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Multivariate Probability Distributions in R
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Learn to analyze, plot, and model multivariate data.
Probability & Statistics
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Time Series Analysis in SQL Server
- IntermediateSkill Level
- 4.5+
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Explore ways to work with date and time data in SQL Server for time series analysis
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Market Basket Analysis in R
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Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.
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Understanding Data Science
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An introduction to data science with no coding involved.
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Data Manipulation with pandas
- BasicSkill Level
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Learn how to import and clean data, calculate statistics, and create visualizations with pandas.
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Introduction to Data Literacy
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Data is all around us, which makes data literacy an essential life skill.
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Data Preparation in Excel
- BasicSkill Level
- 4.6+
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Understand how to prepare Excel data through logical functions, nested formulas, lookup functions, and PivotTables.
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Joining Data with pandas
- IntermediateSkill Level
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Learn to combine data from multiple tables by joining data together using pandas.
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Data Modeling in Power BI
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Learn the key concepts of data modeling on Power BI.
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Introduction to Data Visualization with Seaborn
- BasicSkill Level
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Learn how to create informative and attractive visualizations in Python using the Seaborn library.
Data Visualization
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Data Visualization in Excel
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Learn how to create a range of visualizations in Excel for different data layouts, ensuring you incorporate best practices to help you build dashboards.
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Cleaning Data in Python
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Data Storytelling Concepts
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Elevate your data storytelling skills and discover how to tell great stories that drive change with your audience.
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Trend Analysis in Power BI
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Analyzing Data in Tableau
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Take your Tableau skills up a notch with advanced analytics and visualizations.
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Introduction to Importing Data in R
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Working with Dates and Times in Python
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Learn how to work with dates and times in Python.
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Big Data Fundamentals with PySpark
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Financial Analysis in Power BI
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Reshaping Data with pandas
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Data Manipulation
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Time Series Analysis in Python
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In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
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Cluster Analysis in Python
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Transform almost any dataset into a tidy format to make analysis easier.
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Learn the core techniques necessary to extract meaningful insights from time series data.
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Data Validation in Excel
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Linear Algebra for Data Science in R
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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.