Course
Introduction to Python for Finance
- BasicSkill Level
- 4.8+
- 3.5K
Build Python skills to elevate your finance career. Learn how to work with lists, arrays and data visualizations to master financial analyses.
Applied Finance
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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.38 Courses
Course
Financial Modeling in Excel
- IntermediateSkill Level
- 4.9+
- 2.1K
Learn about Excel financial modeling, including cash flow, scenario analysis, time value, and capital budgeting.
Applied Finance
Course
Financial Analysis in Power BI
- IntermediateSkill Level
- 4.9+
- 1.6K
Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.
Applied Finance
Course
Intermediate Python for Finance
- IntermediateSkill Level
- 4.8+
- 1.5K
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
Applied Finance
Course
Introduction to R for Finance
- BasicSkill Level
- 4.8+
- 814
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Applied Finance
Course
Introduction to Portfolio Risk Management in Python
- IntermediateSkill Level
- 4.8+
- 685
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Applied Finance
Course
Introduction to Financial Concepts in Python
- BasicSkill Level
- 4.9+
- 675
Using Python and NumPy, learn the most fundamental financial concepts.
Applied Finance
Course
Credit Risk Modeling in Python
- IntermediateSkill Level
- 4.8+
- 673
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Applied Finance
Course
Financial Trading in Python
- IntermediateSkill Level
- 4.8+
- 564
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Applied Finance
Course
Quantitative Risk Management in Python
- AdvancedSkill Level
- 4.8+
- 541
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Applied Finance
Course
Case Study: Net Revenue Management in Excel
- IntermediateSkill Level
- 4.8+
- 523
You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.
Applied Finance
Course
Importing and Managing Financial Data in Python
- IntermediateSkill Level
- 4.8+
- 504
In this course, youll learn how to import and manage financial data in Python using various tools and sources.
Applied Finance
Course
Math for Finance Professionals
- BasicSkill Level
- 4.8+
- 498
Learn essential finance math skills with practical Excel exercises and real-world examples.
Applied Finance
Course
Financial Analytics in Google Sheets
- BasicSkill Level
- 4.7+
- 480
Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
Applied Finance
Course
Introduction to Portfolio Analysis in Python
- AdvancedSkill Level
- 4.9+
- 459
Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.
Applied Finance
Course
Corporate Finance Fundamentals
- BasicSkill Level
- 4.8+
- 435
Learn key financial concepts such as capital investment, WACC, and shareholder value.
Applied Finance
Course
Introduction to Financial Statements in Power BI
- IntermediateSkill Level
- 4.8+
- 418
Discover how to use the income statement and balance sheet in Power BI
Applied Finance
Course
Financial Modeling in Google Sheets
- IntermediateSkill Level
- 4.8+
- 405
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.
Applied Finance
Course
Case Study: Mortgage Trading Analysis in Power BI
- IntermediateSkill Level
- 4.9+
- 392
In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.
Applied Finance
Course
Intermediate R for Finance
- BasicSkill Level
- 4.8+
- 369
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Applied Finance
Course
GARCH Models in Python
- IntermediateSkill Level
- 4.8+
- 329
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Applied Finance
Course
Introduction to Portfolio Analysis in R
- BasicSkill Level
- 4.8+
- 304
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Applied Finance
Course
Analyzing Financial Statements in Python
- IntermediateSkill Level
- 4.7+
- 295
Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
Applied Finance
Course
Introduction to Business Valuation
- BasicSkill Level
- 4.9+
- 240
Learn business valuation with real-world applications and case studies using discounted cash flows (DCF).
Applied Finance
Course
Case Study: Financial Analysis in KNIME
- IntermediateSkill Level
- 4.8+
- 215
Apply financial analysis in KNIME with real-world data, enhancing data preparation and workflow skills.
Applied Finance
Course
Quantitative Risk Management in R
- BasicSkill Level
- 4.9+
- 184
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Applied Finance
Course
Credit Risk Modeling in R
- IntermediateSkill Level
- 4.8+
- 165
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Applied Finance
Course
GARCH Models in R
- AdvancedSkill Level
- 4.8+
- 159
Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
Applied Finance
Course
Financial Forecasting in Python
- IntermediateSkill Level
- 4.8+
- 150
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Applied Finance
Course
Importing and Managing Financial Data in R
- IntermediateSkill Level
- 4.7+
- 136
Learn how to access financial data from local files as well as from internet sources.
Applied Finance
FAQs
What is data science?
Data science is an area of expertise focused on gaining information from data. Using programming skills, scientific methods, algorithms, and more, data scientists analyze data to form actionable insights.
How can I learn data science?
You’ll need to learn a programming language such as Python or R and master the principles of math and statistics. Knowledge of data analysis methods and data science tools is also essential. There are many ways to learn data science. As well as formal means of education, such as a degree or university study, there are plenty of other resources to help you learn at your own pace. As well as online courses and tutorials, there are books, videos, and more.
What skills are required for data science?
As well as knowledge of mathematics and statistics, data scientists need programming skills in languages such as Python, R, and SQL. Additionally, data science requires the ability to work with large data sets, knowledge of data visualization, data wrangling, and database management. Skills in machine learning and deep learning can also be useful.
What can I use data science for?
In a professional capacity, almost every industry can use data science to some degree. Healthcare organizations use data science to detect and cure diseases, while finance companies use it to detect and prevent fraud. All kinds of industries use data science for marketing, such as building recommendation systems and analyzing customer churn.
Is data science a good career?
Yes, data science is among the fastest-growing sectors in the US and worldwide. It’s also one of the best-paid careers out there. According to data from Payscale, experience data scientists earn an average of $97,609 and have a satisfaction rating of four stars out of five in the US.
Is it difficult to become a data scientist?
There are a few things to consider here. First, data science degrees can be competitive to get onto, often requiring consistently high grades. Similarly, many of the skills required for data science require a lot of study and patience. It can take several months to master all of the necessary basics, as well as a lot of practical experience to secure an entry-level position.
Does data science require coding?
Yes, you’ll need some coding experience in languages such as Python, R, SQL, Java, and C/C++. However, due to its relatively simple syntax, Python programming language is often the preferred choice among newcomers.
How long does it take to become a data scientist?
For a person with no prior coding experience and/or mathematical background, it can typically take 7 to 12 months of intensive studies to be at the level of an entry-level data scientist. However, it is important to remember that learning only the theoretical basis of data science may not make you a real data scientist.
What topics can I study within data science?
Once you’ve mastered the foundations of data science, you can then specialize in a variety of areas, including machine learning, artificial intelligence, big data analysis, business analytics and intelligence, data mining, and more.
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