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Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard, and Stemplicity Q&A Support

Are you looking to land a top-paying job in Data Science?

Or are you a seasoned AI practitioner who want to take your career to the next level?

Read more

Are you looking to land a top-paying job in Data Science?

Or are you a seasoned AI practitioner who want to take your career to the next level?

Or are you an aspiring entrepreneur who wants to maximize business revenue with Data Science and Artificial Intelligence?

If the answer is yes to any of these questions, then this course is for you.

Data Science is one of the hottest tech fields to be in right now. The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, transportation and technology.

In business, Data Science is applied to optimize business processes, maximize revenue and reduce cost. The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.

In this course, we will assume that you are an experienced data scientist who have been recently as a data science consultant to several clients. You have been tasked to apply data science techniques to the following 6 departments: (1) Human Resources, (2) Marketing, (3) Sales, (4) Operations, (5) Public Relations, (6) Production/Maintenance. Your will be provided with datasets from all these departments and you will be asked to achieve the following tasks:

  1. Task #1 @Human Resources Department: Develop an AI model to Reduce hiring and training costs of employees by predicting which employees might leave the company.

  2. Task #2 @Marketing Department: Optimize marketing strategy by performing customer segmentation

  3. Task #3 @Sales Department: Develop time series forecasting models to predict future product prices.

  4. Task #4 @Operations Department: Develop Deep Learning model to automate and optimize the disease detection processes at a hospital.

  5. Task #5 @Public Relations Department: Develop Natural Language Processing Models to analyze customer reviews on social media and identify customers sentiment.

  6. Task #6 @Production/Maintenance Departments: Develop defect detection, classification and localization models.

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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches advanced methods in Data Science with a focus on business applications
Taught by instructors who are recognized experts in the field
Uses real-world datasets for practical experience
Covers a wide range of topics relevant to data science in business
Designed for experienced data scientists looking to enhance their skills
May require additional background knowledge in data science

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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 Data Science for Business | 6 Real-world Case Studies with these activities:
Seek guidance from experienced data science professionals
Connect with mentors who can provide valuable insights and support throughout your learning journey.
Show steps
  • Attend industry events or meetups to network with data scientists
  • Reach out to professionals on LinkedIn or other platforms
  • Identify potential mentors based on their experience and expertise
Review introductory data science concepts
Review basic data science concepts to strengthen your foundation before starting the course.
Browse courses on Data Science Fundamentals
Show steps
  • Watch a series of video tutorials on data science fundamentals
  • Read introductory articles or blog posts on data science concepts
Participate in online discussion forums
Engage with peers and clarify concepts by participating in online discussions related to the course topics.
Show steps
  • Join online discussion forums dedicated to data science
  • Ask questions, share insights, and respond to others' discussions
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice data manipulation and analysis
Reinforce your understanding of data manipulation and analysis techniques through hands-on exercises.
Browse courses on Data Manipulation
Show steps
  • Solve coding challenges or practice problems involving data manipulation and analysis
  • Work through practice datasets to apply data analysis techniques
Develop a machine learning model
Apply your knowledge of machine learning by building a model to solve a real-world problem.
Browse courses on Machine Learning
Show steps
  • Identify a suitable dataset and problem statement
  • Choose and implement an appropriate machine learning algorithm
  • Evaluate and refine your model
Contribute to open-source data science projects
Gain practical experience in data science and contribute to the community by participating in open-source projects.
Show steps
  • Identify open-source data science projects that align with your interests
  • Review the documentation and contribute code or other resources
  • Collaborate with other contributors and learn from their expertise

Career center

Learners who complete Data Science for Business | 6 Real-world Case Studies will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists develop algorithms and statistical models that businesses use to optimize operations, identify trends, and make predictions. Those who take this course may be well suited to data science as it provides hands-on experience using real-world datasets in a variety of business contexts.
Machine Learning Engineer
Machine Learning Engineers apply machine learning techniques to solve business problems. By providing practical experience with real-world datasets, this course can help students build a solid foundation and enhance their skills in machine learning. The course also covers key aspects of data science applications in business, making it particularly relevant for those interested in business applications of machine learning.
Business Intelligence Analyst
Business Intelligence Analysts use data analysis to provide insights and recommendations to businesses. This course can help students develop the skills needed to gather, analyze, and interpret data, and communicate findings to stakeholders.
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses understand their customers, optimize operations, and make informed decisions. The course's emphasis on practical, hands-on experience with real-world datasets can provide valuable skills for data analysts.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to help businesses optimize their operations. The course's coverage of topics such as optimization and forecasting can provide valuable skills for operations research analysts.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The course's focus on statistical modeling and predictive analytics can provide valuable skills for quantitative analysts.
Data Visualization Analyst
Data Visualization Analysts create visual representations of data to help businesses communicate complex information effectively. The course's emphasis on practical experience with real-world datasets can provide valuable skills for data visualization analysts.
Risk Analyst
Risk Analysts assess and manage risks for businesses. The course's coverage of topics such as data analysis and forecasting can provide valuable skills for risk analysts.
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. The course's focus on statistical modeling and predictive analytics can provide valuable skills for financial analysts.
Market Research Analyst
Market Research Analysts conduct research to understand customer needs and preferences. The course's emphasis on practical experience with real-world datasets can provide valuable skills for market research analysts.
Product Manager
Product Managers oversee the development and launch of new products. The course's coverage of topics such as customer segmentation and market analysis can provide valuable skills for product managers.
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. The course's emphasis on customer segmentation and predictive analytics can provide valuable skills for marketing managers.
Sales Manager
Sales Managers lead and motivate sales teams to achieve sales targets. The course's coverage of topics such as time series forecasting can provide valuable skills for sales managers.
Business Development Manager
Business Development Managers develop and execute strategies to grow a business's customer base. The course's focus on customer segmentation and predictive analytics can provide valuable skills for business development managers.
Operations Manager
Operations Managers oversee the day-to-day operations of a business. The course's coverage of topics such as optimization and process improvement can provide valuable skills for operations managers.

Reading list

We've selected nine 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 Data Science for Business | 6 Real-world Case Studies.
Provides a comprehensive overview of data science for business, covering topics such as data collection, data analysis, and data visualization. It valuable resource for anyone who wants to learn more about how data science can be used to improve business outcomes.
Provides a comprehensive overview of deep learning. It covers topics such as deep learning concepts, deep learning algorithms, and deep learning applications. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of predictive analytics for business. It covers topics such as predictive analytics techniques, predictive analytics applications, and predictive analytics best practices. It valuable resource for anyone who wants to learn more about how predictive analytics can be used to improve business outcomes.
Provides a comprehensive overview of machine learning for beginners. It covers topics such as machine learning concepts, machine learning algorithms, and machine learning applications. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of natural language processing for business. It covers topics such as natural language processing concepts, natural language processing algorithms, and natural language processing applications. It valuable resource for anyone who wants to learn more about how natural language processing can be used to improve business outcomes.
Provides a practical introduction to machine learning for business users. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about how machine learning can be used to solve business problems.
Provides a comprehensive overview of data mining for business intelligence. It covers topics such as data mining techniques, data mining applications, and data mining best practices. It valuable resource for anyone who wants to learn more about how data mining can be used to improve business outcomes.
Provides a comprehensive overview of data visualization for business. It covers topics such as data visualization principles, data visualization tools, and data visualization best practices. It valuable resource for anyone who wants to learn more about how data visualization can be used to improve business outcomes.
Provides a comprehensive overview of artificial intelligence for business. It covers topics such as natural language processing, computer vision, and robotics. It valuable resource for anyone who wants to learn more about how artificial intelligence can be used to improve business outcomes.

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