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Hypothesis Testing in Python
- IntermediateSkill Level
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Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Probability & Statistics
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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.292 Courses
Course
AWS Cloud Technology and Services Concepts
- BasicSkill Level
- 4.6+
- 2.2K
Master AWS cloud technology with hands-on learning and practical applications in the AWS ecosystem.
Cloud
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Multi-Agent Systems with LangGraph
- AdvancedSkill Level
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Build powerful multi-agent systems by applying emerging agentic design patterns in the LangGraph framework.
Artificial Intelligence
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Introduction to Microsoft Fabric
- BasicSkill Level
- 4.6+
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To understand Fabric’s main use cases, you will explore various tools in the seven Fabric experiences.
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Introduction to Bash Scripting
- IntermediateSkill Level
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Software Development
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Model Validation in Python
- IntermediateSkill Level
- 4.6+
- 955
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Machine Learning
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Natural Language Processing with spaCy
- IntermediateSkill Level
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- 571
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Machine Learning
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Math for Finance Professionals
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Applied Finance
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Marketing Analytics in Google Sheets
- IntermediateSkill Level
- 4.5+
- 380
Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.
Reporting
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Hierarchical and Recursive Queries in SQL Server
- AdvancedSkill Level
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- 308
Learn how to write recursive queries and query hierarchical data structures.
Software Development
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Optimizing Code in Java
- AdvancedSkill Level
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- 303
Learn key techniques to optimize Java performance, from algorithm efficiency to JVM tuning and multithreading.
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Customer Analytics and A/B Testing in Python
- IntermediateSkill Level
- 4.4+
- 277
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Probability & Statistics
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Statistical Simulation in Python
- IntermediateSkill Level
- 4.7+
- 268
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Probability & Statistics
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Quantitative Risk Management in R
- BasicSkill Level
- 4.2+
- 192
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Applied Finance
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Parallel Programming with Dask in Python
- IntermediateSkill Level
- 4.8+
- 98
Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.
Software Development
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Introduction to Statistics in Python
- IntermediateSkill Level
- 4.5+
- 8.7K
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Probability & Statistics
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Introduction to Statistics in R
- IntermediateSkill Level
- 4.4+
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Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Probability & Statistics
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Plan and Implement a Data Analytics Environment with Microsoft Fabric
- BasicSkill Level
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- 285
Learn how to set up and manage your Microsoft Fabric infrastructure.
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Data Visualization in KNIME
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Learn to create compelling data visualizations with KNIME, covering charts, components, and dashboards.
Data Visualization
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Window Functions in Snowflake
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Discover Snowflake window functions to solve complex data problems with rankings, partitions, and rolling calculations.
Data Manipulation
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Analyzing Business Data in SQL
- IntermediateSkill Level
- 4.7+
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Learn to write SQL queries to calculate key metrics that businesses use to measure performance.
Reporting
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Cleaning Data in R
- IntermediateSkill Level
- 4.4+
- 1.2K
Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.
Data Preparation
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Introduction to Relational Databases in SQL
- BasicSkill Level
- 4.7+
- 6.4K
Learn how to create one of the most efficient ways of storing data - relational databases!
Software Development
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Introduction to Data Culture
- BasicSkill Level
- 4.5+
- 4.2K
Learn the key components of building a strong data culture within an organization.
Data Literacy
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Introduction to Data Privacy
- BasicSkill Level
- 4.7+
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Gain a clear understanding of data privacy principles and how to implement privacy and security processes.
Data Literacy
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Introduction to Data Quality
- BasicSkill Level
- 4.5+
- 1.8K
Explore the basics of data quality management. Learn the key concepts, dimensions, and techniques for monitoring and improving data quality.
Data Management
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Introduction to Data Ethics
- BasicSkill Level
- 4.6+
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Explore data ethics with this comprehensive introductory course, covering principles, AI ethics, and practical skills to ensure responsible data use.
Data Literacy
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Intermediate Importing Data in R
- IntermediateSkill Level
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Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
Data Preparation
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Working with Dates and Times in R
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Learn the essentials of parsing, manipulating and computing with dates and times in R.
Software Development
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.