Practical Data Science for Data Analysts

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.

Data science is about using statistics to draw insights from data to drive action and improve business performance. This course will guide you through the world of data science and machine learning, using applied examples to demonstrate real-world applications. Whether you’re an aspiring data scientist or a c-level exec, this course will bring you up to speed on everything data science. You’ll be introduced to machine learning, classification, exploratory data analysis, feature selection, and feature engineering—what they mean and how they are relevant to your business. We’ll start by defining the skills, tools, and roles behind data science that work together to create insights. We’ll then walk through regression and classification—the most common predictive and statistical techniques. Finally, you will understand why having a basic understanding of data science outputs is essential to all business stakeholders and how we can use those outputs to make business decisions. Whether you are a business leader or an aspiring analyst exploring data science, this Data Science & Machine Learning Fundamentals 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.

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.

Python is a core skill for anyone working with data—and this course shows you exactly how to use it in practice. Through real-world examples, you’ll build an end-to-end data analysis workflow using four of Python’s most powerful libraries: NumPy, Pandas, Matplotlib, and Seaborn. You’ll start by loading and cleaning messy datasets, then move into common statistical analysis and transformation techniques. Finally, you’ll bring your insights to life with clear, compelling visualizations. Whether you’re just getting started in data or want to reinforce your Python fundamentals, this course will give you the tools and confidence to turn raw data into impactful results.

Python is one of the most popular programming languages for data analysis—and it’s easier to learn than you might think. Whether you’re coming from Excel, BI tools, finance, or another programming background, this course will help you build a strong foundation in Python for working with data. You’ll start by setting up your environment with Anaconda and Jupyter Notebooks, then learn the core building blocks of Python: variables, data types, and data structures. From there, you’ll explore how to manipulate and analyze data using operators and functions, and write more advanced logic with conditionals and loops. By the end of the course, you’ll be confident writing basic Python code to structure and prepare data for analysis. You will also gain practical experience working with real-world datasets and common data analysis workflows. This includes cleaning, transforming, and organizing data so it can be used for meaningful insights. You’ll develop the ability to think like a data analyst, breaking problems into logical steps and applying Python to solve them efficiently.