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Elizaveta Chernenko, Ilia Karpov, Valentina Kuskova, Kirill Mikhin, and Elena Beylina
This course is designed to open the doors of the world of business analytics. Nowadays a lot of organizations make their decisions based on data-driven approach. How to make the right decision? Which methods are used in multinational companies? This course is about demonstrating the diversity of real cases and applications of methods, techniques, and theories in various areas. Each week of this course is a piece of a puzzle where you will meet different experts from the industry who will share with you best practices from the market. Bringing together all the pieces you will understand the key definitions used in business...
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This course is designed to open the doors of the world of business analytics. Nowadays a lot of organizations make their decisions based on data-driven approach. How to make the right decision? Which methods are used in multinational companies? This course is about demonstrating the diversity of real cases and applications of methods, techniques, and theories in various areas. Each week of this course is a piece of a puzzle where you will meet different experts from the industry who will share with you best practices from the market. Bringing together all the pieces you will understand the key definitions used in business analytics and will learn about data analytics techniques which can be applied in marketing, sales, PR, HR, and finance. “Business Analytics: Diversity of Practical Applications” aims to help you to navigate in the variety of career opportunities which are opened for business analysts. This Course is part of HSE University Master of Data and Network Analytics degree program. Learn more about admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/WMKM6.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for beginners with a budding interest in data and business analytics
Serves as an accessible starting point for ambitious students looking to transition into the field of business analytics
Provides fundamental concepts and practical applications, suitable for professionals seeking to enhance their existing knowledge in business analytics
Facilitates exploring various methods and techniques used in real-world business analytics scenarios
Offers exposure to industry best practices from experts in the field
Can be used to explore career opportunities within business analytics

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Business Analytics: Diversity of Practical Applications with these activities:
Review basic statistics
Improve your understanding of foundational statistical concepts and techniques to enhance your grasp of data analytics.
Browse courses on Statistical Methods
Show steps
  • Review notes or textbooks on descriptive statistics
  • Practice solving statistical problems
  • Complete online quizzes or exercises on statistical concepts
Read 'Data Analytics for Business' by Thomas H. Davenport
Gain insights into the practical applications of data analytics in business decision-making and strategy.
Show steps
  • Purchase or borrow the book
  • Read the book thoroughly, taking notes and highlighting key concepts
  • Reflect on the implications of data analytics for your current or future career
Join a study group or discussion forum
Engage with peers to exchange ideas, clarify concepts, and enhance your understanding of business analytics.
Show steps
  • Find or create a study group with fellow learners
  • Set regular meeting times to discuss course materials
  • Actively participate in discussions and ask questions
Five other activities
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Show all eight activities
Follow tutorials on data analysis techniques
Enhance your practical skills in data analysis by following guided tutorials and applying techniques to real-world datasets.
Browse courses on Data Analysis
Show steps
  • Identify reputable online tutorials or courses
  • Select tutorials covering relevant data analysis techniques
  • Follow the tutorials step-by-step and practice implementing the techniques
Attend workshops on data analytics tools and techniques
Expand your knowledge and skills by attending workshops led by experts in the field of business analytics.
Browse courses on Data Analytics Tools
Show steps
  • Research and identify relevant workshops in your area
  • Register and attend the workshops
  • Take notes, ask questions, and actively participate in discussions
  • Apply the knowledge and techniques learned in your own projects or assignments
Solve practice problems and case studies
Strengthen your analytical abilities by solving practice problems and case studies, applying data analysis techniques to make informed decisions.
Browse courses on Data Analysis
Show steps
  • Find online platforms or textbooks with practice problems
  • Attempt to solve the problems independently
  • Review solutions and identify areas for improvement
Develop a data visualization dashboard
Enhance your understanding of data visualization and communication by creating an interactive dashboard that effectively presents insights from a dataset.
Browse courses on Data Visualization
Show steps
  • Select a relevant dataset and identify key variables
  • Choose appropriate visualization techniques to present the data
  • Use a data visualization tool to create the dashboard
  • Share the dashboard with peers or instructors for feedback
Develop a data-driven business proposal
Apply the concepts and techniques learned in the course to address a real-world business problem or opportunity.
Browse courses on Business Analytics
Show steps
  • Identify a business problem or opportunity
  • Collect and analyze relevant data
  • Develop data-driven insights and recommendations
  • Create a business proposal outlining your findings and recommendations
  • Present your proposal to instructors or peers for feedback

Career center

Learners who complete Business Analytics: Diversity of Practical Applications will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data **Scientists** use data to build predictive models. As a Data **Scientist**, you will learn how to use data to build models that can predict future events. This course will help you develop the skills you need to be successful in this field, including data analysis, machine learning, and artificial intelligence.
Business Analyst
Business **Analysts** use data to help businesses make better decisions. As a Business **Analyst**, you will learn how to collect, analyze, and interpret data to identify problems and opportunities. This course will help you develop the skills you need to be successful in this field, including data analysis, problem-solving, and communication skills.
Operations Research Analyst
Operations **Research Analysts** use data to improve business operations. As an Operations **Research Analyst**, you will learn how to use data to model and analyze business processes. This course will help you develop the skills you need to be successful in this field, including data analysis, optimization, and simulation.
Data Analyst
Data **Analysts** use data to solve business problems. As a Data **Analyst**, you will learn how to use data to make better decisions. This course will help you build a foundation in data analysis, which is essential for success in this field. It will also introduce you to different data analysis techniques that can be used to solve problems in a variety of business areas, including marketing, sales, PR, HR, and finance.
Financial Analyst
Financial **Analysts** use data to make investment recommendations. As a Financial **Analyst**, you will learn how to use data to analyze financial statements and make investment decisions. This course will help you develop the skills you need to be successful in this field, including data analysis, financial modeling, and investment principles.
Marketing Analyst
Marketing **Analysts** use data to measure the effectiveness of marketing campaigns. As a Marketing **Analyst**, you will learn how to use data to track key metrics and identify areas for improvement. This course will help you develop the skills you need to be successful in this field, including data analysis, marketing principles, and campaign management.
Risk Analyst
Risk **Analysts** use data to identify and mitigate risks. As a Risk **Analyst**, you will learn how to use data to analyze risks and develop risk mitigation plans. This course will help you develop the skills you need to be successful in this field, including data analysis, risk management, and compliance.
Pricing Analyst
Pricing **Analysts** use data to set prices for products and services. As a Pricing **Analyst**, you will learn how to use data to analyze demand and competition. This course will help you develop the skills you need to be successful in this field, including data analysis, pricing strategy, and market research.
Market Research Analyst
Market **Research Analysts** use data to understand customer needs. As a Market **Research Analyst**, you will learn how to use data to conduct surveys and focus groups, and analyze the results. This course will help you develop the skills you need to be successful in this field, including data analysis, market research, and customer behavior.
Sales Analyst
Sales **Analysts** use data to track and improve sales performance. As a Sales **Analyst**, you will learn how to use data to identify trends and opportunities, and develop sales strategies. This course will help you develop the skills you need to be successful in this field, including data analysis, sales forecasting, and customer relationship management.
Human Resources Analyst
Human **Resources Analysts** use data to improve human resources processes. As a Human **Resources Analyst**, you will learn how to use data to analyze employee data and develop HR strategies. This course will help you develop the skills you need to be successful in this field, including data analysis, human resources management, and organizational behavior.
Customer Success Manager
Customer **Success Managers** use data to improve customer satisfaction. As a Customer **Success Manager**, you will learn how to use data to track customer activity and identify areas for improvement. This course will help you develop the skills you need to be successful in this field, including data analysis, customer relationship management, and account management.
Product Manager
Product **Managers** use data to develop and manage products. As a Product **Manager**, you will learn how to use data to identify customer needs and develop product roadmaps. This course will help you develop the skills you need to be successful in this field, including data analysis, product development, and market research.
Consultant
Consultants use data to help organizations solve problems and improve performance. As a Consultant**, you will learn how to use data to analyze business processes and develop recommendations. This course will help you develop the skills you need to be successful in this field, including data analysis, problem-solving, and communication skills.
Data Engineer
Data **Engineers** use data to build and maintain data systems. As a Data **Engineer**, you will learn how to use data to design and implement data pipelines and data warehouses. This course may be useful for those who are interested in a career in data engineering. The course will provide a foundation in data management and data engineering principles, which are essential for success in this field.
Decision Scientist
Decision **Scientists** use data to make better decisions. As a Decision **Scientist**, you will learn how to use data to model and analyze complex decisions. This course may be useful for those who are interested in a career in decision science. The course will provide a foundation in decision theory and data analysis, which are essential for success in this field.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Business Analytics: Diversity of Practical Applications .
Comprehensive introduction to pattern recognition and machine learning, covering the basics of supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for both beginners and experienced practitioners.
Comprehensive guide to deep learning, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for both beginners and experienced practitioners.
Comprehensive introduction to reinforcement learning, covering the basics of Markov decision processes, value functions, and reinforcement learning algorithms. It valuable resource for both beginners and experienced practitioners.
Comprehensive introduction to data science, covering the basics of big data, machine learning, and deep learning. It great way to get started with data science.
Comprehensive introduction to time series analysis, covering the basics of time series models, time series forecasting, and time series analysis applications. It valuable resource for both beginners and experienced practitioners.
Practical guide to using data analytics to improve decision-making in business. It covers a wide range of topics, from data collection to data visualization.
Comprehensive introduction to Bayesian analysis, covering the basics of Bayesian inference, Bayesian modeling, and Bayesian computation. It valuable resource for both beginners and experienced practitioners.
Comprehensive introduction to causal inference, covering the basics of causal graphs, causal effects, and causal inference methods. It valuable resource for both beginners and experienced practitioners.
Comprehensive introduction to econometric analysis of cross section and panel data, covering the basics of linear regression, nonlinear regression, and panel data models. It valuable resource for both beginners and experienced practitioners.
Great introduction to data science for business professionals. It covers the basics of data mining and data-analytic thinking, and how they can be used to improve decision-making.

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