The OneCampus programs for Data Science, Data Analytics, and Machine Learning are structured into two main learning tracks, Core Foundations (made up of 3 Academies) and MasterClass (consisting of specialization academies)
CoreFoundations:Â
- Python Programming Academy
- Data Wrangling Academy
- Data Visualization Academy
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MasterClass (Speciualization) Academies:
- Data Analytics Academy for The Data Analytics Program
- Data Science ACademy for The Data Science Program
- Machine Learning Academy for The Machine Learning Program
- Generative AI Academy
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All students, regardless of the program admitted into, will go through the Core Foundations Academies ( first three academies), and then branch out to their specific programs when they get to the Specializations Academy.
Upon completion of each academy, successful candidates are awarded domain badges and a mini-Certificate of completion
Bootcamp Format and Structure
This Bootcamp will be taught live online over a duration of 12 months. The classes will be weekend-only sessions (3 hours ) with a 30-minute break. The time schedule assigned to your cohort will be communicated upon registration. You will receive an enrollment kit prior to the start of your cohort.
See course syllabus for course details
Bootcamp Prerequisites
Knowledge/Skill Requirement:
Prior knowledge of programming in Python is useful but not required. This course assumes beginner status for all participants. However, individuals who have taken the OneCampus Python Foundations or Python for Data Science are well off to a good start.
Course Resource Requirement:
Before attending this class, please take the free “Setting Up Environment for Data Science” course. This will ensure that your development environment is ready and fired up. You need to set up the following environment on your computer:
Install the latest version of
- Python
- Jupyter Notebook
The first session of this Bootcamp includes a session to assist you to set up your development environment.
Python Packages Required:
You will be provided a .txt file of all required python packages for this course upon enrollment as part of your Starter Kit. Your Instructor will provide step-by-step guidance on how to install packages
ABOUT THE PYTHON ACADEMY
The Python Academy is the first of 4 academies you will be passing through in your journey to becoming a Data Analytics professional, Data Scientist, or machine learning Engineer.
Upon completion of each academy, successful candidates are awarded domain badges and a mini-Certificate of completion
This academy focuses on building your skill in python for task-oriented programming.
We will cover all the basics of Python and get you off to a good start loading data and implementing code scripts to execute any required task
See the program Syllabus for details of program content
Learn to work with data and extract features that are mission-specific
Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.
- Identify trends and outliers
- Tell a story within the data
- Reinforce an argument or opinion
- Highlight an important point in a set of data
- Generate Insights locked in the data
- Build sharable dashboards
Specialize in any of the Data SCience domains and become job-readyÂ
About the Data Analytics Program:
What you will learn
•                    learn how to discover these hidden patterns in your data
•                    Analyze patterns in data and leverage the results to help transform your organization
•                    Learn how to correlate data, plot histograms, and analyze temporal features
•                    Learn how to visualize data for your organization using the Seaborn and Matplotlib libraries
•                    Explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imba…
•                    Learn different data analysis techniques, including hypothesis testing, correlation, and null-value imputation
•                    Become a confident data analyst with a certification to show
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Program Benefits
Through this Bootcamp, participants will gain strong mastery of expert data analysis and visualization skills to solve business problems using state-of-the-art data analytics models. Key aspects of the course include:
•                    Get to grips with the fundamental concepts and conventions of data analysis
•                    Understand how different algorithms help you to analyze the data effectively
•                    Determine the variation between groups of data using hypothesis testing
•                    Visualize your data correctly using appropriate plotting points
•                    Use correlation techniques to uncover the relationship between variables
•                    Find hidden patterns in data using advanced techniques and strategies
•                    Learn how to analyze time series and categorical data
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About The Data Science Program:
What you will learn
•                    Gain mastery of how to apply data science techniques to convert raw data into game-changing insights
•                    Multiple projects and exercises strengthen your skill to solve business problems using machine learning algorithms
•                    Learn how to assess model performance and improve model accuracy using ensemble techniques and hyper-parameter tuning te…
•                    Learn to wrangle data like a pro and create new features that better expose hidden concepts in the data to your algorithms
•                    Build the confidence to take on big data science projects and organize your work as a data science expert
Program Benefits
Through this course, participants will gain strong mastery of the full cycle of data science model development; from data preprocessing down to pushing trained/tested models to production. Key aspects of the course include:
•                    Explore the key differences between supervised learning and unsupervised learning
•                    Manipulate and analyze data using scikit-learn and pandas libraries
•                    Understand key concepts such as regression, classification, and clustering
•                    Discover advanced techniques to improve the accuracy of your model
•                    Understand how to speed up the process of adding new features
•                    Simplify your machine learning workflow for production
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About The Machine Learning Program:
•                    Understand Algorithms and how they work
•                    Learn about Supervised and Unsupervised Learning Techniques
•                    Learn about artificial neural networks using scikit-learn and how to improve their performance by fine-tuning hyperparameters
•                    Build end-to-end machine learning models that solve real-world problems
•                    Learn how to compare models, optimize performance and select the best model for the job
•                    Get the skills required to start programming your own machine learning algorithms
