Linear regression analysis is critical for understanding and defining the strength of the relationship between variables. This analysis can be used to make predictions for a variable given the value of another known variable.

This course provides an overview of linear regression. You will learn how linear regression works, how to build effective linear regression models and how to use and interpret the information these models give us. In addition to the theory, we will perform linear regression on real data using both Excel and Python. The practical cases you will work through will be similar to those you might encounter in a business setting. Upon completing this course, you will be able to: • Define linear regression and its applications • Perform simple “pen and paper” regression calculations in Excel • Apply Excel’s RegressIt plugin to solve advanced regression calculations • Construct linear regression models in Python using both statsmodels and sklearn modules • Explain the implicit assumptions behind linear regression • Interpret regression outputs such as coefficients and p-values • Recommend various regression techniques when appropriate Regression is the critical tool used for making inferences or predictions based on the relationships between variables. Whether you’re working as a business leader or data analyst, the theory and practical toolsets taught in this course will serve you throughout your career. No background in coding with Python is required for this course. Common career paths for students who take the BIDA™ program are Business Intelligence, Asset Management, Data Analyst, Quantitative Analyst, and other finance careers.

Classification problems are one of the most common scenarios we face in data science. This course will help you understand and apply common algorithms to make predictions and drive decision-making in business. Whether you’re an aspiring data scientist, studying analytics, or have a focus on business intelligence, this course will give you a comprehensive overview of classification problems, solutions, and interpretations.

From Logistic Regression to KNN and SVM models, you’ll learn how to implement techniques in Excel and Python and how to create loops to run models in parallel.  Since model evaluation is so important, we’ll dedicate a whole chapter to interpreting model outputs with evaluation metrics and the confusion matrix. With this, you’ll learn about false negatives, and false positives, and consider the impacts these may have on specific business scenarios. Finally, we’ll give you a brief insight into more advanced classification techniques such as feature importance, SHAP values, and PDP plots. Upon completing this course, you will be able to: • Distinguish between classic classification techniques including their implicit assumptions and practical use-cases • Perform simple logistic regression calculations in Excel & RegressIt • Create basic classification models in Python using statsmodels and sklearn modules • Evaluate and interpret the performance of classification model outputs and parameters Whether you’re an aspiring data scientist, studying analytics, or have a focus on business intelligence, this classification course will serve as your comprehensive introduction to this fascinating subject. You’ll learn all the key terminology to allow you to talk data science with your teams, benign implementing analysis, and understand how data science can help your business.

Machine learning models rely on good data to produce meaningful insights. For that reason, data prep is one of the most critical skills for machine learning.

In this course, you’ll learn how to import and clean data before populating missing values using imputation. You’ll learn how to visualize histograms, scatter charts, and box plots to identify trends of interest before using the analysis to select the most important features. Feature engineering techniques such as one hot encoding, binning and scaling will help us transform the structure of our data to produce higher quality machine learning insights. This data prep course in Python includes more interactive exercises and challenges than previous BIDA courses have. You will also have the opportunity to test your skills on a comprehensive guided Python case study before completing the final exam. Upon completing this course, you will be able to: • Import and clean your data in Python • Apply imputation to estimate missing values in the dataset • Conduct exploratory data analysis (EDA) to find initial patterns to guide our analysis • Select features to focus on the most important variables • Apply feature engineering to make datasets machine learning-friendly • Select appropriate feature engineering techniques based on the model type Whether you are a business leader or an aspiring analyst exploring data science, this Data Prep for Machine Learning in Python course will serve as your comprehensive introduction to this fascinating subject. You’ll learn all the key terminology to allow you to talk data science with your teams, begin implementing analysis, and understand how data science can help your business.