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Online Master of Science in Computer Science

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
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No Tuition Until Hired

Here at Contech, you can commence your degree program without any payments. After graduation and landing a high-paying job, you can start paying your tuition $500 a month, thanks to our Study Now, Pay Later Model. 

High Demand

Professionals working in AI & Machine Learning fields earn between $ 120,000 - $234,000 annually.

Employability Focus

We will be providing Practicums for acquiring hands-on skills and extracurricular courses, where you create your personal brand and getting ready for interviews.
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AIEFocus
creative approach

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.

You want to

become an expert on Machine Learning

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you want to

become an expert on Machine Learning

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throughout this program

You Will Acquire Skills for

  • Python
  • Statistics
  • 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)

Program Structure

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.

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Tools Covered

Tools Covered

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Our Curriculum

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 the Python programming language. They will explore its fundamental principles and techniques as well as its usage in data-centric fields, which are becoming more and more popular for all industries. Students will have a chance to examine real-world examples and cases to place data science techniques in context. They will further develop data-analytic thinking. This course will illustrate the proper application of data science is as much an art as it is a science. Finally, this course covers Python-associated data analysis libraries for conducting data science techniques successfully.

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 essential tool sets to conduct data-related analysis. Students will learn and practice how to use the terminal on UNIX machines. They will learn about how to navigate the file system, how to alter permissions for different users, and how to create and run a Python script from the command line to become comfortable in day-to-day data analysis tasks. They will also learn the concepts such as how to pipe and redirect the output into a file, how to search files for a string, and how to clean, explore, and consolidate data using the command line. Students will be exposed to building a project that combines Python data skills with command line expertise, and write Python scripts to compute summary statistics, and then running the scripts directly from the command line. They will further be exposed to learn Git and version control systems and why it’s critical to be able to use version control in any sort of collaborative programming environment by covering the fundamentals. Students will start building some experience working with SQL databases to explore and analyze data in SQL through hands-on active learning. Students will master how to view SQLite database tables, and how to apply filters, and functions to create summary statistics or various tables. They will also learn how to compute group-level summaries, how to query virtual columns, and how to write complex or nested SQL queries using subqueries. Students will learn and master working with PostgreSQL. They will also learn how to query external data sources using an API and explore the basics of scraping data from the web to analyze.
In this course, students will be introduced to the basics of machine learning and the concepts of supervised, unsupervised, and reinforcement learning. Students will discover machine learning algorithms including both classification and regression models such as linear regression, logistic regression, decision tree, random forest, KNN, SVM, and so on. They will build an understanding of what is happening in the model training process with an introduction to scikit-learn, which is an open-source machine learning library for the Python programming language. Students will get insights into performance evaluation and learn parameters of machine learning models to optimize machine learning algorithms to boost the accuracy and performance of trained models. They will dig into k-fold cross-validation to perform more rigorous testing for machine learning models. Students will learn the basics of linear regression and classification models and how to apply feature engineering for machine learning by learning how to evaluate the importance of features and select appropriate features to yield the best performance. Students will also learn concepts such as machine learning explainability to open the ‘black box’ of algorithms, machine learning pipelines, and workflow of machine learning projects. They will apply various machine learning algorithms by using the scikit-learn library and discover some well-known cloud services. Students will also learn about online competitions for data science and how to get prepared and join competitions to contribute to online environments.
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.

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.

In this course, students will be faced with some real-world applications including but not limited to prediction, regression, classification, recommender systems, image recognition, audio recognition, text recognition, computer vision, clustering, anomaly detection, and Natural Language Processing (NLP). They will be exposed to case studies in which they may need to use data cleaning, data visualization, data manipulation, model training, model evaluation, sampling, feature engineering, etc. techniques.
This course is for the students to apply theoretical knowledge acquired during the M.Sc. in C.S. program to a project involving actual data in a realistic environment. During the project, students engage in the entire process of solving a real-world data science project, from collecting and processing actual data to applying suitable and appropriate analytic methods to the problem. Both the problem statements for the project assignments and the datasets originate from real-world domains similar to those that students might typically encounter within the industry, government, non-governmental organizations (NGOs), or academic research. Students will work individually or in small teams on a problem statement. Research groups (both from within, as well as external to Contech) may propose projects. A list of possible projects will be posted early in the semester so students can align themselves with problem statements corresponding to their interests. As the project and problem statements warrant, students may be permitted to organize into teams of two to three participants. Teams larger than three will be considered for approval on a case-by-case basis. Each project team will be supervised by the Course Instructor and advised by a Project Coach assigned from the academic, governmental, NGO, or industry sponsor. The final problem statements and the composition of the teams will be approved by the Course Instructor.
Notice to Prospective Degree Program Students This institution is provisionally approved by the Bureau for Private Postsecondary Education to offer degree programs. To continue to offer this degree program, this institution must meet the following requirements: • Become institutionally accredited by an accrediting agency recognized by the United States Department of Education, with the scope of the accreditation covering at least one degree program. • Achieve accreditation candidacy or pre-accreditation, as defined in regulations, by (date two years from date of provisional approval), and full accreditation by (date five years from date of provisional approval) If this institution stops pursuing accreditation, it must: • Stop all enrollment in its degree programs, and • Provide a teach-out to finish the educational program or provide a refund.
Esteemed

Faculty Members

The program brings together leading academicians and industry experts to give you a practical understanding of core concepts. 

Nail Senbas

Arnold Jianwei Zheng

Adjunct Professor

Emmanuel Tsukerman

Adjunct Professor

Louis Sapia

Adjunct Professor

Atlas Khan

Adjunct Professor

Farhad Malek Ashgar

Associate Professor

Robert Kumar

Adjunct Professor

Admission

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.

01

Application

*Create your account in My Contech*Apply to the selected program with the start date.*Fill out the application form and submit.
02

Selection

In this step, you need to complete two short assessments, upload necessary documents and upload a short video answering the requested questions (or you may also book a zoom interview with an admissions officer). Admission committee will review your documents and test scores to give you a final answer for acceptance.
Documents to be uploaded:*Certificate of graduation*Transcript*Proof for English Proficiency (for international applicants)
03

Enrollment

If you are accepted to the university, you will be receiving your acceptance letter. Additionally, you will be receiving your enrollment agreements alongside the school catalog and school performance sheet to sign digitally, within 3 days. After signing the enrollment documents you are officially enrolled as a Contech student. The community team will be welcoming you to the university and you will be joining our hub.
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INNOVATIVE WAY FOR TUITION

Study Now, Pay Later!

Without any payments, you can enrol, study and graduate to receive your degree. Then, let us also help you to get you a job. Only then, you pay the tuition fee in installments of $500 a month.
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Upfront Payment

This option allows you to pay for your whole tuition at the start of your program and receive a 20% discount.

Monthly Payment

If you choose this option, we divide your tuition into 12 installments, which you can pay every month as you progress in your studies.
AITuition

Lowest Possible Cost

Lowest Possible Cost

Tuition

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.

Number of credits in program
30
Cost per credit hour
$660
Total tuition for program
$19,800
Who Should

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
  • Programming
  • Database Administration
  • Statistics
  • Science Marketing
  • Consultancy

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.

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