Course
Demystifying Decision Science
- BasicSkill Level
- 4.8+
- 284 reviews
Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions.
Data Literacy
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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Course
Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions.
Data Literacy
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Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
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Learn key financial concepts such as capital investment, WACC, and shareholder value.
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Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.
Data Manipulation
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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
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Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
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Learn about ARIMA models in Python and become an expert in time series analysis.
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Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
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Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Probability & Statistics
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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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Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
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Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
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Leverage the power of Python and PuLP to optimize supply chains.
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Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
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Learn to write scripts that will catch and handle errors and control for multiple operations happening at once.
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Learn how to approach and win competitions on Kaggle.
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Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
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Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Probability & Statistics
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Interact with a customized GPT and use your prompting skills to plan and open your restaurant.
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In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level of machine learning development pipelines.
Cloud
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.