Data Science Program

Career-oriented learning to develop in-demand skills

1 Day Master Class: 6 Units for $100 $80

5 Month Full-Time Class: (pricing to be announced)

Intake In Progress

Learn key data science essentials, including R/Python and machine learning, through real-world case studies to jumpstart your career as a data scientist.

Companies across the globe are collecting huge volumes of data about their customers, operations, and performance. Because of this, there is increasing demand for people who can help to transform this explosion in data into new insights that will drive the businesses forward to competitive advantage.

In this class, you will acquire a firm grounding in data analytics and practical strategies for implementing data-focused initiatives that create and capture more value.

Key Benefits

This program will guide you through the process of data-driven decision-making for the purpose of creating competitive business models and breakthrough products. At the end, you will be able to::

Become a marketable professional with the needed Data Science skills for management of business models and operations

  • Learn how to draw on big data to inform your organization's long-term strategies
  • Integrate algorithms and data analytics into your decision-making processes and key functions across the organization, such as marketing, supply chain, human resources, and more

Expand your personal and professional network

  • Extend your network by working with accomplished executives from various backgrounds and industries
  • Build relationships with a diverse group of peers who can provide wide-ranging insights and support into your business challenges and career decisions

Who Should Attend

The masterclass and the full-time professional courses are for individuals currently working in or aiming to deepen their skills and expertise for these types of roles:

  • Data Scientist
  • Business Intelligence Analyst
  • Information Architect
  • Information Systems Manager
  • Predictive Analytics Developer
  • Big Data Engineer

Individuals with a bachelor’s degree in engineering, science, math/statistics, finance, computer science, accounting or marketing who enjoy statistical and analytical thinking may excel in this field.

To understand the content, derive value, and successfully complete these courses, you should have at least two (2) of the following prerequisite:.

  • Understanding of basic statistics methods used in business performance measures.
  • Strong interest in data science
  • Some programming experience (In any language)

You can choose to pursue either or both of the two tracks below

Data Science Masterclass (1 Day, 8 hours)

The Data Science Masterclass is a day-long program that is facilitated by an industry expert and covers select units from our Main Data Science Program. The aim is to give you insightful in-depth knowledge and advanced practical skills that you can immediately apply in your day-to-day.

Key topics covered in the class

  • Deep Dive into Linear Regression
  • 2 hours of Python/R
  • 2 hours of library use like Numpy, Pandas. Used for data cleaning. Library for visualisation
  • 4 hours Hands on with data sets, correlating variables using linear regression models
  • Work on classification models
  • Supervised Machine Learning Algorithms

Data Science Full-time Class (20 Weeks, 5 Days a Week, 8 Hours a Day)

This is the more intensive program that builds on from our Master Class by introducing both foundational and complex concepts in addition to practicals that will get you on your way to becoming a fully-fledged Professional Data Scientist.

The classes are facilitated by industry experts and will take 20 Weeks to complete.

Key topics covered in the class

  1. Introduction to Data Science
  2. This unit introduces you to the basics and foundations of Data Science and you’ll learn about what it takes to become a Data Scientist..

    Topics covered: Life-cycle of Data Science Project, Python Programming, Git & GitHub, Mark Down

  3. Data Science Toolkit
  4. Tools are an important element of the Data Science field. This unit introduces the Data Science toolkits available to you and how you can use them to dynamically express your insights through rich visualizations and customizations

    Topics covered: NumPy, Pandas.

  5. Managing and Curating Data
  6. Learn how to organize, clean, enhance and preserve data collected from various sources for current and future use.

    Topics covered: Working With Data Sources (excel, csv, json, xml, etc), Interactive Data Libraries for Web Data

  7. Exploratory Data Analysis
  8. Learn how to perform investigations on data to discover patterns, to spot anomalies, and to test hypothesis and assumptions with the help of summary statistics and graphical representations.

    Topics covered: Exploratory Techniques, Data Visualizations With Plotting Libraries

  9. Databases & Big Data
  10. Learn how popular database technologies are used to capture, store, share and query data.

    Topics covered: Excel, Relational Databases, Server Query Language (SQL), Other Database Management Systems, Big Data

  11.  Modelling Data
  12. Learn how to use algorithm development, data interface, and technology to build data models that allow you to examine, structure and contextualize your data to get proper results each and every time.

    Topics covered: Machine Learning Supervised, Machine Learning Unsupervised

  13. Real-world case studies
  14. Team up for practicals that explore real-world datasets and problems from various domains, including healthcare, financial services, entertainment, consumer marketing/retail, and government.

  15. Data Products
  16. Learn how to use technology to automate complex analysis tasks to expand the utility and output of a statistical data-informed model, algorithm or inference.

    Topics covered: Real-time Prediction, Batch Prediction, REST Endpoints, Security and Machine Learning in the Cloud

  17. Market Readiness
  18. This final step is for professional development and gets you ready for the market. You will learn how to package yourself to take advantage of your newly acquired skills. Topics covered include:

    • Dashboard and Visualization building.
    • Resume preparation/marketing yourself, interview preparation, negotiating, and more.
    • Demo: Portfolio
    • Interest Cases i.e. Building recommended systems
    • Prospective employers at your class Demo Day

    Finally, you get to meet and mingle with working data scientists from several industries as you get your certificate.