mode of delivery

Flexible mode of delivery

Pre-Recorded Video Lectures* from NUS faculty with for self-paced learning interspersed with live lectures from renowned NUS faculty and industry practitioners

Live and interactive sessions

Live and interactive sessions

4 live sessions with faculty and 2 with industry experts

Campus immersion

Campus immersion

Participants will be invited to attend a 1-day networking opportunity to meet with fellow global leaders and interact with programme faculty/director

Capstone project

Capstone project

Toward the end of the programme, you will demonstrate your newly gained analytics skills by applying what you learned to build a simulation of real-life projects

Modules on Generative AI Including Applications

Gen-AI Masterclass and Live Case Study Discussions

Tools

SQLite

Excel Power Pivot

Excel Tables and Charts

Power BI Desktop

Orange Data Mining

DAX

Python

Jupyter Notebook

Pandas

Numpy

Seaborn

Matplotlib

Scikit Learn

Scikit Learn

Tensorflow

Note: All product and company names are trademarks or registered trademarks of their respective holders.
Use of them does not imply any affiliation with or endorsement by them.

Programme Modules

The curriculum is organised into a 30-week programme that will be taught via video lectures from NUS faculty experts with more than 20 years’ experience in the field.

Segmented into 3 integrative sections, the programme aims to systematically equip you with the knowledge and skills you need to implement Machine Learning and Data Analytics to solve real-life problems and meet evolving needs of organisations.
  • This section teaches you how to collect, organise, and analyse data to
    a. provide insights on how a business is functioning
    b. predict trends
    c. identify areas for improvement

    Week 1: Introduction to Analytics

    • The role of analytics in business intelligence and artificial intelligence
    • Data Processing Chain
    • A simple analytics example

    Week 2: Database: Data Source and Data Queries

    • Understanding databases structure
    • Exploring a database in SQLite
    • Getting started with queries using SQLite
    • Turning data into information
    • Working with multiple tables
    • Using functions

    Week 3: Data Warehouse: Load and Transform Data and Create a Data Model

    • Data preparation and data wrangling
    • Introduction to Exploratory Data Analysis (EDA)
    • Exploring and importing data into Power Pivot
    • Data munging with Power Query
    • Star schema data model
    • Creating a data model in Power Pivot

    Week 4: Data Warehouse: DAX and Time-based Analysis

    • DAX calculation types and functions
    • Getting data from related tables
    • Data context
    • Creating calculated columns and measures in Power Pivot
    • Time-based analysis and period-based evaluations
    • Utilising EDA to present time-based data

    Week 5: Data Visualisation: Pivot Tables and Charts and Power BI Basics

    • Working with Pivot tables and charts to create visualisations
    • Creating the BI interface in Excel
    • Case study using Power Pivot: Reseller Sales Analysis
    • Creating data Model, visualisations and dashboards in Power BI
    • Case study using Power BI: Sales Quota Analysis
    • Common visualisations used in EDA

    Week 6: Data Mining Basics

    • The fundamentals of data mining and CRISP-DM
    • Demonstrating unsupervised learning technique, specifically, K-Means clustering in Orange using practical examples
    • Demonstrating supervised learning techniques such as decision tree and linear regression models in Orange to solve real-life problems
  • This section provides hands-on experience on the most popular programming language in the data science world, Python Programming, to apply Python functions, operators, files, and packages for:
    a. data evaluation
    b. data visualisation
    c. application development

    Week 8: Getting Started with Python

    • Introduction to Python as a programming language
    • Identifying types of errors in a Python program
    • Examining the basic building blocks in a Python program
    • Creating a simple Python program

    Week 9: Collections, Strings and Comments

    • Using appropriate containers or collection data types to store data
    • Applying properties of lists, tuples, sets and dictionaries to organise data
    • Manipulating data and containers using functions and built-in methods on collections
    • Identifying the characteristics of strings
    • Implementing functions, operators and built-in methods on strings
    • Applying comments to describe written Python programs

    Week 10: Operators and Program Flow Controls

    • Explaining concept of various operators
    • Using operators under appropriate conditions
    • Applying operator precedence and associativity for expression resolution
    • Examining the different types of program flow control
    • Utilising appropriate program flow control mechanisms
    • Executing sequence, selection and iteration program flow controls

    Week 11: Functions, Import Statements, Inputs, Outputs, Exception Handling and File Handling

    • Exploring different types of functions
    • Differentiating functions from methods
    • Applying import keywords to Python modules
    • Applying the correct input syntax
    • Using exception statements to handle code errors
    • Displaying outputs with the correct syntax and format
    • Exploring methods in handling text and binary files

    Week 12: The Numpy Library

    • Applying concepts in indexing, slicing and iterating arrays
    • Understanding the process of reading and writing arrays into files
    • Exploring the methods to manipulate arrays

    Week 13: The Pandas Library

    • Creating and manipulating series and dataframes in the pandas library
    • Applying operations and functions to manipulate and manage values

    Week 14: Scikit Learn and Keras

    • Overview on Scikit learn
    • Classification, regression, clustering, model selection, pre-processing and dimensionality reduction in Scikit Learn
    • Importing the library along with various functions required for model evaluation- confusion matrix, test_train_split etc.,
    • Overview on deep learning
    • Types of models supported by Keras
    • Importing the library and building models in Keras and its applications

    Week 15: Data Visualisation

    • Creating data visualisations in matplotlib, pandas plot and Seaborn library
    • Applying concepts in enhancing and manipulating data visualisations

    Week 16: Python Application Development

    • Applying concepts in Python to create an Inventory Management System
    • Building a Python application using external data
  • This final section on Machine Learning covers the theoretical learning while emphasising on practical know-how needed to apply the techniques to generate impactful results.

    While focusing on Advance models and implementation challenges, the section stands on 3 building blocks of Machine Learning namely
    1. supervised learning
    2. reinforcement learning
    3. unsupervised learning

    Week 18: Introduction to Machine Learning and Learning

    • Defining artificial intelligence, machine learning and data science and their correlation
    • The history and applications of AI and machine learning
    • Future trends and approaches in the AI field
    • Learning patterns of machines
    • The machine learning process and approaches
    • Concepts of learning difficulties and lack of performance
    • Underfitting and overfitting

    Week 19: Supervised Learning: Classification

    • Applications of classification in business scenario
    • Explanation of dependent variable in classification
    • Continuous dependent variable versus categorical variable
    • ML techniques to predict the classes in classification
    • Demonstration using logistic regression in Python
    • Discussion of model statistics to evaluate the model

    Week 20: Supervised Learning: Regression

    • Regressive algorithms for implementing supervised learning with examples
    • Case study discussion: The use of supervised learning, specifically linear regression in NBA teams to improve their ranking and win a cup
    • Regressive models such as function estimation as linear regression and classification using logistic regression
    • The role of regressive models in function estimation and classification

    Week 21: Supervised Learning: Decision Trees

    • The theory, terminologies and applications of decision trees
    • The random forest ensemble system for high-performance decision-making and its applications
    • Machine learning concepts such as the entropy, GINI index and imbalanced classes issues

    Week 22: Support Vector Machine Algorithms

    • Defining support vector machine (SVM) and explaining the underlying algorithm
    • Discussing parameter tuning in SVM
    • Demonstrating SVM for classification and regression
    • Advantages and disadvantages of using SVM

    Week 23: Unsupervised Learning: Clustering techniques in K-Means

    • Describing the K-means clustering algorithm
    • Developing and using the K-means algorithm using Scikit Learn
    • Optimising a clustering system by changing the clustering parameters
    • VIsualising the results of clustering

    Week 24: Unsupervised Learning: Hierarchical Clustering Method

    • Different applications of hierarchical clustering
    • The importance of metrics and linkage criteria in hierarchical clustering
    • Top-down and bottom-up approaches of hierarchical clustering
    • The implementation of hierarchical clustering and interpretation of results

    Week 25: Unsupervised Learning: Probabilistic and Association Rule

    • Overview of probabilistic clustering and comparison with K-means
    • Exploring the Gaussian Mixture Model and Expectation Maximization algorithm
    • Overview of association rule and its application
    • Applying pre-processing and Apriori algorithm
    • Demonstration of association rule for market basket analysis

    Week 26: Neural Network

    • The theory, terminology and definition of artificial neural networks
    • Training algorithms of neural networks
    • Neural network parameters and ways to prevent underfitting and overfitting
    • Identifying a multi-layer perceptron as a solution to the problems of nonlinearity
    • The implementation of some machine learning applications in the system
    • Developing a model of biological brain as an artificial neural network
    • Examples of how to use neural networks as a function estimator
    • Setting optimal parameters to improve performance

    Week 27: Reinforcement Learning

    • Definitions and applications of reinforcement learning
    • Types of reinforcement learning
    • Reinforcement learning examples, such as traffic light control and personalised recommendation systems
    • Robot navigation using reinforcement learning

    Week 28: Deep Learning Using Keras and Tensorflow

    • Defining deep learning and its applications
    • Examining how deep neural networks deal with semi-structured and unstructured data
    • Explain types of networks and its applications
    • Exploring backward and reverse propagation
    • Describing layers in sequential neural net-hidden layers, input and output layers
    • Describing hyperparameters in neural net
    • Demonstration and application of deep learning techniques

    Week 29: Recommendation Systems

    • Defining recommendation systems and its uses
    • Exploring types of recommendation systems and their advantages and disadvantages
    • Exploring types of recommendation systems and their advantages and disadvantages

    Week 30: Challenges

    • The effects of super intelligence on society and its future
    • Examples of AI used in the real world
    • The role of AI in developing effective surveillance systems
    • Technical advantages and future trends
    • The role of quantum computing in the field of ML and big data

Integrated with Generative AI(Optional)

●   Module 1: Introduction to Generative AI

●   Module 2: Generative AI Models

●   Module 3: Working with Generative AI

●   Module 4: Coding

Masterclass to address cutting-edge tools and latest developments

Note: *This programme is primarily self-paced online with some live sessions conducted by programme faculty. The availability of post-session video recordings is at the discretion of the faculty members, and Emeritus or the institute cannot guarantee their availability. We have a curated panel of distinguished industry practitioners who will conduct weekly live doubt-clearing sessions.
**Assignments will be graded by industry practitioners who are available to support participants in their learning journey, and/or by the Emeritus grading team. The final number of quizzes, assignments, and discussions will be confirmed closer to the start of the programme.
The bonus content on Generative AI is optional and does not count towards your final evaluation.

Case Studies

Marketing Analysis for a Sunglasses Retailer

Marketing Analysis for a Sunglasses Retailer

In this case study, the faculty demonstrates how to use data to provide insights on the potential customers of a sunglasses retailer.

Airlines Delay Data Analyis

In this demonstration, the faculty shows how to transform data on flight delays into a user-friendly interface.

Performance Analysis of Top 10 Tennis Players

In this case study, the faculty demonstrates the application of logical flow in programming using various statements in Python to the study the performance of athletes.

Movie Reviews Analysis

In this case study, the faculty demonstrates several operations on string data to organise and display audience feedback.

Hierarchical Clustering for a Data Set on Celebrities

Hierarchical Clustering for a Data Set on Celebrities

Through a lecture video, the faculty will show how to develop a system to cluster a dataset containing information on celebrities.

Boston Housing Dataset Analysis

In this demonstration, the faculty demonstrates the application of neural network concepts to identify the median value of houses in Boston based on input features.

Programme Goals

  • Developing Machine Learning Champions in future-ready organisations.
  • Productive and efficient implementation of Machine Learning to solve real-life problems.
  • Enabling competency in Machine Learning, including knowledge of Data Analytics and Python programming language.

Programme Faculty

Faculty Member DANNY POO

DANNY POO

Associate Professor

Dr Danny Poo is a tenured Associate Professor at School of Computing (SOC), National University of Singapore (NUS).

Dr. Poo is a member of the School’s Curriculum Committee tasked in the design and implementation of Information Systems and Computer Science curriculum. As the Curriculum Chair of the Department of Information Systems and Analytics, he is instrumental in the development of undergraduate degree programmes in Information Systems, E-Commerce and Business Analytics at the NUS.
More info

AMIRHASSAN MONAJEMI

Senior Lecturer (Educator Track)

Dr Amirhassan Monajemi is a Senior Lecturer with the School of Computing, National University of Singapore (NUS).

Before joining the NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications.
More info

AI XIN

Lecturer (Educator Track)

Dr. Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS).

She has many years of experience teaching Artificial Intelligence and Data Science courses such as machine learning, deep learning, and data mining, etc.
More info

Why Enrol for the Programme?

In an increasingly data-driven world where humans are producing and consuming about 94 zettabytes of data by the end of 2022, data is becoming integral part of every organisational decision, interaction, and process. Machine Learning and Data Analytics are giving rise to innovative business models and products, and present immense potential to bring transformative changes across business sectors and industries.

Machine Learning and Data Analytics could accelerate businesses’ competitive edge by analysing and processing data to obtain insights to make more accurate predictions and deliver innovative and strong business values.

A key component needed to excel in Machine Learning and Data Analytics includes knowledge and applications of programming languages.

Python is ranked as one of the most demanded programming languages in the workplace used not only by software developers but also by professionals in major industries including finance, healthcare, consulting and academia.

The Machine Learning and Data Analytics using Python programme by National University of Singapore’s School of Computing is curated with a strong emphasis on real-world relevance to meet rapidly evolving industry needs and trends.

The Programme’s holistic and integrative design will provide you not only with strong conceptual knowledge and applications of Machine Learning and Data Analytics but also strong market-ready coding skills and practical applications of Python Programming to deploy solutions and streamline core business processes to increase returns, to meet the evolving needs of organisations.

38.8%

The Machine Learning Market is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8% in forecast period.

Source: MRFR, 2022

30.7%

The Data Analytics Market Worth USD 346.24 Billion at a CAGR of 30.7% by 2030.

Source: MRFR, 2022

15.42%

Python is the world’s most popular programming language and has reached an all-time high of 15.42% market share.

Source: TIOBE and PYPL Index , 2022

*The schedule of live sessions and profile of Industry experts is subject to change and confirmation will be provided post programme start.

Who Is This Programme For?

The programme is designed for professionals without prior coding experience*, from both tech and non-tech domains, who want to:

  • Pivot into a Machine Learning career
  • Acquire Machine Learning concepts and techniques to transition into Machine Learning roles
  • Gain the expertise to lead and solve real-world business problems with data and modelling at the core
The programme is particularly applicable to major industries such as IT Product & Services, Banking and Financial Services, Consulting, Education, Healthcare, and Retail, across sectors and functions.

Note: *Although no prior coding experience is required, non-tech participants are encouraged to put additional effort and complete the set of pre-readings provided to prepare for the course.
Programme Faculty for the live sessions might change due to unavoidable circumstances, and revised details will be shared closer to the programme start date.

Emeritus Career Services

Stepping into a business leadership career requires a variety of job-ready skills. Below given services are provided by Emeritus, our learning collaborator for this program. The primary goal is to give you the skills needed to succeed in your career; however, job placement is not guaranteed.

Emeritus provides the following career preparation services:

●   Resume building videos

●   Interview preparation videos

●   Linkedln profile building videos

●   Interview guidebooks

●   Glossary of resume templates

Please note:

NUS or Emeritus do not promise or guarantee a job or progression in your current job. Career Services is only offered as a service that empowers you to manage your career proactively. The Career Services mentioned here are offered by Emeritus. NUS is not involved in any way and makes no commitments regarding the Career Services mentioned here

Certificate

Certificate

Upon successful completion of the programme, participants will be awarded a verified digital certificate by NUS School of Computing.

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The Learning Experience

What is it like to learn with the learning collaborator, Emeritus?

More than 300,000 professionals globally, across 200 countries, have chosen to advance their skills with Emeritus and its educational learning partners. In fact, 90 percent of the respondents of a recent survey across all our programs said that their learning outcomes were met or exceeded. All the contents of the course would be made available to students at the commencement of the course. However, to ensure the program delivers the desired learning outcomes, the students may appoint Emeritus to manage the delivery of the program in a cohort-based manner during the course period the cost of which is already included in the overall Course fee of the course.

A dedicated program support team is available 7 days a week to answer questions about the learning platform, technical issues, or anything else that may affect your learning experience.
Limited seats are available.
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