Introduction to Business Analytics
Business Analytics offers a unique perspective on how world-class organizations use data-driven decision making as a tool for success. Throughout the course, you will develop the practical skills you need to manage a successful analytics project. The step-by-step modules take you all the way from stakeholder and process mapping, through end-to-end processes and benchmarking, to hands-on analytical techniques, including historical analysis, variance analysis, trend analysis, value-based analysis, correlation, time series, regression, as well as machine and deep learning analysis. Once you finish the course, you will have a solid foundation and a good idea how these techniques can be applied in practice. This will allow you to thrive in any corporate environment.
4 hours
7 exercises
6 quizzes
22 downloadables Sign up to
preview the program
for FREE!
Create a free account and start learning data science today.
create free account
Our graduates work at exciting places:
Section 1
Setting the scene
The first section of the course teaches you the fundamentals of how companies develop short- and long-term expectations, why that is necessary, and how businesses set up a system to monitor their performance.
What does the course cover? Managing expectations of different stakeholder groups Preparing an annual business plan and a long-range plan Analytics vs analysis Section 2
Understanding your business
Here, you will gain an in-depth understanding of the mechanics of the entire process through practical examples. You will learn more about the various groups of stakeholders, their expectations, and how these are translated into the long-range plan and the annual business plan.
Section intro Stakeholder mapping Defining business intelligence End-to-end process mapping (part 1) End-to-end process mapping (part 2) Section 3
An in-depth view of end-to-end processes in a corporation
An in-depth view of end-to-end processes in a corporation - Hire-to-Retire (H2R), Record-to-Report (R2R), Order-to-Cash (O2C), Source-to-Pay (S2P)
Hire-to-retire (H2R) Source-to-Pay (S2P) Record-to-report (R2R) Order-to-Cash (O2C) Global process owners - Conclusion Section 4
Target setting
This section is dedicated to target-setting. You will learn how to identify key value drivers, what are metrics, and what distinguished metrics and KPIs. This part of the course also explains internal and external benchmarking, as well as the importance of the Master data governance function.
Identifying a firm's key value drivers Metrics vs KPIs Define your metrics The benchmarking exercise The importance of data The master data governance function Section 5
Maturity stages in analytics
An introduction of the different stages of analysis and a high-level overview of the types of analytics techniques that can be implemented in each of these stages within a company’s day to day activities. This section provides you with an excellent holistic framework about the descriptive, predictive, and prescriptive types of analytics implemented by businesses in different industries around the world. Once you complete the section, you will have a good understanding of what can be done in different situations and how these analytics can create value for your organization.
Section intro Descriptive analytics Diagnostic analytics Predictive analytics Prescriptive analytics Section 6
Analytics techniques in practice
A closer look into the topic where you will improve your understanding of trend analysis, comparative analysis, and value-based analysis through relevant practical examples. Further on, the section covers time-series correlation and regression analysis, and you will get familiar with some of the more advanced techniques that are rapidly increasing in popularity, such as business decision trees, machine learning, and natural language processing.
Section intro Trend analysis Comparative analysis Value-based analysis Correlation analysis Time series analysis Regression analysis Machine learning analysis Types of machine learning Analytics in practice - Conclusion Section 7
Analytics life cycle
This last section focuses on project management and reveals some critical success factors which you can bear in mind when working on your own analytical project. We will go through a Step-By-Step plan showing how to execute a project by reviewing each of the six phases of the analytics project's lifecycle: the hypothesis development phase, situation analysis, current state analysis, the blueprint and design phase, the build and test phase, and the deploy and operationalize phase.
Section intro Hypothesis development Situational analysis Building a RACI matrix What makes for a successful project? Current state analysis Blueprint and design Build and test Build and test: Step-by-step Deploy and operationalise Advanced Specialization
This course is part of Module 4 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Module 4 is focused on developing a specialized, industry-relevant skill set, and students are encouraged to complete Modules 1, 2, and 3 before they start this part of the training. Here, you will learn how to perform Credit Risk Modeling for banks, Customer Analytics for retail or other commercial companies, and Time Series Analysis for finance and stock data.
See All Modules Why Choose the 365 Data Science Program?
Practice
Real-life project and data. Solve them on your own computer as you would in the office.
Q&A Hub
Our expert instructors are happy to help. Post a question and get a personal answer by one of our instructors.
Certificates
Earn a verifiable certificate after each completed course. Celebrate your successes and share your progress with your professional network!
Trust the other 450,000 students
The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense аof how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.
Ready to start?
Sign up today for FREE!
