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Practical Data Science for Data Analysts. Advanced Finance and Data Science Specialization
Instructor: CFI (Corporate Finance Institute)
Included with
(8 reviews)
Recommended experience
Advanced level
Ideal for learners with basic math/stats and Excel knowledge; no programming experience needed. Finance background helpful but not essential.
(8 reviews)
Recommended experience
Advanced level
Ideal for learners with basic math/stats and Excel knowledge; no programming experience needed. Finance background helpful but not essential.
Build machine learning models in Python for financial trend analysis and prediction
Data cleaning and feature engineering to enhance model accuracy
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November 2024
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This specialization covers essential skills in Python programming, data preparation, and foundational machine learning techniques such as linear regression and classification, providing a well-rounded introduction to data science in finance. Learners will gain hands-on experience in data manipulation and predictive modeling, preparing them for data-driven roles in finance and analytics. Designed in partnership with industry-leading experts, the courses ensure relevant, practical skills aligned with the latest industry demands.
Applied Learning Project
The included projects allow learners to apply Python programming and machine learning techniques to real-world financial datasets, guiding them through the process of data cleaning, feature engineering, and predictive modeling. By tackling authentic finance problems, learners will build practical skills in data analysis and model development, preparing them to make data-driven decisions in professional settings.
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.
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.
CFI is the leading global provider of training and productivity tools for finance and banking professionals. CFI delivers the skills, certifications, CPE credits, and resources to help anyone—from beginner to seasoned pro—drive their career in finance & banking.
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Typically 2-3 months if you commit 2 hours each week.
Ideal for learners with basic math/stats and Excel knowledge; no programming experience needed. Finance background helpful but not essential.
No, it's not strictly necessary to take the courses in order, but it is beneficial. Starting with Python fundamentals and data preparation will make the more advanced topics, like linear regression and classification, easier to understand and apply.
Upon completing the specialization, you'll be able to confidently use Python for data analysis, clean and prepare financial datasets, and build machine learning models to make predictive insights in finance. You’ll have the skills to analyze financial trends, create data-driven forecasts, and apply feature engineering and modeling techniques to solve real-world financial challenges.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
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