Course
Data Processing in Shell
- IntermediateSkill Level
- 4.6+
- 584
Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
Data Manipulation
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Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
Data Manipulation
Course
Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.
Data Literacy
Course
Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions.
Data Literacy
Course
Build PowerPoint presentations with Microsoft Copilot. Turn documents into slides, generate visuals, and speaker notes.
Artificial Intelligence
Course
Master Microsoft Copilot in Word to write faster, understand documents instantly, and collaborate more effectively.
Artificial Intelligence
Course
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Machine Learning
Course
Learn to bring data into Microsoft Fabric, covering Pipelines, Dataflows, Shortcuts, Semantic Models, security, and model refresh.
Other
Course
Advance your Java skills by learning to handle files, process data, and build clean, reusable code using real-world techniques.
Software Development
Course
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
Data Manipulation
Course
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Machine Learning
Course
Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
Artificial Intelligence
Course
Create a go-to-market strategy with generative AI: target industries, generate leads, and optimize website keywords.
Artificial Intelligence
Course
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
Data Preparation
Course
Master SQL Server programming by learning to create, update, and execute functions and stored procedures.
Software Development
Course
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Machine Learning
Course
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Applied Finance
Course
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Data Engineering
Course
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Machine Learning
Course
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Machine Learning
Course
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Probability & Statistics
Course
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
Software Development
Course
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Probability & Statistics
Course
Learn essential finance math skills with practical Excel exercises and real-world examples.
Applied Finance
Course
In this course, youll learn how to import and manage financial data in Python using various tools and sources.
Applied Finance
Course
You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.
Applied Finance
Course
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Machine Learning
Course
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Data Engineering
Course
Learn how to use Power BI for supply chain analytics in this case study. Create a make vs. buy analysis tool, calculate costs, and analyze production volumes.
Data Visualization
Course
Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
Cloud
Course
Learn how to pull character strings apart, put them back together and use the stringr package.
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.