MS in Data Science at Harvard

Harvard University is a private ivy-league research university established in 1636. It is one of the top-most universities in the US. It is widely acclaimed for its focus on innovation, invention, and international exposure. It is very popular for the top notch courses it provides in the field of engineering. One of these popular courses is M.S. in Data Science.

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. Data science has applications in several fields like healthcare, business, artificial intelligence etc.

Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains. The program 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. Before applying MS Abroad you may check the following facts:

Admissions

Students have to submit their applications online. Admissions to this program are provided through the Graduate School of Arts and Sciences (GSAS). Students are required to possess a bachelor’s degree. GSAS considers students for admission to the fall term only and the applications for this course will be accepted from September 2018.

Learning outcomes

The design of the program was developed through discussions between the computer science and statistics faculty at Harvard. Following are the learning outcomes for this course:

  • Obtain, cleanse, and manipulate data
  • Analyse data for exploration and communication
  • Working in a team environment
  • Deliver reproducible data analysis
  • Management and evaluation of massive data sets
  • Assembling of the computational pipelines to enhance data science related processes
  • Understanding of policies, privacy, safety considerations
  • Using problem-solving ability to reach better solutions

Jobs prospects

In 2012, when Harvard Business Review called it “The Sexiest Job of the 21st Century”, the term “data science” became a buzzword. More recently, Glassdoor named data scientist as the best job of the year 2016. The data science has applications in various areas, leading to more job creation for the data science course pass outs. Here are some prominent job profiles related to data science.

Data Scientist

Data scientists are responsible for setting up a data infrastructure for the companies, which have a lot of traffic. They are required to cleanse and organize the data by using a feasible infrastructure. This job profile involves the usage of distributed computing, predictive modelling and other software engineering related skills.

Data Analyst

The data analyst is responsible for collecting, processing and performing data analysis. A data analyst may have to analyse the results of an A/B test or take the lead on your company’s Google Analytics account. The person should have an expertise in using SQL database and spreadsheet tools like Excel. Knowledge of maths, stats and machine learning will be an additional advantage for better data optimization.

Data Architect

The responsibility of a data architect is to create blueprints of the data management systems to integrate, centralize, protect and maintain data sources. He must be able to provide data warehousing solutions and have an in-depth knowledge of database architecture. A person in this role builds the framework of the database for easy retrieval of the required information. The data architect should master technologies like Hive, Pig, Spark and needs to be on top of every new innovation in the industry.

Data Engineer

A data engineer has a sound knowledge of the concepts related to software engineering. He is responsible for developing and maintaining large scale processing systems. A data engineer must also be able to provide efficient data warehousing solutions. A good understanding of the statistical programming languages and other programming languages is a must have for people in this profession.

Statistician

Statisticians generally have a quantitative background. They have a strong understanding of statistical theories and methodologies. Their mind set is more logic driven and stats oriented. A statistician has to collect, analyse and interpret both qualitative and quantitative data by making use of the statistical theories and methods.

Database Administrator

A database administrator has to make sure that the data is always available to the users. He has to ensure that the database is working properly and kept safely. In order to ensure the safety, the backup and recovery systems are maintained by the database administrator. Knowledge of SQL and programming languages like Ruby on Rails, XML, Python is required for a statistician to perform his tasks.

Business Analyst

The process of data modelling is carried out by a business analyst. A business analyst has the knowledge related to different business processes. He is a master at the skill of linking the data to aid the business growth. Business analysts bridge the gap between technical and business teams. Knowledge of data visualization tools like Tableau and business intelligence is required for people in this profession to execute their tasks.

Salary

The salary of people working in these profiles is quite handsome both in India and abroad. According to PayScale, the average salary of Data Scientists in the US is $139,840 per annum.

Check out our article on the Advantages of applying early MS

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