Course
Data-Driven Decision Making for Business
- BasicSkill Level
- 4.6+
- 1.3K
Discover how to make better business decisions by applying practical data frameworks—no coding required.
Leadership
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Discover how to make better business decisions by applying practical data frameworks—no coding required.
Leadership
Course
Learn about data science for managers and businesses and how to use data to strengthen your organization.
Data Literacy
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In this Introduction to DevOps, you’ll master the DevOps basics and learn the key concepts, tools, and techniques to improve productivity.
Software Development
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Apply AI in finance to analyze data, prompt effectively, and automate workflows for better decisions.
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Master AWS security, governance, and cost optimization to prepare for the Cloud Practitioner certification.
Cloud
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In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Machine Learning
Course
Master the key concepts of data management, from life cycle stages to security and governance.
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Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Machine Learning
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Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.
Machine Learning
Course
Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.
Data Preparation
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Unlock BigQuerys power: grasp its fundamentals, execute queries, and optimize workflows for efficient data analysis.
Data Engineering
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Take your R skills up a notch by learning to write efficient, reusable functions.
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Start your reinforcement learning journey! Learn how agents can learn to solve environments through interactions.
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Master multi-stage builds, Docker networking tools, and Docker Compose for optimal containerized applications!
Software Development
Course
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
Probability & Statistics
Course
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
Probability & Statistics
Course
In this course you will learn the basics of machine learning for classification.
Machine Learning
Course
Learn how to create a PostgreSQL database and explore the structure, data types, and how to normalize databases.
Data Preparation
Course
Transform almost any dataset into a tidy format to make analysis easier.
Data Manipulation
Course
What makes LLMs tick? Discover how transformers revolutionized text modeling and kickstarted the generative AI boom.
Artificial Intelligence
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Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
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Data Visualization
Course
Master data manipulation and analysis techniques such as CASE statements, subqueries, and CTEs in Snowflake.
Data Manipulation
Course
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Applied Finance
Course
Create interactive data visualizations in Python using Plotly.
Data Visualization
Course
Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.
Artificial Intelligence
Course
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Machine Learning
Course
Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.
Artificial Intelligence
Course
Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.
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Course
Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.
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.