Course
Power BI for End Users
- BasicSkill Level
- 4.8+
- 482
Explore Power BI Service, master the interface, make informed decisions, and maximize the power of your reports.
Reporting
Data, AI, and Cloud Courses
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Course
Window Functions in Snowflake
- IntermediateSkill Level
- 4.9+
- 480
Discover Snowflake window functions to solve complex data problems with rankings, partitions, and rolling calculations.
Data Manipulation
Course
Dealing With Missing Data in R
- BasicSkill Level
- 4.8+
- 478
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Data Preparation
Course
Importing and Managing Financial Data in Python
- IntermediateSkill Level
- 4.8+
- 477
In this course, youll learn how to import and manage financial data in Python using various tools and sources.
Applied Finance
Course
Gemini in Google Docs
- BasicSkill Level
- 4.9+
- 475
Write and edit faster with Gemini in Google Docs. Get AI-powered drafting, rewriting, and content suggestions to create clear, polished documents effortlessly.
Artificial Intelligence
Course
Case Study: Net Revenue Management in Excel
- IntermediateSkill Level
- 4.8+
- 472
You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.
Applied Finance
Course
Case Study: Analyzing Healthcare Data in Power BI
- IntermediateSkill Level
- 4.9+
- 466
Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Data Visualization
Course
Math for Finance Professionals
- BasicSkill Level
- 4.8+
- 466
Learn essential finance math skills with practical Excel exercises and real-world examples.
Applied Finance
Course
Ensemble Methods in Python
- AdvancedSkill Level
- 4.9+
- 464
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Machine Learning
Course
Supervised Learning in R: Regression
- IntermediateSkill Level
- 4.7+
- 462
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Machine Learning
Course
Introduction to TensorFlow in Python
- IntermediateSkill Level
- 4.8+
- 460
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Machine Learning
Course
Foundations of PySpark
- IntermediateSkill Level
- 4.7+
- 458
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Data Engineering
Course
Gemini in Google Slides
- BasicSkill Level
- 4.8+
- 457
Create impactful presentations faster with Gemini in Google Slides. Use AI-powered design and suggestions to build professional, engaging slides in minutes.
Artificial Intelligence
Course
RNA-Seq with Bioconductor in R
- IntermediateSkill Level
- 4.7+
- 453
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Gemini in Google Sheets
- BasicSkill Level
- 4.9+
- 452
Analyze data smarter with Gemini in Google Sheets. Use AI-powered insights, formula suggestions, and automation to simplify spreadsheets and boost productivity.
Artificial Intelligence
Course
Data Manipulation with data.table in R
- BasicSkill Level
- 4.7+
- 452
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Data Manipulation
Course
Financial Analytics in Google Sheets
- BasicSkill Level
- 4.7+
- 451
Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
Applied Finance
Course
Web Scraping in R
- IntermediateSkill Level
- 4.7+
- 448
Learn how to efficiently collect and download data from any website using R.
Data Preparation
Course
Experimental Design in R
- IntermediateSkill Level
- 4.7+
- 446
In this course youll learn about basic experimental design, a crucial part of any data analysis.
Probability & Statistics
Course
Hierarchical and Mixed Effects Models in R
- AdvancedSkill Level
- 4.7+
- 443
In this course you will learn to fit hierarchical models with random effects.
Probability & Statistics
Course
Case Study: Supply Chain Analytics in Power BI
- BasicSkill Level
- 4.8+
- 441
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
Visualizing Time Series Data in R
- IntermediateSkill Level
- 4.8+
- 439
Learn how to visualize time series in R, then practice with a stock-picking case study.
Data Visualization
Course
Feature Engineering with PySpark
- AdvancedSkill Level
- 4.8+
- 439
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Data Manipulation
Course
Introduction to AWS Boto in Python
- IntermediateSkill Level
- 4.8+
- 437
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
Cloud
Course
Visualizing Time Series Data in Python
- IntermediateSkill Level
- 4.8+
- 437
Visualize seasonality, trends and other patterns in your time series data.
Data Visualization
Course
Introduction to Spark SQL in Python
- AdvancedSkill Level
- 4.8+
- 436
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Data Manipulation
Course
Intermediate Importing Data in R
- IntermediateSkill Level
- 4.8+
- 435
Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.
Data Preparation
Course
Introduction to GCP
- BasicSkill Level
- 4.8+
- 435
Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
Cloud
Course
Streaming Concepts
- BasicSkill Level
- 4.8+
- 435
Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
Data Engineering
Course
ARIMA Models in Python
- AdvancedSkill Level
- 4.9+
- 434
Learn about ARIMA models in Python and become an expert in time series analysis.
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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