Business Analytics courses have slowly started to gain momentum, as it has emerged as one of the most lucrative career options whether it be in terms of salary or growth. As almost all businesses use statistical methods and technologies for analyzing historical data, business analytics professionals are in demand. Business analytics helps in gaining new insight and improving the strategic decision-making process. If you are looking to join a business analytics course, then Harvard Business School is here offering five online business analytics courses that can develop a data mindset and improve the ability to interpret data. These courses have been designed and taught by world-renowned Harvard faculty. These courses can help you make better business decisions as an individual, leader, or manager. Here are more details of the Online Business Analytics Courses at Harvard Business School.
Instructor: Professors Michael D. Smith and Jim Waldo
Fees: $1,600
Term: 5weeks, 5hrs/week
Data Privacy and Technology course will uplift you to think critically about the trade-offs and challenges that are presented by the ever-changing role of technology. The course will teach you about the legal and ethical implications of one's personal data, the risks and rewards of data collection, and surveillance and the need for policy, advocacy, and privacy monitoring. The structure of the course has five modules covering privacy, data collection, data use, and data reuse, technology, and price of privacy.
Apply here by October 11
Instructor: Professor Raj Chetty
Fees: $1,600
Term: 4weeks, 5hrs/week
Big Data for Social Good will help you with how to use various tools of modern data science to analyze diverse social questions that are important from improving equality of opportunities to tracking racial disparities in the education system. With real-world data and privacy interventions as applications, this course will equip you with various concepts in economics and statistics to handle the challenges of the present and future. The course consists of eight modules addressing the geography of upward mobility, effects of neighborhoods, characteristics of high mobility areas and policies, historical and international evidence on drivers of inequality and mobility, higher education, K-12 education, racial disparities, and economic opportunities.
Apply here by September 21
Instructor: Professor Dustin Tingley
Fees: 1,600
Term: 4weeks, 5hrs/week
Data science principles is an online course that is in collaboration with the Harvard Business School Online that gives an overview of data science with a code and math-free ranging from introduction to prediction, causality, data wrangling, privacy, and ethics. The course comprises four modules dealing with Data 101, predictions and recommendations, cause and effects, data governance and privacy, and data science ecosystems.
Apply here by September 13
Instructor: Professor Janice Hammond
Fees: $1,600
Term: 8weeks, 5hrs/week
Business analytics is helpful in demystifying data and strengthening your analytical skills. It starts with basic descriptive statistics and progresses into regression analysis. The student can implement analytical techniques in Excel and apply fundamental quantitative methods to real business problems. It equips students with A/B testing on a website to use sampling to check warehouse inventory. The course consists of five modules covering data describing and summarizing, sampling and estimation, hypothesis testing, single variable linear regression, and multiple regressions.
Apply here by October 4
Instructor: Professor Yael Grushka-Cockayne
Fees: $1,600
Term: 4weeks, 5hrs/week
Data science for business analytics courses teach you how to effectively use data and handle business decisions. This course is designed for managers and provides a hands-on- approach for demystifying the data science ecosystem that can help them make more decisions. The course has six models dealing with data science shifts, data wrangling, visualization, time series forecasting, linear regressions, logistic regressions, and machine learning.
Apply here by September 6
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