Course
Time Series Analysis in Power BI
- IntermediateSkill Level
- 4.8+
- 347
Learn to analyze data over time with this practical course on Time Series Analysis in Power BI. Work with real datasets & practice common techniques.
Data Visualization
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Course
Baseball Data Visualization in Power BI
- BasicSkill Level
- 4.9+
- 346
Discover how to analyze and visualize baseball data using Power BI. Create scatter plots, tornado charts, and gauges to bring baseball insights alive.
Data Visualization
Course
Gemini in Google Meet
- BasicSkill Level
- 4.9+
- 344
Enhance virtual meetings with Gemini in Google Meet. Leverage AI-driven summaries, notes, and tools to make every meeting more efficient and actionable.
Artificial Intelligence
Course
Case Study: Ecommerce Analysis in Power BI
- IntermediateSkill Level
- 4.9+
- 344
In ecommerce, increasing sales and reducing costs are key. Analyze data from an online pet supply company using Power BI.
Data Visualization
Course
HR Analytics: Exploring Employee Data in R
- IntermediateSkill Level
- 4.8+
- 343
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Exploratory Data Analysis
Course
AI-Assisted Product Launch
- BasicSkill Level
- 4.8+
- 342
Analyze market dynamics and craft a strategic entry plan for an EV manufacturer using generative AI.
Artificial Intelligence
Course
Introduction to Data Versioning with DVC
- IntermediateSkill Level
- 4.8+
- 342
Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.
Machine Learning
Course
Supply Chain Analytics in Python
- IntermediateSkill Level
- 4.8+
- 339
Leverage the power of Python and PuLP to optimize supply chains.
Exploratory Data Analysis
Course
Interactive Maps with leaflet in R
- BasicSkill Level
- 4.7+
- 338
Learn how to produce interactive web maps with ease using leaflet.
Data Visualization
Course
Advanced Deep Learning with Keras
- IntermediateSkill Level
- 4.8+
- 335
Learn how to develop deep learning models with Keras.
Artificial Intelligence
Course
Case Study: Analyzing City Time Series Data in R
- IntermediateSkill Level
- 4.9+
- 334
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Probability & Statistics
Course
Data Fluency
- BasicSkill Level
- 4.8+
- 332
Master data fluency! Learn skills for individuals and organizations, understand behaviors, and build a data-fluent culture.
Data Literacy
Course
Case Study: Inventory Analysis in Power BI
- IntermediateSkill Level
- 4.8+
- 331
This Power BI case study follows a real-world business use case on tackling inventory analysis using DAX and visualizations.
Data Visualization
Course
GARCH Models in Python
- IntermediateSkill Level
- 4.8+
- 329
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Applied Finance
Course
Designing Machine Learning Workflows in Python
- AdvancedSkill Level
- 4.8+
- 329
Learn to build pipelines that stand the test of time.
Machine Learning
Course
AI-Assisted Restaurant Planning
- BasicSkill Level
- 4.8+
- 328
Interact with a customized GPT and use your prompting skills to plan and open your restaurant.
Artificial Intelligence
Course
Object-Oriented Programming with S3 and R6 in R
- AdvancedSkill Level
- 4.8+
- 327
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Software Development
Course
Multi-Modal Models with Hugging Face
- IntermediateSkill Level
- 4.8+
- 326
Combine text, images, audio, and video with the latest AI models from Hugging Face, and generate new images and videos!
Artificial Intelligence
Course
Optimizing Code in Java
- AdvancedSkill Level
- 4.8+
- 326
Learn key techniques to optimize Java performance, from algorithm efficiency to JVM tuning and multithreading.
Software Development
Course
Data Transformation with Spark SQL in Databricks
- IntermediateSkill Level
- 4.9+
- 320
Build end-to-end data pipelines - from cleaning and aggregation to streaming and orchestration.
Data Engineering
Course
Fundamentals of Bayesian Data Analysis in R
- IntermediateSkill Level
- 4.9+
- 319
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
Course
Building Agentic Workflows with LlamaIndex
- AdvancedSkill Level
- 4.7+
- 315
Build AI agentic workflows that can plan, search, remember, and collaborate, using LlamaIndex.
Artificial Intelligence
Course
Inference for Numerical Data in R
- AdvancedSkill Level
- 4.9+
- 314
In this course youll learn techniques for performing statistical inference on numerical data.
Probability & Statistics
Course
Azure Compute Solutions
- IntermediateSkill Level
- 4.8+
- 311
Learn how containers work in Azure, including registries, ACI, AKS basics, scaling, monitoring, and troubleshooting.
Cloud
Course
Introduction to DataLab
- BasicSkill Level
- 4.9+
- 309
Learn the fundamentals of using DataLab, an AI-powered data notebook for data analysis and exploration.
Reporting
Course
Building Marketing Workflows with n8n
- BasicSkill Level
- 4.9+
- 306
Build marketing workflows in n8n using AI agents. Automate campaign strategy, conversion optimization, and lead generation from scratch.
Artificial Intelligence
Course
Customer Segmentation in Python
- IntermediateSkill Level
- 4.9+
- 306
Learn how to segment customers in Python.
Data Manipulation
Course
Categorical Data in the Tidyverse
- BasicSkill Level
- 4.8+
- 306
Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.
Data Manipulation
Course
Hierarchical and Recursive Queries in SQL Server
- AdvancedSkill Level
- 4.8+
- 305
Learn how to write recursive queries and query hierarchical data structures.
Software Development
Course
Introduction to Portfolio Analysis in R
- BasicSkill Level
- 4.8+
- 305
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Applied Finance
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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