Course
Introduction to Portfolio Analysis in Python
- AdvancedSkill Level
- 4.9+
- 447
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
Data, AI, and Cloud Courses
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Course
Dealing With Missing Data in R
- BasicSkill Level
- 4.8+
- 447
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Data Preparation
Course
Hierarchical and Mixed Effects Models in R
- AdvancedSkill Level
- 4.7+
- 444
In this course you will learn to fit hierarchical models with random effects.
Probability & Statistics
Course
Conquering Data Bias
- BasicSkill Level
- 4.8+
- 443
Unlock your datas potential by learning to detect and mitigate bias for precise analysis and reliable models.
Data Management
Course
Financial Modeling in Google Sheets
- IntermediateSkill Level
- 4.8+
- 442
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.
Applied Finance
Course
Multi-Modal Systems with the OpenAI API
- IntermediateSkill Level
- 4.9+
- 439
Create multi-modal systems using OpenAIs text and audio models, including an end-to-end customer support chatbot!
Artificial Intelligence
Course
Intermediate Importing Data in R
- IntermediateSkill Level
- 4.8+
- 438
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
Data Preparation
Course
Corporate Finance Fundamentals
- BasicSkill Level
- 4.8+
- 435
Learn key financial concepts such as capital investment, WACC, and shareholder value.
Applied Finance
Course
Time Series Analysis in SQL Server
- IntermediateSkill Level
- 4.8+
- 434
Explore ways to work with date and time data in SQL Server for time series analysis
Data Manipulation
Course
Experimental Design in R
- IntermediateSkill Level
- 4.7+
- 433
In this course youll learn about basic experimental design, a crucial part of any data analysis.
Probability & Statistics
Course
Communicating with Data in the Tidyverse
- BasicSkill Level
- 4.8+
- 432
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
Data Visualization
Course
Cluster Analysis in R
- IntermediateSkill Level
- 4.9+
- 430
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Machine Learning
Course
Reinforcement Learning from Human Feedback (RLHF)
- AdvancedSkill Level
- 4.8+
- 430
Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
Artificial Intelligence
Course
AI-Assisted Travel Planning
- BasicSkill Level
- 4.8+
- 428
Master travel planning with WanderBot: craft prompts, build confidence, and streamline your next adventure.
Artificial Intelligence
Course
Visualizing Time Series Data in R
- IntermediateSkill Level
- 4.8+
- 423
Learn how to visualize time series in R, then practice with a stock-picking case study.
Data Visualization
Course
Generalized Linear Models in Python
- AdvancedSkill Level
- 4.8+
- 423
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Probability & Statistics
Course
Introduction to Financial Statements in Power BI
- IntermediateSkill Level
- 4.8+
- 422
Discover how to use the income statement and balance sheet in Power BI
Applied Finance
Course
Recommending Skincare Products
- BasicSkill Level
- 4.8+
- 422
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
Artificial Intelligence
Course
Gen AI: Beyond the Chatbot
- BasicSkill Level
- 4.8+
- 412
This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization.
Cloud
Course
Dealing with Missing Data in Python
- IntermediateSkill Level
- 4.8+
- 412
Learn how to identify, analyze, remove and impute missing data in Python.
Data Manipulation
Course
Case Study: Analyzing Job Market Data in Tableau
- BasicSkill Level
- 4.8+
- 412
In this case study, you’ll use visualization techniques to find out what skills are most in-demand for data scientists, data analysts, and data engineers.
Data Visualization
Course
Visualizing Geospatial Data in Python
- IntermediateSkill Level
- 4.8+
- 408
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
Data Visualization
Course
Foundations of Probability in Python
- IntermediateSkill Level
- 4.8+
- 407
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Probability & Statistics
Course
Transform and Analyze Data with Microsoft Fabric
- BasicSkill Level
- 4.8+
- 402
Learn how to transform and analyze data within your Microsoft Fabric account
Other
Course
Case Study: Mortgage Trading Analysis in Power BI
- IntermediateSkill Level
- 4.9+
- 396
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
Gemini in Google Docs
- BasicSkill Level
- 4.9+
- 395
Write and edit faster with Gemini in Google Docs. Get AI-powered drafting, rewriting, and content suggestions to create clear, polished documents effortlessly.
Artificial Intelligence
Course
Intermediate Data Visualization with Seaborn
- IntermediateSkill Level
- 4.9+
- 395
Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.
Data Visualization
Course
Introduction to Data Quality with Great Expectations
- IntermediateSkill Level
- 4.8+
- 393
Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.
Data Engineering
Course
Working with Dates and Times in R
- IntermediateSkill Level
- 4.8+
- 392
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Software Development
Course
Anomaly Detection in Python
- IntermediateSkill Level
- 4.8+
- 390
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Probability & Statistics
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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