Become a Data Scientist by mastering the skills to interpret data, formulate insights, and communicate your knowledge by applying machine learning, managing large data sets, and generating interesting visualizations.
- Start, study, graduate and get a job!
- No tuition fee until you are hired.
- Starting date: Jan 2022
Master's Degree Program with Employability Focus
As data accumulates across broad sectors of 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. A famous example from academia is the determination of the Higgs Boson from simulated data with machine learning methods.
Master of Science in Computer Science with Specialization in Data Science program covers basic and advanced essentials in statistical inference, machine learning, data visualization, data mining, and big data methods, all of which are key for a trained data scientist. To be selected for our program, we require a basic background in calculus, linear algebra, probability, computer programming, data structures, and algorithms. Our program is spread across 30 credits and contains projects involving big datasets, classification methods, variable selection, and deep learning to name a few.
become an expert on Data Science
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You Will Acquire Skills For
- Data Science
- SQL databases
- Data Cleaning
- Data Manipulation
- Data Visualization
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.
This course will explore the fundamental principles and techniques of the Python programming language 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. Students will further develop data-analytic thinking. This course will illustrate that 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.
This course will expose students to essential toolsets to conduct data-related analysis. Students 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. Students further 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, including how to clone a project to your local machine, iterate on the project by creating branches, and push your work to Git remotes like Github. Students will also learn the basics of this critical skill and start building some experience working with SQL databases to explore and analyze data in SQL through hands-on active learning.
In this course, students will get an introduction to statistics and how this mathematical discipline is used in data science. Students will also learn to measure variability using variance or standard deviation, and how to locate and compare values using z-scores. This course will cover the fundamental rules of probability, and then work to solve increasingly complex probability problems with techniques like permutations and combinations. Students will be expected to understand the difference between theoretical and experimental probability. Students then will be exposed to advanced statistical concepts such as significance testing and multi-category chi-square testing for more powerful and robust data analysis. Students will learn about single and multi-category chi-square tests, degrees of freedom, hypothesis testing, and different statistical distributions. And students will work hands-on with multiple datasets to learn statistical concepts.
In this course, students will learn how to supercharge data analysis workflow with cleaning and analytical techniques from the Python pandas library. Students will learn concepts such as group by objects to solve split-apply-combine problems faster. Students will also learn how to use pandas to create pivot tables, concatenate data, and merge data to solve complex data problems as well as look at your data in a completely different way.
In this course, students will be introduced to Panda’s DataFrames to import and inspect a variety of datasets and practice building DataFrames from scratch, and become familiar with Pandas’ intrinsic data visualization capabilities. Students will learn and apply exploratory data analysis (EDA). Students will learn how to manipulate and visualize time series data using Pandas. 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.
In this course, students will learn about the different number of resources to explore and showcase data in an easy and digestible way. This course will cover how to use matplotlib to create visualizations such as line charts, bar plots, scatter plots, histograms, and box plots to better understand your data and help others understand your data as well.
In this course, students will learn about the basics of machine learning. This course will cover concepts such as K-Nearest Neighbors (KNN) Algorithms and error metrics such as the Mean Squared Error and the Root Mean Squared Error. Students will also learn about hyperparameter optimization, a technique used to optimize machine learning algorithms to boost the accuracy and performance of trained models. Then students will dig into k-fold cross-validation to perform more rigorous testing for machine learning models.
In this course, students will learn additional algorithms such as logistic regression and k-means clustering. Students will also learn about concepts on how to detect overfitting and the bias-variance tradeoff.
Students will be introduced to understanding model performance using sensitivity and specificity as it relates to classification models. Students will be exposed to clustering, an unsupervised learning technique designed to find patterns in data and group data into clusters that are closely related. Students will discover the difference between supervised and unsupervised learning, as well as when it makes sense to use each type of machine learning.
Students will learn the basics of deep neural networks. Students will be introduced to Scikit-learn to build and train neural networks. Students will learn concepts such as graph theory, activation functions, hidden layers, and how to classify images.
In this course, students will learn concepts such as the Naive Bayes theorem, Naive Bayes classifiers, and the K-Nearest Neighbors algorithm (KNN). Students will also learn the concept of Euclidean distance and how it plays a role in the kNN algorithm, and how to evaluate the mean squared error for predictions that are the kNN algorithm predictions.
Students will use Naive Bayes classifiers —figuring out how likely data attributes are associated with a certain class — to classify movie reviews based on sentiment, to perform sentiment analysis. Additionally, this course will cover how to compute prediction error using the receiver operating characteristic curve, which tells how good a model is.
In this course, students will learn the basics of natural language processing while analyzing stories from Hacker News to make predictions about how popular an article will be. Students will learn about concepts like stopwords, the bag of words model, and tokenization. This course will build intuition from the ground up by using libraries like Natural Language Toolkit (NLTK) and spaCy.
The purpose of the Capstone Project in Data Science 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.
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.
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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?
Professionals who want to increase their earning potential, advance their careers and make a greater impact within their business or organization with advanced data analytic and coding skills.
The Master of Science in Computer Science with specialization in Data Science is well suited for a variety of professionals in the following fields:
- Business analysis
- Data analysis
- Financial analysis