Today's businesses no longer view data as an add-on but rather as a vital component of their whole company strategy. Organisations acquire a competitive edge and solve both immediate and long-term business issues by properly utilising data analytics. Businesses are seeking data leaders who can transform data into actionable insights. With the support of Columbia Engineering's Executive Business Analytics Programme training in collaboration with Emeritus, students can turn data into effective resources that can be used to make business choices.
The demands of busy executives have been taken into consideration while structuring the nine to twelve-month learning path. Online classes will be used to teach students, and they will feature guest lectures from well-known business figures. The Executive Business Analytics Program's flexibility enables them to personalise their learning path for the greatest possible effect on their career and business.
The Executive Business Analytics Programme certification fee is US$18,000.
Fee structure
Participants can make immediate payments or they can select the instalment option to make payment.
Course
Amount
Executive Business Analytics Programme
US$18,000
Eligibility Criteria
Academic Qualification
Fluency in written and spoken English
Work Experience
A minimum of 8 years of work experience is required.
Candidates having International exposure will be preferred.
Certification Qualifying Details
A digital Executive Business Analytics Programme Certification by Emeritus will be given to participants who proficiently complete the training.
What you will learn
LeadershipDecision making skillsMachine learningKnowledge of Artificial Intelligence
The curriculum is structured to incorporate a variety of learning touchpoints, including core modules, electives, and a capstone project. As you progress through the learning process, learners will:
Obtain practical experience using data exploration tools to extract and prepare data for application.
Use Python code to examine and display huge data from the real world.
Learn the fundamentals of artificial intelligence and machine learning to investigate how artificial neural networks resemble human brains.
Use optimisation methods to address common issues in the workplace.
Develop their communication abilities to lead data science teams more successfully.
Learn the steps and underlying principles of using data to aid decision-making in corporate contexts.
Examine consumer and economic data, and choose the best models to provide the proper business answers.
This programme is appropriate for CEOs and CXOs looking to create data analytics teams and functional heads or technical leads in software engineering and software product development. Business executives who want to learn how analytics might boost performance in their company they can gain from this learning opportunity.
Admission Details
Students can enrol in Executive Business Analytics Programme classes by following the steps:
Step 1: Browse the official URL: https://online-exec.cvn.columbia.edu/executive-business-analytics-program
Step 2: Click the apply now option, log in from the registered email ID and make the payment.
Step 3: After receiving a confirmation email, the students can start taking the classes.
Application Details
Students first need to browse the official website, register for the course by logging in with their email ID and creating an account then make the payment to start with the Executive Business Analytics Programme online course.
The Syllabus
Fundamentals of Data Exploration
Introduction to Python for Analytics
Basic Data Cleaning
Introduction to Pandas
Introduction to Data Visualization and Bokeh
Serialization and Regex
Data Harvesting
Visualization with Geographical Data
Applied Machine Learning and Artificial Intelligence
Visual Introduction to Optimization
Delving into Optimization
Regression and Machine Learning: A Visualized Walk-Through
What Is Deep Learning?
Deep Learning Applications
Overfitting
Business Analytics and Data Science
Introduction to Business Analytics and Basics of Descriptive Analytics