Course
Microsoft Copilot in Excel
- BasicSkill Level
- 4.4+
- 993
Stop fighting Excel and start talking to it! Use Copilot in Excel to clean data, build charts, and get answers faster.
Artificial Intelligence
Data, AI, and Cloud Courses
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Course
Introduction to Statistics in Google Sheets
- BasicSkill Level
- 4.4+
- 976
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.
Probability & Statistics
Course
Machine Learning with PySpark
- AdvancedSkill Level
- 4.7+
- 892
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Machine Learning
Course
Marketing Analytics for Business
- BasicSkill Level
- 4.7+
- 868
Discover how Marketing Analysts use data to understand customers and drive business growth.
Leadership
Course
MLOps Deployment and Life Cycling
- AdvancedSkill Level
- 4.6+
- 856
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
Machine Learning
Course
Reporting with R Markdown
- IntermediateSkill Level
- 4.3+
- 737
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
Reporting
Course
CI/CD for Machine Learning
- AdvancedSkill Level
- 4.5+
- 695
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
Machine Learning
Course
Image Modeling with Keras
- AdvancedSkill Level
- 4.7+
- 684
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Artificial Intelligence
Course
Statistical Thinking in Python (Part 1)
- IntermediateSkill Level
- 4.4+
- 678
Build the foundation you need to think statistically and to speak the language of your data.
Probability & Statistics
Course
Financial Trading in Python
- IntermediateSkill Level
- 4.7+
- 652
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Applied Finance
Course
Introduction to Bioconductor in R
- IntermediateSkill Level
- 4.4+
- 595
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
Probability & Statistics
Course
Case Study: Net Revenue Management in Excel
- IntermediateSkill Level
- 4.5+
- 534
You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.
Applied Finance
Course
Introduction to AWS Boto in Python
- IntermediateSkill Level
- 4.6+
- 504
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
Cloud
Course
Foundations of Inference in R
- IntermediateSkill Level
- 4.4+
- 491
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Probability & Statistics
Course
RNA-Seq with Bioconductor in R
- IntermediateSkill Level
- 4.5+
- 489
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Power BI for End Users
- BasicSkill Level
- 4.3+
- 468
Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.
Reporting
Course
Introduction to Financial Statements in Power BI
- IntermediateSkill Level
- 4.7+
- 420
Discover how to use the income statement and balance sheet in Power BI
Applied Finance
Course
Hierarchical and Mixed Effects Models in R
- AdvancedSkill Level
- 4.4+
- 398
In this course you will learn to fit hierarchical models with random effects.
Probability & Statistics
Course
Analyzing Financial Statements in Python
- IntermediateSkill Level
- 4.2+
- 375
Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
Applied Finance
Course
Statistical Thinking in Python (Part 2)
- IntermediateSkill Level
- 4.5+
- 256
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Probability & Statistics
Course
Foundations of Inference in Python
- AdvancedSkill Level
- 4.5+
- 207
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.
Probability & Statistics
Course
Monitor and Troubleshoot Azure Solutions
- IntermediateSkill Level
- 4.5+
- 200
Learn how to monitor, diagnose, and optimize Azure applications using Azure Monitor, Application Insights, and Log Analytics.
Cloud
Course
Introduction to Natural Language Processing in R
- IntermediateSkill Level
- 4.5+
- 144
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
Machine Learning
Course
Python for MATLAB Users
- BasicSkill Level
- 4.5+
- 139
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
Software Development
Machine Learning
Course
DataLab with SQL
- BasicSkill Level
- 4.3+
- 123
Elevate your analysis with this hands-on course using SQL with DataLab workbooks.
Reporting
Course
Intermediate Regular Expressions in R
- IntermediateSkill Level
- 4.8+
- 119
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Software Development
Course
Case Studies in Statistical Thinking
- IntermediateSkill Level
- 4.7+
- 109
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.
Probability & Statistics
Course
ChIP-seq with Bioconductor in R
- IntermediateSkill Level
- 4.7+
- 106
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.
Probability & Statistics
Course
Intermediate Predictive Analytics in Python
- BasicSkill Level
- 4.3+
- 84
Learn how to prepare and organize your data for predictive analytics.
Machine Learning
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.