This course is aimed at familiarizing Data and Business professionals with the basic concepts of statistical analysis and methods used for data-driven decision-making. After completing this course, you will be able to apply descriptive and inferential analysis methods, use data visualization methods to communicate data, apply concepts of probability in real-life scenarios, and apply regression techniques to predict trends.
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The course takes a hands-on approach to statistical analysis using Microsoft Excel and uses examples to illustrate the concepts to help you gain the foundational knowledge of statistical techniques needed to solve business intelligence (BI) problems.
A hands-on project will provide you an opportunity to apply the concepts to a real-life scenario involving data-driven decision-making and an understanding of basic statistical thinking and reasoning.
This course is suitable for professionals or students who aspire to embark on a career in the BI or Data Analytics fields by equipping them with the crucial skills and knowledge in statistical analysis. It is expected that learners be familiar with Excel/spreadsheet basics and high school mathematics prior to starting this course.
This course is part of the IBM Business Intelligence (BI) Analyst Professional Certificate.
What you'll learn
- Describe the fundamental concepts of statistics and apply them to business and data analytics settings.
- Apply data collection, analysis, and interpretation techniques to derive actionable insights for making informed business decisions.
- Apply descriptive and inferential analysis methods to derive insights and actionable recommendations from data.
- Apply hypothesis testing, regression analysis, and forecasting to support business decision-making processes.
Syllabus
Introduction and Descriptive Statistics
This module introduces descriptive statistics and its role in summarizing and describing data. You will learn about the significance of statistics in making informed decisions and its relevance to professions like Data Analyst, BI Analyst, and Data Scientist. The module covers key measures of central tendency, including mean, median, and mode, and their applications in different scenarios. Additionally, you will evaluate the importance of measures of dispersion, such as variance and standard deviation, in assessing data variability.
Data Visualization
This module focuses on data visualization and its role in effectively communicating information. You will learn to identify different types of visualizations suitable for various types of data and information. The module covers the calculation and interpretation of measures and graphs used in data visualization. You will also apply principles and guidelines to select appropriate visualizations based on data characteristics and communication goals. Additionally, you will learn data visualization techniques to present and communicate information clearly and intuitively. The module emphasizes the analysis and evaluation of visualizations to derive insights and effectively convey the intended message.
Introduction to Probability Distributions
In this module, students will apply fundamental concepts of probability to real-world scenarios. They will differentiate between various probability distributions, including the normal distribution and the T-distribution, and calculate probabilities to make informed decisions. The significance of hypothesis testing, alpha levels, and p-values in statistical analysis will be explored. Students will apply probability distribution concepts and techniques to solve practical problems and analyze real-world data.
Regression Analysis and Forecasting
This module focuses on regression analysis and its significance in business analytics. You will develop a comprehensive understanding of regression analysis and its applications in examining variable relationships and making predictions. The module covers building regression models and evaluating their assumptions, diagnosing problems, and identifying potential remedies. Additionally, you will develop forecasting skills by applying regression techniques to predict future trends and outcomes, supporting informed decision-making.
Analysing Sales Performance and Forecasting
The project focuses on analysing sales performance using data visualization and making simple forecasts for future sales based on historical data.
