Course
Introduction to BigQuery
- IntermediateSkill Level
- 4.8+
- 962
Unlock BigQuerys power: grasp its fundamentals, execute queries, and optimize workflows for efficient data analysis.
Data Engineering
Data, AI, and Cloud Courses
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Course
Reshaping Data with tidyr
- IntermediateSkill Level
- 4.8+
- 952
Transform almost any dataset into a tidy format to make analysis easier.
Data Manipulation
Course
Case Study: Analyzing Customer Churn in Tableau
- BasicSkill Level
- 4.8+
- 949
You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
Data Visualization
Course
Feature Engineering for Machine Learning in Python
- IntermediateSkill Level
- 4.8+
- 945
Create new features to improve the performance of your Machine Learning models.
Machine Learning
Course
Data Storytelling Case Study: Green Businesses
- BasicSkill Level
- 4.8+
- 944
Practice data storytelling using real-world examples! Communicate complex insights effectively with a dataset of certified green businesses.
Data Literacy
Course
Intermediate Docker
- IntermediateSkill Level
- 4.7+
- 944
Master multi-stage builds, Docker networking tools, and Docker Compose for optimal containerized applications!
Software Development
Course
Analyzing Marketing Campaigns with pandas
- BasicSkill Level
- 4.8+
- 942
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
Exploratory Data Analysis
Course
Creating PostgreSQL Databases
- BasicSkill Level
- 4.8+
- 931
Learn how to create a PostgreSQL database and explore the structure, data types, and how to normalize databases.
Data Preparation
Course
AWS Security and Cost Management Concepts
- BasicSkill Level
- 4.8+
- 928
Master AWS security, governance, and cost optimization to prepare for the Cloud Practitioner certification.
Cloud
Course
Introduction to Bash Scripting
- IntermediateSkill Level
- 4.8+
- 921
Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.
Software Development
Course
Introduction to KNIME
- BasicSkill Level
- 4.8+
- 904
Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.
Data Preparation
Course
Developing Python Packages
- IntermediateSkill Level
- 4.8+
- 897
Learn to create your own Python packages to make your code easier to use and share with others.
Software Development
Course
Artificial Intelligence Governance
- BasicSkill Level
- 4.8+
- 893
Learn AI governance with Collibra. Build, embed, and scale responsible AI using tools, frameworks, and MLOps workflows.
Artificial Intelligence
Course
Introduction to NoSQL
- BasicSkill Level
- 4.8+
- 890
Conquer NoSQL and supercharge data workflows. Learn Snowflake to work with big data, Postgres JSON for handling document data, and Redis for key-value data.
Data Engineering
Course
Explainable Artificial Intelligence (XAI) Concepts
- BasicSkill Level
- 4.8+
- 889
Understand the role and real-world realities of Explainable Artificial Intelligence (XAI) with this beginner friendly course.
Artificial Intelligence
Course
Intermediate SQL Server
- IntermediateSkill Level
- 4.9+
- 885
In this course, you will use T-SQL, the flavor of SQL used in Microsofts SQL Server for data analysis.
Software Development
Course
Introduction to Apache Kafka
- IntermediateSkill Level
- 4.8+
- 884
Master Apache Kafka! From core concepts to advanced architecture, learn to create, manage, and troubleshoot Kafka for real-world data streaming challenges!
Data Engineering
Course
Time Series Analysis in R
- IntermediateSkill Level
- 4.9+
- 882
Learn the core techniques necessary to extract meaningful insights from time series data.
Probability & Statistics
Course
Model Validation in Python
- IntermediateSkill Level
- 4.9+
- 879
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Machine Learning
Course
Azure App Services
- IntermediateSkill Level
- 4.8+
- 873
Build and deploy scalable web apps and serverless functions in Azure while mastering security, monitoring, and automation.
Cloud
Course
Data Visualization in Tableau
- BasicSkill Level
- 4.8+
- 864
Data visualization is one of the most desired skills for data analysts. This course allows you to present your findings better using Tableau.
Data Visualization
Course
ARIMA Models in R
- BasicSkill Level
- 4.9+
- 858
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Probability & Statistics
Course
Microsoft Copilot in PowerPoint
- BasicSkill Level
- 4.8+
- 820
Build PowerPoint presentations with Microsoft Copilot. Turn documents into slides, generate visuals, and speaker notes.
Artificial Intelligence
Course
Introduction to Google Workspace with Gemini
- BasicSkill Level
- 4.8+
- 812
You learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.
Artificial Intelligence
Course
Cleaning Data with PySpark
- AdvancedSkill Level
- 4.8+
- 807
Learn how to clean data with Apache Spark in Python.
Data Preparation
Course
Connecting Data in Tableau
- BasicSkill Level
- 4.9+
- 806
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
Data Preparation
Course
Understanding Microsoft Azure Management and Governance
- BasicSkill Level
- 4.8+
- 804
Master Azure Management and Governance with our comprehensive course, ideal for data professionals seeking cloud expertise.
Cloud
Course
Data Types and Exceptions in Java
- IntermediateSkill Level
- 4.8+
- 789
Learn to work with Plain Old Java Objects, master the Collections Framework, and handle exceptions like a pro, with logging to back it all up!
Software Development
Course
Reporting in SQL
- IntermediateSkill Level
- 4.9+
- 787
Learn how to build your own SQL reports and dashboards, plus hone your data exploration, cleaning, and validation skills.
Reporting
Course
MLOps Deployment and Life Cycling
- AdvancedSkill Level
- 4.8+
- 787
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
Machine Learning
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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