Data Science and Applied Artificial Intelligence
Become an expert in data science and artificial intelligence by mastering the skills to analyze data, formulate insights, and communicate your knowledge by applying machine learning and artificial intelligence algorithms. Improve your experience with their applications in cutting-edge technologies such as self-driving cars, smart cameras,
surveillance systems, robotic manufacturing, machine translations, search engines, and product recommendation systems.
- Start, study, graduate and get a job!
- No tuition fee until you are hired.
- Starting date: September 2022
No Tuition Until Hired
Master's Degree Program with Employability Focus
As data accumulates across broad sectors of the industry and academia we see a need for data scientists equipped with skills to assist with data-based decision-making. For example, businesses are using data to determine insurance coverage, make marketing decisions, offer recommendations to customers, and provide more effective health care.
Master of Science in Computer Science with Specialization in Data Science and Applied Artificial Intelligence program is created for students who are looking to gain practical and job-related knowledge for a career in data science and artificial intelligence. The curriculum encompasses a variety of courses ranging from fundamentals of programming in Python, statistics, data analysis, and database systems to advanced algorithms in machine learning, deep learning, reinforcement learning, and artificial intelligence, all of which are key for trained data scientists and AI engineers.
Our program provides students with both theoretical and activity-based learning, enabling them to enhance their careers. The program is spread across 30 credits and contains projects involving various datasets, classification methods, variable selection, and deep learning to name a few. Through advanced projects, you will have the opportunity to apply knowledge and skills to real-world challenges and obtain summative and formative feedback from expert instructors in the field. The hands-on training ensures job readiness upon graduation.
become an expert on Machine Learning
become an expert on Machine Learning
You Will Acquire Skills for
- SQL Databases
- Data Cleaning
- Data Manipulation
- Data Visualization
- Machine Learning
- Reinforcement Learning
- Deep Neural Networks
- Natural Language Processing (NLP)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- K-Nearest Neighbors algorithm (KNN)
The program is designed within a sequenced learning path including a predefined order of courses. As the student completes one course, access is granted to the next one. All of the courses structured in this path are compulsory and the student has to finish one course at a time. This allows students to gain knowledge sequentially and apply it immediately. This experiential learning approach increases information retention and eventually, execution. The learning path helps students’ understanding of what is expected of them and their preparation. It also facilitates gathering timely feedback that increases the effectiveness of learning. Researchers show that this holistic experience is vital in adult students’ engagement and achievement.
The program consists of 4 semesters and each semester includes 3 courses except the final semester that covers capstone project progress. All courses are scheduled for 4 weeks within a final course project.
Contemporary Technology University is aware of adults’ specific learning characteristics and needs, and embraces a collaborative pedagogical approach, and incorporates instructional models such as the 4E Learning Cycle. Each course adapts its daily contents in a learning cycle that helps students build a strong foundation of knowledge through active participation. Each course activity is designed as a part of cognitive stages of learning that comprise engaging, exploring, extending, and evaluating.
In this course, students will be introduced to statistics and how this mathematical discipline is used in data science. Students will learn several techniques for sampling data such as random sampling, stratified sampling, and cluster sampling. They will master how to summarize distributions using the mean, median, mode, range, variance, standard deviation, etc. Students will also learn how to visualize distributions using frequency distribution tables and graphs such as histograms, ogive, and box plots.
Students will be able to solve complex probability problems using the fundamental rules of probability. They will discover the importance of conditional probability and when to use the law of total probability and Bayes’ rule. Students will be introduced to the concepts of discrete and continuous random variables, their probability distributions, and their characteristics. They will build an understanding of the importance of normal distribution in the field of statistics.
Students will then be exposed to advanced statistical concepts such as estimation and hypothesis testing. Students will understand how to construct confidence intervals and how to conduct hypothesis tests for unknown population parameters. They will also be introduced to the statistical learning topics that are fundamentals of machine learning algorithms. Finally, they will be exposed to regression and classification concepts, and the fundamentals of measuring the quality of models.
In this course, students will be introduced to techniques for reading and normalizing JSON, CSV, HTML, SQL, and other common data types. They will demonstrate multiple approaches to aggregate data by groups and examine different strategies for concatenating and merging data. Students will learn to anticipate common data challenges when combining data. They will discover how to handle missing values in data, a critical part of almost every data analysis project, as well as how to handle outliers.
Students will learn how to supercharge data analysis workflow with cleaning and analytical techniques from the Python Pandas library. This course introduces common techniques for navigating around Pandas DataFrame, selecting columns and rows, and generating summary statistics. Students will explore a wide range of strategies to identify missing values and outliers. They will also learn how to update Pandas series with scalars, arithmetic operations, and conditional statements based on the values of one or more series, as well as, look at data in a completely different way.
Students will learn how to communicate insights and tell stories using data visualization by creating visually attractive plots with Seaborn, which is a Python data visualization library based on Matplotlib. Students will also learn how to add annotations to their visualization to provide additional context and add clarity to presentations.
In this course, students will be introduced to Pandas DataFrames to import and inspect datasets and practice building DataFrames from scratch, and become familiar with Pandas’ intrinsic data visualization capabilities. Students will be able to apply Exploratory Data Analysis (EDA).
Students will become familiar with concepts such as upsampling, downsampling, and interpolation by using Pandas’ method chaining to efficiently filter data and perform time series analyses. They will learn how to manipulate and visualize time series data using Pandas. Students will discover MultiIndexes and how to extract data from them. Students will learn how to leverage Pandas’ extremely powerful data manipulation engine to get the most out of datasets. They will understand how to tidy, rearrange, and restructure the data by pivoting or melting and stacking or unstacking DataFrames. Students will also understand how to identify and split DataFrames by groups or categories for further aggregation or analysis.
Students will master handling and manipulating different types of data e.g. numerical, categorical, text, and images. Students will be exposed to the concept of the data preparation process to utilize data for machine learning algorithms.
In this course, students will be introduced to the basics of deep neural networks. Students will learn scikit-learn to build and train neural networks. They will visit concepts such as graph theory, activation functions, hidden layers, and the structure of deep learning models.
Students will learn how to measure the performance of deep learning models with advanced techniques using ROC curves, sensitivity, and specificity for classification, and MAE, MSE for regression models. They will be guided to build strategies to improve performances by hyperparameter tuning, altering the structure of deep learning models, and using various optimization algorithms to boost the accuracy and performance of trained models.
Students will discover some advanced deep learning algorithms, namely, Convolutional Neural Networks (CNN) for visual tasks and Recurrent Neural Networks (RNN) for language tasks. They will also discover transfer learning techniques to adapt models for new tasks. Students will learn to use the TensorFlow library which is specifically designed for deep learning. They will be exposed to contemporary tools and cloud services that data scientists in the industry prefer.
The program brings together leading academicians and industry experts to give you a practical understanding of core concepts.
At Contech, we want to attract the most talented, not the most privileged students. We want to challenge your ambitions and imaginations in our admission process.
Documents to be uploaded:*Certificate of graduation*Transcript*Proof for English Proficiency (for international applicants)
Study Now, Pay Later!
Lowest Possible Cost
Lowest Possible Cost
International top-ranked colleges and universities squander substantial financial amounts on facilities and amenities most students never use, alongside outrageous graduate tuitions and fees – as high as $90,000 per annum – help pay for the waste.
Contech is different.
Our commitment to keeping operating costs low enables us to maintain a tuition that is about a fraction of that of other top universities. We solely charge what is compulsory to provide an exceptional education, inclusive of top faculty, maintaining small class sizes, offering innovative curriculum and challenging courses, and continually advancing the capabilities of the Active Learning Forum.
Students will not discover manicured gardens or million-dollar climbing walls at Contech because lavish amenities as such are believed to be superfluous to actual learning. By devoting every cent to independent student growth and development, Contech has proven that the value of education can far exceed its cost – not the other way around.
Enroll in this Program?
With the demand for Artificial Intelligence in a broad range of industries such as banking and finance, manufacturing, transport and logistics, healthcare, home maintenance, and customer service, the Master of Science in Computer Science with specialization in AI & Machine Learning is well suited for a variety of profiles like:
- Business analysis
- Data analysis
- Financial analysis
- Computer science
- Database Administration
- Science Marketing
The program is designed for students with a strong background in math, computer science, engineering who seek specific techniques and tools involved in computer science and the business skills to apply this knowledge
effectively and strategically.