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AI for Marketing
- BasicSkill Level
- 4.7+
- 167 reviews
Master AI for marketing to plan smarter campaigns, create quality content, and build custom AI agents.
Artificial Intelligence
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771 Courses
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Data Visualization in Databricks
- BasicSkill Level
- 4.7+
- 596 reviews
Create visualizations and dynamic dashboards with Databricks, turning raw data into clear and actionable insights.
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Introduction to Power Automate
- BasicSkill Level
- 4.4+
- 25 reviews
Learn Power Automate hands-on: build cloud flows with Microsoft 365 connectors, dynamic content, expressions, approvals, and Copilot AI assistance.
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Gemini in Google Docs
- BasicSkill Level
- 4.8+
- 202 reviews
Write and edit faster with Gemini in Google Docs. Get AI-powered drafting, rewriting, and content suggestions to create clear, polished documents effortlessly.
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Reshaping Data with tidyr
- IntermediateSkill Level
- 4.8+
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Transform almost any dataset into a tidy format to make analysis easier.
Data Manipulation
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Gemini in Google Sheets
- BasicSkill Level
- 4.8+
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Analyze data smarter with Gemini in Google Sheets. Use AI-powered insights, formula suggestions, and automation to simplify spreadsheets and boost productivity.
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Cleaning Data with PySpark
- AdvancedSkill Level
- 4.7+
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Learn how to clean data with Apache Spark in Python.
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Pivot Tables in Google Sheets
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Hyperparameter Tuning in Python
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Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
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Google: Introduction to AI Agents
- BasicSkill Level
- 4.8+
- 118 reviews
Gain an overview of AI Agents. Discover how AI Agents use autonomous action and reasoning to solve complex problems.
Cloud
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Data Transformation in Alteryx
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- 778 reviews
Unlock Alteryx for data transformation, mastering Crosstab, Transpose, and workflow optimization in this interactive course.
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Introduction to Databases in Python
- IntermediateSkill Level
- 4.8+
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Generate a Study Guide
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Use a chatbot to create a study guide tailored to your goals and schedule. Build skills with simple, effective prompts.
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Learn the core techniques necessary to extract meaningful insights from time series data.
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AI for Sales
- BasicSkill Level
- 4.7+
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Introduction to Azure
- BasicSkill Level
- 4.8+
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Explore Azure services like Compute, Storage, and Automation, with hands-on experience.
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Machine Learning with PySpark
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Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
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Ask data questions in plain English with Databricks Genie - build spaces, curate business language, and monitor quality.
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Sentiment Analysis in Python
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Improving Query Performance in SQL Server
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Gemini in Google Drive
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Organize and manage files with Gemini in Google Drive. Use AI-powered search to quickly find information, streamline collaboration, and boost productivity.
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Manipulating Time Series Data in R
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A/B Testing in Python
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Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
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Developing Machine Learning Models for Production
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Introduction to Oracle SQL
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Linear Algebra for Data Science in R
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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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