Masters in Data Analytics in USA
What is Data Analytics?
Data analytics is the science of extracting patterns, trends and actionable information from large sets of data for business intelligence. Business intelligence is the way in which companies use data to improve management and operations. Data analytics therefore, refers to qualitative and quantitative techniques and processes where data is extracted and classified to identify and analyze behavioral data and patterns used to enhance productivity and business gain.
Data analytics is primarily conducted in business-to-consumer (B2C) applications. Today, global organizations collect and analyze data associated with customers, market economics, user experience, purchasing trends and business processes; proper analysis of which reveals key user trends and facilitates appropriate alignment of content and strategy for social media network.
What does a Data Analyst do?
The responsibility of a data analyst varies across various industries and organizations. But the common function comprises using data to draw insights and solve problems. Data analysts usually analyze well-defined sets of data by employing different tools to support and fulfill tangible business needs. Data analysts need not have a mathematical or research background to invent new algorithms, but need to have is a strong understanding of how to use existing tools to solve problems.
Skills for pursuing a Masters in Data Analytics
To have a baseline understanding of this core competencies:
Statistical Language like SAS, R, Python
Programming- Querying Language: SQL, Hive, PIG, and Scripting Language: MATLAB, Python
Data visualization: charts and graphs via excel, Microsoft Power BI, Oracle Visual Analyser, SAS Visual Analytics, Tableau
Machine learning: regression, classification and segmentation
Data mining/data warehouse, data modeling
Organized in managing multiple tasks, data programs, and data flows
Presentations skills to present their analysis visually and/or verbally.
Prerequisites for a Masters in Data Analytics
Interest in data science
Programming experience – Python and R (preferably Python)
Strong understanding of programming concepts such as variables, functions, loops, and basic data structures like lists and dictionaries.
Understand concepts of Algorithms and Data Structures- How algorithms work and how and why they are dependent on Data Structures.
Database: Basic knowledge of MySQL and MongoDB is sufficient.
Mathematics: Algebra, calculus, Statistics and probability (strong grasp of descriptive and inferential statistics)
Machine learning topics- few or all from Predictive Analytics, Computer Vision, Natural Language Processing and Deep Learning Excel (VBA with Macros) is Mandatory along with knowledge of tools like Hadoop, Spark to deal with large data sets.
Course Content for MS in Data Science
Courses in MS will give students hands-on experience with analysis techniques such as multiple regression and logistic regression to find critical patterns within datasets.
MS provides students with the skills needed to extract meaning from raw data, develop analytic dashboards for non-technical users as well as build charts and other visually appealing presentations to thoroughly explain their findings. The program typically focuses on mathematical and programming skills and tools; this provides with the knowledge they need to transform their organization’s data into a usable asset.
Major Subjects for MS in Data Science
Machine Learning, Visualization
Statistics and Inference Modeling
Probability and Statistics for Data Science
Parallel and Distributed Data Management
Introduction to Regression Models and Analysis of Variance
Business Intelligence from Big Data
Paradigms for Computing with Data
Modern Applied Statistics: Learning, Data Mining
Scientific and Analytical Visualization
Probabilistic Modeling and Statistical Computing
Mathematical Tools for Data Science
Optimization and Computational Linear Algebra
What does Big Data Analytics mean?
Big data analytics refers to the strategy of analyzing large volumes of data, allowing data scientists and other users to evaluate large volumes of transaction data and other data sources that traditional business systems would not be capable to tackle. Sophisticated software programs are used for big data analytics, but the unstructured data used in big data analytics may not be well suited to conventional data warehouses. Big data’s high processing requirements may also make traditional data warehousing a poor fit. As a result, newer, bigger data analytics environments and technologies have emerged, including Hadoop, MapReduce and NoSQL databases. These technologies make up an open-source software framework that is used to process huge data sets over clustered systems.
Top Universities for MS in Data Analytics and Data Science
University of California – Berkeley
University of Southern California
Johns Hopkins University
University of Chicago
Indiana University Bloomington
Louisiana State University
University of Massachusetts, Amherst
New York University
University of Illinois at Chicago
Rochester Institute of Technology
University of Maryland University College
University of Washington
George Mason University
What can you do with a Masters in Data Analytics?
Some of the top jobs in data analysis involve using data to make investment decisions, target customers, assess risks, or decide on capital allocations at banks, hedge funds, private equity firms. Data Analysts also work in the healthcare industry, marketing, and retail, large insurance companies, credit bureaus, technology firms, and in almost any industry. Many companies also label data analysts as information scientists which typically involve working with a company’s proprietary database. The government sector employs and relies on information scientist for data collection, mining and analysis. Insurance and health care companies also have deep data infrastructures that require information scientists as well. Average mean salary for a Financial Analyst is around US$80000, for a Market Research Analyst is US$70000.
Business analyst, Operations Analyst
Management reporting, Corporate strategy analyst
Compensation and benefits analyst as part of a human resources department
Budget analyst, Credit analytics, Fraud analytics
Insurance underwriting analyst, Healthcare Data Analyst
Sales analytics, Web analytics, Social media data analyst
Machine learning analyst, IT Systems Analyst, Quantitative Analyst
STEM Degree – Master’s in Data Analytics
Masters in Data Analytics at most US Universities is a STEM degree. This means, you can thus work in the US for upto three years after studying MS in Data Analytics. Learn more about STEM Courses in the US
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