Course
Data Modeling in Power BI
- BasicSkill Level
- 4.8+
- 7,327 reviews
Learn the key concepts of data modeling on Power BI.
Data Manipulation
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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Learn the key concepts of data modeling on Power BI.
Data Manipulation
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Learn how to create informative and attractive visualizations in Python using the Seaborn library.
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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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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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Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
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Data storytelling is a high-demand skill that elevates analytics. Learn narrative building and visualizations in this course with a college major dataset!
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Build end-to-end data pipelines - from cleaning and aggregation to streaming and orchestration.
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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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Learn Excel data validation to improve accuracy, create drop-downs, and manage inventory and orders with confidence.
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Orchestrate data using unions, joins, parsing, and performance optimization in Alteryx.
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Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
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Transform almost any dataset into a tidy format to make analysis easier.
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Take Polars further with text manipulation, rolling statistics, DataFrame joins, and advanced analytics.
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Learn the core techniques necessary to extract meaningful insights from time series data.
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Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
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Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
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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.
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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
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.
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.
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.
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.
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.
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.
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.
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.
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.
Make progress on the go with our mobile courses and daily 5-minute coding challenges.