Master in Data Science

Master in Data Science

Online | 18 Months

Master of Science in

Data Science

By Birchwood University

Most Promising Global Edutech Platform-2022
for Higher Education By the Times Group

Only Few Seats Left   |   No Prior Coding Experience Required

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Program Highlights

18-24 Months Duration

Capstone Projects

Online Lab Sessions

Highly Experienced Faculties

Certificate of Completion

24*7 Online Support

Interactive Online Learning

Holistic Curriculum

Licensure & Associations

Program Overview

The Master’s program in Data Science by Birchwood University is a Minimum 18 Months online professional program for students looking to start or advance a career in data science and offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition. The program focuses on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.


Why Data Science?

Data science helps businesses leverage social media content to capture real-time usage patterns of media content. It enables businesses to create targeted content, measure content performance, and recommend on-demand content. Retailers use data science to improve customer experience and customer retention.


Key Features

Access To Curated Jobs
Dedicated Career Services
Eminent International Faculty
Dedicated Career Support
Hands-On Approach To Ensure Success
Accredited Online Master's Degree Programs
Online Learning Format With Industry Mentorship
Real-World Projects & Case Studies
Robust Learning Management System

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Program Curriculum

MDS500 | Python Programming | 4 Credit Hours
Gain insight into the Python Programming language with this introductory course. An essential programming language for data analysis, Python, Programming is a fundamental key to becoming a successful Data Science professional. In this course, you will learn how to write python code, learn about Python’s data structures, and create your functions. After the completion of this course, you can represent yourself as an ideal candidate for python Developer
MDS530 | Exploratory Data Analysis | 4 Credit Hours
This course includes the necessary exploratory techniques for summarizing data. These techniques are typically implemented before formal modeling begins and can help in informing the development of numerous complex statistical models. Exploratory techniques are also essential for eliminating or sharpening potential hypotheses about the world that the data can address. In this course, we will study the plotting systems and the basic principles of constructing data graphics. We will also cover some of the standard multivariate statistical techniques used to visualize high-dimensional data
MDS550 | Machine Learning Model Deployment | 4 Credit Hours
Implementing models such as support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering, and more in Flask, Sending and receiving the requests from deployed machine learning models, Building machine learning model APls, and deploy models into the cloud, Design testable, version-controlled, and duplicate production code for model deployment.
MDS600 | Capstone Project | 4 Credit Hours
The capstone project will allow you to implement the skills you learned throughout this program. Through dedicated mentoring sessions, you’ll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in the learning path and will enable you to showcase your expertise in Data Science to future employers.
MDS510 | Data Base Management System | 4 Credit Hours
In this course, students will learn how to manage the Data Effectively using My SQL Work Bench. Students will come to know how to Apply Certain Joins techniques, How to manipulate the data. Will be able comfortably design SQL queries to add data to the database, will be familiar with editing, deleting data from the database, and will be able to describe and develop Relational Algebra and Relational Calculus queries.
MDS530 | Exploratory Data Analysis | 4 Credit Hours
This course includes the necessary exploratory techniques for summarizing data. These techniques are typically implemented before formal modeling begins and can help in informing the development of numerous complex statistical models. Exploratory techniques are also essential for eliminating or sharpening potential hypotheses about the world that the data can address. In this course, we will study the plotting systems and the basic principles of constructing data graphics. We will also cover some of the standard multivariate statistical techniques used to visualize high-dimensional data
MDS550 | Machine Learning Model Deployment | 4 Credit Hours
Implementing models such as support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering, and more in Flask, Sending and receiving the requests from deployed machine learning models, Building machine learning model APls, and deploy models into the cloud, Design testable, version-controlled, and duplicate production code for model deployment.
MDS600 | Capstone Project | 4 Credit Hours
The capstone project will allow you to implement the skills you learned throughout this program. Through dedicated mentoring sessions, you’ll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in the learning path and will enable you to showcase your expertise in Data Science to future employers.

Tools to be Covered

Capstone Projects

Test your skills and mettle with a capstone project.

Retail

Techniques used: Market Basket Analysis, RFM (Recency-Frequency Monetary) Analysis, Time Series Forecasting

E-commerce

Techniques used: Text Mining, Kmeans Clustering, Regression Trees, XGBoost, Neural Network

Web & Social Media

Techniques used: Topic Modeling using 9 Latent Dirichlet Allocation. K-Means & Hierarchical Clustering

Banking

Techniques used: Linear Discriminant Analysis, Logistic Regression, Neural Network, Boosting, Random Forest, CART

Supply Chain

Techniques used: Text Mining, Kmeans Clustering, Regression Trees, XGBoost, Neural Network

Healthcare

Techniques used: Logistic Regression, Random Tree, ADA Boost, Random Forest, KSVM

Retail

Techniques used: Logistic Regression, Random Tree, ADA Boost, Random Forest, KSVM

Insurance

Techniques used: NLP (Natural Language Processing), Vector Space Model, Latent Semantic Analysis

Entrepreneurship /Start Ups

Techniques used: Univariate and Bivariate Analysis, Multinomial Logistic Regression, Random Forest

Finance & Accounts

Techniques used: Conditional Inference Tree, Logistic Regression, CART and Random Forest

Why choose Birchwood University?

Enrol with leading global online educational course provider.

Affordability

No matter your financial circumstances, our goal is to reduce cost as a barrier to higher education.

Great Career Outcomes

An education at Birchwood has limitless possibilities. Our courses are taught by esteemed faculty members

Making Learners Career-Ready

Providing industry-level experience to carve a strong place for themselves in the job market and climb the professional success ladder.

Knowledge Base

The complete course material is broken down into smaller units. Students have to appear for the evaluation after completion of each unit.

Practical Approach

With the help of customised software tools students are exposed to real business situations and they have to take strategic decisions.

Holistic Student Development

Birchwood University aims to (re)build an academically focused education system to support holistic student development.

Our Learners From

We Have Learners For Our Data Science Program From Following Companies.

Admission Process

Enroll in the program with a simple online form.

Apply by filling a simple online application form.

Admissions committee will review and shortlist.

Shortlisted candidates need to appear for an online aptitude test.

Screening call with Alumni/ Faculty

FAQs

Find answers to all your queries and doubts here.
Q1 : What is the closing date for issue of applications?
A : You may contact the Birchwood University Admissions Office.
Q2 : Can I visit the campus to know more about the program and University?
A : Yes, you are welcome to visit the University on the given address to get to know more about the program and the University.
Q3 : Can I pursue the Data Science master’s degree part time while I am working?
A : The Data Science master’s degree is a full-time online program. All students are required to fulfill the criteria of 32 credits over the span of full course. So, you can do it according to your own preference and time.
Q4 : Is there financial aid available?
A : For financial aid, please visit the financial aid page of Birchwood University.
Q5 : When and how can I apply? Does the program have rolling admissions?
The Data Science master’s program uses the online application for the Birchwood University. You will find it here.

We do have rolling admissions. Each academic year is divided into three semesters of 16 weeks each described as Fall, Spring, and Summer. Each semester has three (3) Terms (Term A, Term B, Term C).
Q6 : What are the skills required to start a job in the field of Data Science?
A : Skills required to get a data science job are: Python coding, Hadoop platform knowledge, SQL database/coding, machine learning and AI work domain specific knowledge, data visualization skills, statistics, multivariate calculus, linear algebra.
Q7 : Who is eligible for taking the Masters in Data Science course from birchwood university?
A : Companies looking to hire data scientists are looking for the following degrees –

– for recent graduates – B.Tech/M.Tech (any profession), BCA, MCA, or B.Sc ( Degree in Statistics or Mathematics) ), BA (Mathematics or Economics or Statistics), B.Com.

– For Professionals – 1+ years of professional experience in Python, R, SAS, Business Intelligence, Data Warehousing, SQL. Even if your work experience is not related to data analytics, you can switch to a data science career with one of the above degrees.

However, no technical or programming skills are required to enroll in the Master’s Program in Data Science. Teach all modules from scratch.
Q8 : When and how can I apply? Does the program have rolling admissions?
The Data Science master’s program uses the online application for the Birchwood University.

We do have rolling admissions. Each academic year is divided into three semesters of 16 weeks each described as Fall, Spring, and Summer. Each semester has three (3) Terms (Term A, Term B, Term C).
What is included in this course?
  • Non-biased career guidance
  • Counselling based on your skills and prefrence
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while your work
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    I'm interested in this program

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