Data is transforming and powering businesses everywhere. Companies now look for candidates who possess a deep understanding of data mechanics and have the capacity to identify data-driven insights. As the name suggests, data science is the study and analysis of data to extract meaningful information. It covers the entire scope of data collection and processing. Data Science accounts for any data analysis, which is more closely associated with Mathematics and Statistics. It may or may not involve computer processing. However, due to the unprecedented amount of data, computerized processing of data has become an integral part of Data Science. Now there is a need for Data Science experts in almost every sector, be it technical or non-technical. Data Science is used mainly for making decisions and predictions based on predictive analysis, prescriptive analysis, and machine learning.
Most of the data scientists are very high-qualified with a Master's degree or PhDs. However, there are exceptions, but it still requires a strong educational background. Data Science jobs are one of the most analytical roles in the market, which require expertise in programming and applied science along with skills in Machine Learning, Apache Hadoop, Data Mining, SQL, Python, and Data Analysis.
Best Data Science Degree in 2024
Here we have consolidated some of the most popular Data Science Degree Programmes, which will help you to understand this subject matter and prepare yourself with relevant skills.
1. Post Graduate Program In Business Analytics And Business Intelligence (PGP-BABI) By Great Learning In Association With Great Lakes Institute of Management
Course duration: 12 Months
PGP-BABI uses a combination of learning methods such as classroom teaching, self-learning through videos and reading materials, team-based problem solving, and others. The exhaustive course curriculum includes foundational courses (foundation in Statistics using R, business, and management concepts), analytical techniques (R, Python, Tableau SAS), and domain application and industry exposure. They have a capstone project of 3 months, while the course is updated every 6 months.
You can signup here.
2. Post Graduate Program in Data Science and Machine Learning (PGPDM) By Jigsaw Academy And University Of Chicago
Duration of the Course: 10 months
PGDM includes exhaustive coverage of Data Science and Machine Learning (R, Python & SAS), Big Data (Hadoop, HDFS, Pig, Hive, and Spark), and Visualization (Tableau). The program starts from the basics of statistics and covers the entire gamut of descriptive analytics, predictive analytics, as well as A.L. & ML. It includes a hands-on program from case studies across BFSI, retail, telecom, supply chain, H.R., and other industries. The capstone project is for more than 3 months, and the course is updated every 6 months.
You can signup here.
3. Post Graduate Certificate Program In Data Science & Machine Learning By Manipal ProLearn In Association With Manipal Academy Of Higher Education
Duration of the Course: 6 months
Designed with an optimal blend of rigor and relevance, it covers in-depth and industry-relevant content. The course comprises of real-world business case studies and consists of online lectures from industry practitioners. It covers Java programming, advanced excel, R, introduction to Python, data visualization tools, big data technologies, business communication, and others. They do not offer capstone projects, and the course is updated every 6 months.
You can signup here.
4. P.G. Program in Data Science By UpGrad In Association With IIITB
Duration of the Course: 11 months
The curriculum has been designed along with IIIT Bangalore and multiple industry leaders. The broad areas of focus are data management (Excel, Python, SQL, Tableau), statistical and exploratory data analysis, machine learning, big data analytics (Hive, Spark, Sqoop). The candidates choose the Specialization of their choice in BFS, eCommerce, retail, or healthcare. The course includes a capstone project of 1-3 months. The course is updated every 6 months.
You can signup here.
5. Professional Certificate in Python Data Science [IBM]
This Python course provides a beginner-friendly introduction to Python for Data Science. You will learn how to analyze data in Python using multi-dimensional arrays in NumPy, manipulate DataFrames in pandas, use the SciPy library of mathematical routines, and perform machine learning using scikit-learn. Data visualization is the graphical representation of data to interactively and efficiently convey insights to clients, customers, and stakeholders in general. Machine Learning with Python helps you uncover hidden insights and predict future trends. It provides you with all the tools you need to get started with supervised and unsupervised learning. You have to do a project that you can use to showcase your Data Science skills to prospective employers. Apply various data science and machine learning techniques to analyze and visualize a data set involving a real-life business scenario and build a predictive model.
You can signup here.
6. Data Science Specialization
Offered by: John's Hopkins University
Course Duration: Self-Paced.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you'll apply the skills learned by building a data product using real-world data. This course covers 10 modules.
- The Data Scientist Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Inference
- Regression Models
- Practical Machine Learning
- Developing Data Products
- Data Science Capstone
You can signup here.
7. IBM Data Science Professional Certificate
Offered by: IBM
Course Duration: Self-Paced.
This program consists of 9 courses providing you with the latest job-ready skills and techniques covering a wide array of data science topics, including open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You practice hands-on in the IBM Cloud using real data science tools and real-world data sets. By the end of this course, you would have several hands-on assignments and a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. The 9 modules covered by this course are:
- What is Data Science?
- Open Source Tools for Data Science
- Data Science Methodology
- Python for Data Science and A.I.
- Databases and SQL for Data Science
- Data Analysis with Python
- Data Visualization with Python
- Machine Learning with Python
- Applied Data Science Capstone
You can signup here.
8. Applied Data Science Specialization
Offered by: IBM
Course Duration: Self-Paced.
This course covers 4 modules.
- Python for Data Science and A.I.:
- Data Analysis with Python
- Data Visualization with Python
- Applied Data Science Capstone
You can signup here.
9. Data Science: Foundations using R Specialization
Offered by: John's Hopkins University
Course Duration: Self-Paced.
This Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. This Specialization is for learners who want to start and complete the foundational part of the curriculum first, before moving onto the more advanced topics in Data Science. The five modules in this Specialization are:
- The Data Scientist's Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
You can visit here.
10. Executive Data Science
Offered by: John's Hopkins University
Course Duration: Self-Paced.
In its four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You'll get a crash course in data science so that you'll be conversant in the field and understand your role as a leader. You'll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You'll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target. Finally, you'll learn some down-to-earth, practical skills that help you overcome the common challenges that frequently derail data science projects.
- A crash course in Data Science
- Building a Data Science Team
- Managing Data Analysis
- Data Science in Real Life
- Executive Data Science Capstone
You can signup here.
11. Genomic Data Science
Offered by: John's Hopkins University
Course Duration: Self-Paced
Genomic Data Science is the field that applies statistics and data science to the genome. This Specialization covers the concepts and tools to understand, analyze, and interpret data from next-generation sequencing experiments. It teaches the most common tools used in genomic data science, including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy. This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect complement to a primary degree or postdoc in biology, molecular biology, or genetics. It is for the scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work. The modules covered by this Specialization are:
- Introduction to Genomic Technologies
- Genomic Data Science with Galaxy
- Python for Genomic Data Science
- Algorithms for DNA Sequencing
- Command Line Tools for Genomic Data Science
- Bioconductor for Genomic Data Science
- Statistics for Genomic Data Science
- Genomic Data Science Capstone
You can signup here.
12. Professional Certificate in Data Science
Offered by: Harvard University
Course Duration: Self-Paced.
In this course, you first start with building a foundation in R and learn how to wrangle, analyze, and visualize data. You can learn essential data visualization principles and how to apply them using ggplot2. Using a case-study, you can learn probability theory. The other modules covered by this course are:
- Data Science: Productivity Tools - Helps keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.
- Data Science: Wrangling - Teaches how to process and convert raw data into formats needed for analysis.
- Data Science: Linear Regression - Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.
- Data Science: Machine Learning - Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
- Data Science: Capstone - By completing this capstone project, you get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project tests your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.
You can signup here.
13. Machine Learning A-Z™: Hands-On Python & R In Data Science
This course dives deep into Machine Language. It is structured in the following way:
- Part 1 - Data Preprocessing
- Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 - Clustering: K-Means, Hierarchical Clustering
- Part 5 - Association Rule Learning: Apriori, Eclat
- Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Further, this course is packed with practical exercises that are based on real-life examples.
You can signup here.
14. The Data Science Course 2019: Complete Data Science Bootcamp
The course provides the entire toolbox you need to become a data scientist. Data science is a multidisciplinary field. It encompasses a wide range of topics. Each of these topics builds on the previous ones. This course is an effort to create the most effective, time-efficient, and structured data science training by having all the necessary resources in one place. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs. The skills covered in this topic are:
- Introduction to Data Science
- Mathematics
- Statistics
- Python
- Tableau
- Advanced Statistics
- Machine Learning
You can signup here.
15. Data Science A-Z™: Real-Life Data Science Exercises Included
This course gives you a full overview of the Data Science journey. Upon completing this course, you will understand:
How to clean and prepare your data for analysis, how to perform basic visualization of your data, how to model your data, how to curve-fit your data, and finally, how to present your findings and wow the audience. You also develop a good understanding of the following tools: SQL, SSIS, Tableau, and Gretl.
You can signup here.
16. Big Data and Data Science Master's Course
This course helps you gain proficiency in Big Data and Data Science. You work on real-world projects in Hadoop Dev, Admin, Test and Analysis, Apache Spark, Scala, AWS, Tableau, Artificial Intelligence, Deep Learning, Python for Data Science, SAS, R, Splunk Developer and Admin, NoSQL databases and more. This program covers 20 courses and 56 industry-based projects. This is a comprehensive course that is designed to clear multiple certifications, namely:
- CCA Spark and Hadoop Developer (CCA175)
- Splunk Certified Power User Certification
- Splunk Certified Admin Certification
- Tableau Desktop Qualified Associate Exam
- SAS Certified Base ProgrammerExam
- C100DEV: MongoDB Certified Developer Associate Exam
- Apache Cassandra DataStax Certification
- Linux Foundation Linux Certification
- Java S.E. Programmer Certification
You can signup here.
17. Data Science Architect Master's Course
Through this course, you gain proficiency in Data Science. You work on real-world projects in Data Science with R, Apache Spark, Scala, Deep Learning, Tableau, Data Science with SAS, SQL, MongoDB, and more. In this program, you cover 12 courses and 48 industry-based projects with 1 CAPSTONE project. As a part of online classroom training, you receive five additional self-paced courses co-created with IBM, namely Deep Learning with TensorFlow, Build Chatbots with Watson Assistant, R for Data Science, Spark MLlIb, and Python for Data Science. You also get exclusive access to the IBM Watson Cloud Lab for the Chatbots course.
You can signup here.
Data Science Degree Program in University
1. Carnegie Mellon University, Pittsburgh, Pennsylvania
Name: Master of Computational Data Science
Course duration: 2 years
Core courses: Machine Learning, Cloud Computing, Interactive Data Science, and Data Science Seminar
You can signup here.
2. Columbia University, New York City, New York
Name: Master of Science in Data Science
Course Duration: 1.5 year
Core courses: Probability Theory, Algorithms for Data Science, Statistical Inference and Modelling, Computer Systems for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization
You can signup here.
3. Cornell University, Ithaca, New York
Name: Master of Professional Studies (MPS)in Applied Statistics
Course Duration: 1 year
Core courses: Linear Models with Matrices, Probability Models and Inference, MPS Professional Development, and Applied Statistics MPS Data Analysis Project
You can signup here.
4. Georgia Institute of Technology, Atlanta, Georgia
Name: Master of Science in Analytics
Course Duration: 1 year
Core courses: Big Data Analytics in Business, and Data and Visual Analytics,
You can signup here.
5. New York University, New York City, New York
Name: Master of Science in Data Science
Course Duration: 2 years
Core courses: Introduction to Data Science, Probability and Statistics for Data Science, Machine Learning, and Big Data
You can signup here.
6. Northwestern University, Evanston, Illinois
Name: Master of Science in Analytics
Course Duration: 15 months
Core courses: Coursework in statistics, machine learning, optimization, databases, and data management form the core of the fixed MSiA curriculum.
You can signup here.
7. North Carolina State University, Raleigh, North Carolina
Name: Master of Science in Analytics
Course Duration: 10 months
Courses: Has fixed curriculum with an emphasis on Analytics Tools and Techniques, Analytics Foundations, Analytics Methods and Applications I, and Analytic Methods and Applications II
You can signup here.
8. Stanford University, Stanford, California
Name: M.S. in Statistics: Data Science
Course Duration: 2 years
Core courses: Numerical Linear Algebra, Discrete Mathematics and Algorithms, Optimization, Stochastic Methods in Engineering or Randomized Algorithms and Probabilistic Analysis, Introduction to Statistical Inference, Introduction to Regression Models and Analysis of Variance or Introduction to Statistical Modeling, Modern Applied Statistics: Learning, and Modern Applied Statistics: Data Mining
You can signup here.
9. University of California, San Diego
Name: Master of Science in Computer Science (AI depth)
Course Duration: 2 years
Depth area: Artificial Intelligence. One can choose courses such as A.I.: Probabilistic Reasoning and Learning, Machine Learning, Neural Networks, Statistical Learning Theory, among many others.
You can signup here.
10. University of Chicago, Chicago, Illinois
Name: Master of Science in Analytics
Course Duration: 1.5 years
Core courses: Statistical Analysis, Linear and Nonlinear Models for Business Applications, Machine Learning and Predictive Analytics, Time Series Analysis and Forecasting, Data Mining Principles, Big Data Platforms, Data Engineering Platforms for Analytics, and Leadership Skills: Teams, Strategies, and Communications
You can signup here.
11. University of Colorado, Boulder
Name: Professional Master of Science in Computer Science
Course Duration: 2 years
Courses: Machine Learning, Neural Networks, and Deep Learning, Natural Language Processing, Big Data, HCC Big Data Computing and many more
You can signup here.
12. University of Minnesota- Twin Cities, Minneapolis, Minnesota
Name: Master of Science in Data Science
Course Duration: 2 years
Core courses: Introduction to Data Mining, Principles of Database Systems, Applied Regression Analysis, Introduction to Parallel Computing: Architectures, Algorithms, and Programming, Introduction to Nonlinear Optimization, and Applied Multivariate Methods
You can signup here.
13. University of Maryland, College Park
Name: Master of Science in Computer Science
Courses to choose for Data Science: Design and Analysis of Computer Algorithms, Introduction to Artificial Intelligence and Machine Learning
You can signup here.
14. University of Pennsylvania, Philadelphia, Pennsylvania
Name: Master of Science in Engineering in Data Science
Course Duration: 1.5 years
Core courses: Intro to Probability & Statistics, Programming Languages & Techniques, Mathematical Statistics, Big Data Analytics, and Machine Learning
You can signup here.
15. Rutgers University, New Brunswick, New Jersey
Name: Master of Science in Data Science
Course Duration: 2 years
Core courses: Probability and Statistics, Data Structures and Algorithms, Massive Data Storage and Retrieval Tools, Massive Data Mining, and Data Interaction and Visual Analytics
You can signup here.
16. University of Southern California, Los Angeles, California
Name: Master of Science in Computer Science (Data Science track)
Course Duration?
Core courses: Analysis of Algorithms, Database Systems, and Foundations of Artificial Intelligence
You can signup here.
17. University of Texas, Austin, Austin, Texas
Name: Master of Science in Computer Science (Application Track)
Course Duration: 2 years
Core courses: To be chosen by the student
You can signup here.
18. University of Texas, Dallas
Name: Master of Science in Computer Science ( Data Sciences Track)
Course Duration: 2 years
Courses: Machine Learning, Design, and Analysis of Computer Algorithms, Big Data Management and Analytics, and Statistical Methods for Data Science
You can signup here.
19. The University of Washington, Seattle, Washington
Name: Master of Science in Data Science
Course Duration: 2 years
Core courses: Introduction to Statistics & Probability, Information Visualization, Applied Statistics & Experimental Design, Data Management for Data Science, Statistical Machine Learning for Data Scientists, Software Design for Data Science, Scalable Data Systems & Algorithms, and Human-Centered Data Science.
You can signup here.
Conclusion
Data Scientists are now in very high demand because not many people possess the required skill sets. Almost all businesses and organizations rely on big data to make customer-centric decisions. Hence, there is a continuous need for such professionals who understand the business needs, can devise and implement a data-oriented solution. Some of the top data science careers include Business Intelligence Developer, Data Architect, Applications Architect, Infrastructure Architect, Enterprise Architect, Data Scientist, Data Analyst, Data Engineer, Machine Learning Scientist, Machine Learning Engineer, and Statistician. Jobs in this profile are in the increasing trend; hence, it is a perfect time to get yourself on board.
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