Business intelligence and data analytics: Generate insights

Go to Course: https://www.coursera.org/learn/business-intelligence-data-analytics

Introduction

### Course Review: Business Intelligence and Data Analytics: Generate Insights In an age where data drives decision-making and shapes strategic outcomes, the course **"Business Intelligence and Data Analytics: Generate Insights"** on Coursera emerges as an invaluable tool for professionals looking to harness the power of data. As organizations grapple with ‘megatrends’ that influence societal and industrial landscapes, the skills acquired in this course are critical for anyone aiming to remain competitive and insightful in their field. #### Overview The course brilliantly sets the foundation for understanding how to analyze and interpret data to generate actionable insights. Recognizing the influence of megatrends, participants are equipped with analytical tools and skills necessary for evaluating challenges and seizing opportunities that these trends present. The course underscores the importance of sustainability, enabling learners to not only analyze data but also consider the broader implications of their findings. #### Syllabus Breakdown 1. **Basics of Insight Generation** The course kicks off by exploring the fundamental concepts of data exploitation and insight generation. As we live in an era with a tsunami of data from billions of devices and social media, this module highlights how effective decision-making hinges on the ability to harness this data. It sets an excellent tone for the rest of the course, emphasizing real-world applications in both business and governance. 2. **Basic Statistics: Foundations of Quantitative Insights** delving into basic statistics, the second week emphasizes the importance of quantitative analysis. Statistics serve as the backbone of data analytics. This module breaks down complex concepts into digestible pieces, allowing participants to understand how to convert varied data types into informative insights—essential skills for any data-driven role. 3. **The Normal Distribution and Histograms** Understanding distributions is key in data analytics, and week three provides an introduction to histograms and the normal distribution. The empirical rule and measures of correlation and covariance are introduced, giving learners the tools to interpret the relationships between variables—a critical skill in assessing product quality and business performance. 4. **Data Visualization** The art of storytelling with data is cultivated in this week. Participants learn about various visualization techniques that bring data to life. This module emphasizes the necessity to present data in a clear and compelling manner, especially when communicating with stakeholders who may not have a technical background. This aspect of the course resonates with professionals who need to influence decision-makers within their organizations. 5. **Advanced Charts and Dashboards** Building upon the visualization techniques learned in the previous week, this module dives deeper into creating bar charts, bullet charts, and dashboards. This enables participants to curate narratives from raw data, distilling information into formats that are not only comprehensible but also visually appealing. 6. **Demand Forecasting** The final week focuses on predictive modeling techniques for demand forecasting, a skill that empowers professionals to generate insights that drive proactive decision-making. The practical application of these models assures participants of their value in real-world scenarios, enhancing their strategic skill set. #### Conclusion & Recommendation Overall, the **"Business Intelligence and Data Analytics: Generate Insights"** course is a robust offering loaded with both theoretical and practical components. It effectively equips participants with essential analytical skills and best practices in data-driven storytelling, which are critical in today’s data-centric environment. I highly recommend this course for anyone looking to deepen their understanding of data analytics, from beginners wanting to grasp basic concepts to seasoned professionals seeking to refine their analytical prowess. The course's structured approach fosters a comprehensive understanding and practical application of core principles, making it a worthwhile investment in any professional's career toolkit. Enroll in this course today to transform your data insight capabilities and lead your organization toward informed decision-making!

Syllabus

Basics of insight generation

Organisations and governments everywhere want to exploit data to predict behaviors and extract valuable real-world insights. Billions of devices and social media conversations are fueling the rate at which humanity is producing data. Therefore, we need more skills to understand data and make our systems, policies and governance models more efficient. This week we will highlight the potential of generating insights with the help of data in allowing individuals, businesses, and governments to make effective decisions.

In week 2, we’ll focus on basic statistics. It’s one of the most important components of Data Analytics and it’s crucial to have a clear understanding of all the related concepts to be successful in the data industry. Statistics provide us with a set of tools that offer ways to convert quantitative data and qualitative data into information that we can use to generate insights.

Businesses must constantly strive to offer “better” products and services than their competitors. One of the oldest and time-proven techniques by which we can visualise and think about quality in a methodological way is via normal distributions or bell curves. So in week 3, we’ll start by learning about histograms and the normal curve and then have a look at empirical rule which gives us a quick rough estimate about the spread of the given data. Finally, we’ll learn about the measures that quantify the interrelationships between two data variables. Correlation and covariance are two important measures that quantify the relationship between variables and we’ll study both.

Visualisation is a key technique which can provide answers hidden in data. In this week, you will explore various data visualisations available and how to use them for analysis. These techniques will empower you to create compelling stories and dashboards from your data that the non-analyst community can also understand easily. As a person working in the data industry, you don’t just need to deal with data and solve data-driven problems but the incumbent also needs to convince company executives and government officials of the right decisions to make. These executives/officials may not be well versed in data science, so the incumbent must but be able to present and visualise the data’s story in a way they will understand. And this module will help you achieve that.

This week we learn how to create bar and bullet charts, and dashboards. Data visualization helps to tell stories by curating data into a form easier to understand. A good visualisation tells a story, by removing the noise from data and highlighting the useful information.

This week we’ll look at how, by using predictive modelling, we can generate actionable insights that when implemented will provide businesses with a predictable future outcome. Predictive modeling is a group of methods and algorithms that you can employ to forecast an outcome. Utilising basic predictive modelling techniques, we will also explore consumer demand forecasting.