Course
AI-Assisted Product Launch
- BasicSkill Level
- 4.7+
- 395 reviews
Analyze market dynamics and craft a strategic entry plan for an EV manufacturer using generative AI.
Artificial Intelligence
Data, AI, and Cloud Courses
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775 Courses
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Case Study: Analyzing Healthcare Data in Power BI
- IntermediateSkill Level
- 4.8+
- 296 reviews
Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Data Visualization
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Supply Chain Analytics in Python
- IntermediateSkill Level
- 4.8+
- 98 reviews
Leverage the power of Python and PuLP to optimize supply chains.
Exploratory Data Analysis
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Case Study: Analyzing Job Market Data in Power BI
- BasicSkill Level
- 4.8+
- 345 reviews
Help a fictional company in this interactive Power BI case study. You’ll use Power Query, DAX, and dashboards to identify the most in-demand data jobs!
Data Manipulation
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Advanced Data Engineering with Snowflake
- IntermediateSkill Level
- 4.8+
- 30 reviews
Build reliable Snowflake pipelines with DevOps and observability: Git, CI/CD, and Snowflake Trail monitoring.
Data Engineering
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Foundations of Inference in R
- IntermediateSkill Level
- 4.7+
- 57 reviews
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Probability & Statistics
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Statistical Techniques in Tableau
- IntermediateSkill Level
- 4.8+
- 693 reviews
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
Probability & Statistics
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Fraud Detection in Python
- IntermediateSkill Level
- 4.7+
- 202 reviews
Learn how to detect fraud using Python.
Machine Learning
Course
Calculations in Sigma
- BasicSkill Level
- 4.8+
- 200 reviews
Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.
Data Manipulation
Course
Monitoring Machine Learning in Python
- AdvancedSkill Level
- 4.8+
- 399 reviews
This course covers everything you need to know to build a basic machine learning monitoring system in Python
Machine Learning
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Introduction to GCP
- BasicSkill Level
- 4.7+
- 364 reviews
Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
Cloud
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ARIMA Models in Python
- AdvancedSkill Level
- 4.8+
- 426 reviews
Learn about ARIMA models in Python and become an expert in time series analysis.
Machine Learning
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Introduction to Network Analysis in Python
- IntermediateSkill Level
- 4.7+
- 227 reviews
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Probability & Statistics
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Building Web Applications with Shiny in R
- IntermediateSkill Level
- 4.7+
- 228 reviews
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Software Development
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Graph RAG with LangChain and Neo4j
- AdvancedSkill Level
- 4.7+
- 121 reviews
Create more accurate and reliable RAG systems with Graph RAG and hybrid RAG.
Artificial Intelligence
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Introduction to TensorFlow in Python
- IntermediateSkill Level
- 4.8+
- 57 reviews
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Machine Learning
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Cleaning Data in Java
- IntermediateSkill Level
- 4.7+
- 119 reviews
Master data cleaning in Java using statistical methods, transformations, and validation for reliable apps.
Importing & Cleaning Data
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Sentiment Analysis in Python
- IntermediateSkill Level
- 4.8+
- 464 reviews
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Machine Learning
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Intermediate Regression with statsmodels in Python
- IntermediateSkill Level
- 4.8+
- 606 reviews
Learn to perform linear and logistic regression with multiple explanatory variables.
Probability & Statistics
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Databricks with the Python SDK
- AdvancedSkill Level
- 4.8+
- 95 reviews
Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.
Artificial Intelligence
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Develop Azure Event-based and Message-based Solutions
- IntermediateSkill Level
- 4.7+
- 128 reviews
Learn to design scalable event-driven architectures in Azure using messaging services and real-world integrations.
Cloud
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Introduction to Data Engineering on Google Cloud
- BasicSkill Level
- 4.7+
- 37 reviews
Learn the data engineering role on Google Cloud. Explore data sources, storage solutions, ETL/ELT architectures, BigQuery, Dataform, and Dataproc.
Cloud
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Data Modeling in Sigma
- BasicSkill Level
- 4.8+
- 133 reviews
Stop rewriting the same joins and calculations, and dive into well-governed, scalable analytics using Sigma data models.
Reporting
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Case Study: Analyzing Sales Data in Alteryx
- BasicSkill Level
- 4.8+
- 507 reviews
Explore Alteryx Designer in a retail data case study to boost sales analysis and strategic decision-making.
Data Preparation
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Fine-Tuning with Llama 3
- IntermediateSkill Level
- 4.7+
- 412 reviews
Fine-tune Llama for custom tasks using TorchTune, and learn techniques for efficient fine-tuning such as quantization.
Artificial Intelligence
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Serverless Applications with AWS Lambda
- IntermediateSkill Level
- 4.7+
- 45 reviews
Build, deploy, and optimize serverless apps with AWS Lambda. Master event processing, error handling, concurrency, and safe deployments in a live AWS Console.
Cloud
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Introduction to AI Apps in Sigma
- BasicSkill Level
- 4.8+
- 177 reviews
Build interactive AI apps in Sigma using user input, actions, and polished interfaces, no coding required.
Reporting
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Monitor and Troubleshoot Azure Solutions
- IntermediateSkill Level
- 4.7+
- 127 reviews
Learn how to monitor, diagnose, and optimize Azure applications using Azure Monitor, Application Insights, and Log Analytics.
Cloud
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Cleaning Data in PostgreSQL Databases
- IntermediateSkill Level
- 4.8+
- 474 reviews
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
Data Preparation
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Foundations of PySpark
- IntermediateSkill Level
- 4.7+
- 635 reviews
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Data Engineering
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.
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