Course
R For SAS Users
- BasicSkill Level
- 4.5+
- 144
Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.
Software Development
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669 Courses
Course
Support Vector Machines in R
- IntermediateSkill Level
- 4.5+
- 144
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Machine Learning
Course
Python for MATLAB Users
- BasicSkill Level
- 4.4+
- 143
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
Software Development
Course
Advanced NLP with spaCy
- IntermediateSkill Level
- 4.8+
- 142
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Machine Learning
Course
Dimensionality Reduction in R
- BasicSkill Level
- 4.5+
- 142
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
Machine Learning
Course
Gen AI: Unlock Foundational Concepts
- BasicSkill Level
- 4.7+
- 141
You unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI.
Cloud
Course
Discrete Event Simulation in Python
- AdvancedSkill Level
- 4.5+
- 141
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Pythons SimPy package.
Probability & Statistics
Course
Credit Risk Modeling in R
- IntermediateSkill Level
- 4.6+
- 140
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Applied Finance
Course
Trust and Security with Google Cloud
- BasicSkill Level
- 4.8+
- 139
Trust and Security with Google Cloud
Cloud
Course
Sentiment Analysis in R
- IntermediateSkill Level
- 4.6+
- 138
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Machine Learning
Course
Feature Engineering in R
- IntermediateSkill Level
- 4.5+
- 137
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Machine Learning
Course
Cleaning Data in Java
- IntermediateSkill Level
- 4.7+
- 135
Master data cleaning in Java using statistical methods, transformations, and validation for reliable apps.
Importing & Cleaning Data
Course
Nonlinear Modeling with Generalized Additive Models (GAMs) in R
- IntermediateSkill Level
- 4.4+
- 135
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
Probability & Statistics
Course
Pandas Joins for Spreadsheet Users
- IntermediateSkill Level
- 4.4+
- 131
Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.
Data Manipulation
Course
Modernize Infrastructure and Applications with Google Cloud
- BasicSkill Level
- 4.8+
- 129
Modernize Infrastructure and Applications with Google Cloud
Cloud
Course
Gen AI: Navigate the Landscape
- BasicSkill Level
- 4.7+
- 129
You explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.
Cloud
Course
Foundations of Functional Programming with purrr
- IntermediateSkill Level
- 4.5+
- 129
Learn to easily summarize and manipulate lists using the purrr package.
Software Development
Course
Loan Amortization in Google Sheets
- IntermediateSkill Level
- 3.5+
- 129
Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.
Applied Finance
Course
Business Process Analytics in R
- IntermediateSkill Level
- 4.5+
- 127
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Reporting
Machine Learning
Course
Analyzing Social Media Data in R
- IntermediateSkill Level
- 4.6+
- 126
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Data Manipulation
Course
Introduction to Spark with sparklyr in R
- IntermediateSkill Level
- 4.6+
- 125
Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
Data Engineering
Course
Intermediate Regular Expressions in R
- IntermediateSkill Level
- 4.8+
- 124
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Software Development
Course
Preparing for Your Associate Cloud Engineer Journey
- IntermediateSkill Level
- 4.7+
- 122
This course helps your preparation for the Associate Cloud Engineer exam, learn about the Google Cloud domains in the exam and create a study plan.
Cloud
Course
DataLab with SQL
- BasicSkill Level
- 4.2+
- 122
Elevate your analysis with this hands-on course using SQL with DataLab workbooks.
Reporting
Course
Case Studies in Statistical Thinking
- IntermediateSkill Level
- 4.7+
- 119
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.
Probability & Statistics
Course
Financial Trading in R
- IntermediateSkill Level
- 4.7+
- 118
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Applied Finance
Course
Life Insurance Products Valuation in R
- BasicSkill Level
- 4.6+
- 116
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
Applied Finance
Course
Case Study: Analyzing Healthcare Data in Tableau
- IntermediateSkill Level
- 4.7+
- 113
Practice Tableau with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Data Visualization
Course
Case Study: HR Analytics in Tableau
- IntermediateSkill Level
- 4.7+
- 112
Explore HR data analysis in Tableau with this case study.
Data Visualization
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.