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Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Machine Learning
Course
Explore Azure services like Compute, Storage, and Automation, with hands-on experience.
Cloud
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Learn Snowflake data types and functions to manipulate text, numbers, and dates while building custom functions and pivot tables.
Data Manipulation
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Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Machine Learning
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In this course, youll learn the basics of relational databases and how to interact with them.
Data Manipulation
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In this course, students will learn to write queries that are both efficient and easy to read and understand.
Software Development
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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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Learn to bring data into Microsoft Fabric, covering Pipelines, Dataflows, Shortcuts, Semantic Models, security, and model refresh.
Other
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Learn how to design Power BI visualizations and reports with users in mind.
Data Visualization
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Go beyond MCP basics with sampling, notifications, roots, and the STDIO and StreamableHTTP transports in Python.
Artificial Intelligence
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Learn AI agent fundamentals — how they differ from LLMs, when to use them, and explore agent architecture, orchestration, and tools.
Cloud
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Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
Machine Learning
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Orchestrate data using unions, joins, parsing, and performance optimization in Alteryx.
Data Manipulation
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Take Polars further with text manipulation, rolling statistics, DataFrame joins, and advanced analytics.
Data Manipulation
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Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Data Manipulation
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Master travel planning with WanderBot: craft prompts, build confidence, and streamline your next adventure.
Artificial Intelligence
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Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
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Prepare for your next coding interviews in Python.
Software Development
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In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Data Engineering
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Expand your Google Sheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Data Preparation
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Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
Artificial Intelligence
Course
In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!
Data Visualization
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Learn essential finance math skills with practical Excel exercises and real-world examples.
Applied Finance
Course
Create a go-to-market strategy with generative AI: target industries, generate leads, and optimize website keywords.
Artificial Intelligence
Course
Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
Artificial Intelligence
Course
Discover Snowflake window functions to solve complex data problems with rankings, partitions, and rolling calculations.
Data Manipulation
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Build marketing workflows in n8n using AI agents. Automate campaign strategy, conversion optimization, and lead generation from scratch.
Artificial Intelligence
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Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Machine Learning
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Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Applied Finance
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Learn key techniques to optimize Java performance, from algorithm efficiency to JVM tuning and multithreading.
Software Development
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.
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