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.

Python is the most popular programming language used for data science and is a must-know to start or advance your career in data. In this course, you will learn the most fundamental skills to write and execute Python code. We will have an overview of the basic Python concepts, which will enable you to get started with a data science project! You will see how to load, clean, analyze, and transform data with two popular Python packages: Numpy and Pandas. Then, we will demonstrate how to effectively communicate the key insights from your analysis by visualizing your data using the Matplotlib and Seaborn packages. Finally, you’ll combine these skills and put your new knowledge into practice by analyzing financial data through a case study. Upon completing this course, you will be able to: • Write and execute Python code to create variables, generate outputs, apply various operators, and manipulate different types of data • Capture and transform data using Numpy and Pandas packages • Explore data through different statistical methods to gain a deeper understanding • Visualize data to share insights using the Matplotlib and Seaborn packages • Combine and apply the skills above to analyze financial data This Python Fundamentals course is perfect for anyone who would like to build up their programming skills and use Python for data science to analyze data. This course is designed to equip anyone who desires to begin or further their career in data analysis, quantitative analysis, business intelligence, or other areas of business and finance.