Course
Fraud Detection in Python
- IntermediateSkill Level
- 4.7+
- 506
Learn how to detect fraud using Python.
Machine Learning
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670 Courses
Course
ARIMA Models in Python
- AdvancedSkill Level
- 4.7+
- 505
Learn about ARIMA models in Python and become an expert in time series analysis.
Machine Learning
Course
Visualization Best Practices in R
- BasicSkill Level
- 4.6+
- 505
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Data Visualization
Course
Generalized Linear Models in R
- IntermediateSkill Level
- 4.6+
- 500
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Probability & Statistics
Course
RNA-Seq with Bioconductor in R
- IntermediateSkill Level
- 4.4+
- 493
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Foundations of Inference in R
- IntermediateSkill Level
- 4.4+
- 493
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Probability & Statistics
Course
Unsupervised Learning in R
- IntermediateSkill Level
- 4.8+
- 491
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Machine Learning
Course
Introduction to AWS Boto in Python
- IntermediateSkill Level
- 4.6+
- 491
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
Cloud
Course
Data Manipulation with data.table in R
- BasicSkill Level
- 4.3+
- 484
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Data Manipulation
Course
Building Dashboards with Dash and Plotly
- IntermediateSkill Level
- 4.7+
- 481
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
Course
Introduction to Spark SQL in Python
- AdvancedSkill Level
- 4.4+
- 475
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Data Manipulation
Course
Time Series Analysis in SQL Server
- IntermediateSkill Level
- 4.5+
- 470
Explore ways to work with date and time data in SQL Server for time series analysis
Data Manipulation
Course
Streaming Concepts
- BasicSkill Level
- 4.6+
- 466
Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
Data Engineering
Course
Statistical Techniques in Tableau
- IntermediateSkill Level
- 4.7+
- 464
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
Probability & Statistics
Course
Intermediate Importing Data in R
- IntermediateSkill Level
- 4.6+
- 463
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
Data Preparation
Course
Power BI for End Users
- BasicSkill Level
- 4.3+
- 463
Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.
Reporting
Course
Visualizing Time Series Data in Python
- IntermediateSkill Level
- 4.6+
- 457
Visualize seasonality, trends and other patterns in your time series data.
Data Visualization
Course
Ensemble Methods in Python
- AdvancedSkill Level
- 4.7+
- 455
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Machine Learning
Course
Introduction to Julia
- BasicSkill Level
- 4.8+
- 452
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
Software Development
Course
Web Scraping in R
- IntermediateSkill Level
- 4.3+
- 451
Learn how to efficiently collect and download data from any website using R.
Data Preparation
Course
Introduction to Portfolio Analysis in Python
- AdvancedSkill Level
- 4.6+
- 441
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 Management
Course
AI-Assisted Travel Planning
- BasicSkill Level
- 4.5+
- 441
Master travel planning with WanderBot: craft prompts, build confidence, and streamline your next adventure.
Artificial Intelligence
Course
Monitoring Machine Learning Concepts
- IntermediateSkill Level
- 4.6+
- 440
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
Machine Learning
Course
Graph RAG with LangChain and Neo4j
- AdvancedSkill Level
- 4.6+
- 439
Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.
Artificial Intelligence
Course
Multi-Modal Systems with the OpenAI API
- IntermediateSkill Level
- 4.6+
- 434
Create multi-modal systems using OpenAIs text and audio models, including an end-to-end customer support chatbot!
Artificial Intelligence
Course
Case Study: Analyzing Job Market Data in Tableau
- BasicSkill Level
- 4.5+
- 432
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
Introduction to Data Quality with Great Expectations
- IntermediateSkill Level
- 4.5+
- 430
Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.
Data Engineering
Course
Machine Learning with caret in R
- IntermediateSkill Level
- 4.9+
- 428
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Machine Learning
Course
Corporate Finance Fundamentals
- BasicSkill Level
- 4.7+
- 427
Learn key financial concepts such as capital investment, WACC, and shareholder value.
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.