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.

Data, AI, and Cloud Courses

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Intermediate Importing Data in Python

Improve your Python data importing skills and learn to work with web and API data.

BasicSkill Level

2-3 hoursData PreparationHugo Bowne-AndersonCourse

Data Structures and Algorithms in Python

Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!

AdvancedSkill Level

Over 3 hoursSoftware DevelopmentMiriam AntonaCourse

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Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.

IntermediateSkill Level

Over 3 hoursData ManipulationJacob MarquezCourse

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Discover the different ways you can enhance your Power BI data importing skills.

BasicSkill Level

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IntermediateSkill Level

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Learn about Microsoft Copilot and 365 Copilot to enhance productivity, streamline workflows, and make informed, data-driven decisions in your business.

BasicSkill Level

1-2 hoursArtificial IntelligenceShreya VashistCourse

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Master Responsible AI Practices with this comprehensive course, featuring real-world case studies and interactive content.

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2-3 hoursArtificial IntelligenceEsther Montoya van EgerschotCourse

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Build Python skills to elevate your finance career. Learn how to work with lists, arrays and data visualizations to master financial analyses.

BasicSkill Level

Over 3 hoursApplied FinanceAdina HoweCourse

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Continue your data visualization journey where youll learn practical techniques for incorporating DAX measures and progressive disclosure in your reports.

IntermediateSkill Level

Over 3 hoursData VisualizationKevin FeaselCourse

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Learn how to deploy and maintain assets in Power BI. You’ll get to grips with the Power BI Service interface and key elements in it like workspaces.

IntermediateSkill Level

2-3 hoursData ManipulationKevin FeaselCourse

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Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

IntermediateSkill Level

Over 3 hoursProbability & StatisticsRichie CottonCourse

Machine Learning with Tree-Based Models in Python

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IntermediateSkill Level

Over 3 hoursMachine LearningElie KawerkCourse

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Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

IntermediateSkill Level

Over 3 hoursSoftware DevelopmentLogan ThomasCourse

Introduction to LLMs in Python

Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!

IntermediateSkill Level

Over 3 hoursArtificial IntelligenceIván Palomares CarrascosaCourse

Introduction to Regression with statsmodels in Python

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

IntermediateSkill Level

Over 3 hoursProbability & StatisticsMaarten Van den BroeckCourse

Introduction to Power Query in Excel

Explore Excel Power Query for advanced data transformation and cleansing to boost your decision-making and analysis.

BasicSkill Level

Over 3 hoursData PreparationLyndsay GirardCourse

Introduction to Data Quality

Explore the basics of data quality management. Learn the key concepts, dimensions, and techniques for monitoring and improving data quality.

BasicSkill Level

2-3 hoursData ManagementChrissy BloomCourse

MLOps Concepts

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

IntermediateSkill Level

2-3 hoursMachine LearningFolkert StijnmanCourse

Intermediate ChatGPT

Learn the architecture behind GPT models and master advanced prompt crafting to unlock ChatGPTs full potential.

BasicSkill Level

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Understanding Microsoft Azure

Learn about the power of Microsoft Azure and cloud computing software to help you improve your data engineering skills.

BasicSkill Level

Over 3 hoursCloudKevin JamesCourse

Introduction to Apache Airflow in Python

Learn how to implement and schedule data engineering workflows.

AdvancedSkill Level

Over 3 hoursData EngineeringMike MetzgerCourse

Introduction to Shell

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.

BasicSkill Level

Over 3 hoursSoftware DevelopmentFilip SchouwenaarsCourse

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IntermediateSkill Level

Over 3 hoursData EngineeringMike MetzgerCourse

ETL and ELT in Python

Learn to build effective, performant, and reliable data pipelines using Extract, Transform, and Load principles.

IntermediateSkill Level

Over 3 hoursData EngineeringJake McGrathCourse

Hypothesis Testing in Python

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

IntermediateSkill Level

Over 3 hoursProbability & StatisticsJames ChapmanCourse

Data Literacy Case Study: Remote Working Analysis

Improve data literacy skills by analyzing remote working policies.

BasicSkill Level

1-2 hoursData LiteracyAnneleen RummensCourse

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Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

IntermediateSkill Level

Over 3 hoursProbability & StatisticsJames ChapmanCourse

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Leverage the OpenAI API to get your AI applications ready for production.

IntermediateSkill Level

Over 3 hoursArtificial IntelligenceFrancesca DonadoniCourse

Introduction to Google Sheets

Bring your Google Sheets to life by mastering fundamental skills such as formulas, operations, and cell references.

BasicSkill Level

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FAQs

Intermediate Importing Data in Python

Data Structures and Algorithms in Python

Trend Analysis in Power BI

Data Connections in Power BI

Data Warehousing Concepts

Introduction to Microsoft Copilot

Forming Analytical Questions

Responsible AI Practices

Introduction to Python for Finance

Report Design in Power BI

Deploying and Maintaining Assets in Power BI

Introduction to Regression in R

Machine Learning with Tree-Based Models in Python

Writing Efficient Python Code

Introduction to LLMs in Python

Introduction to Regression with statsmodels in Python

Introduction to Power Query in Excel

Introduction to Data Quality

MLOps Concepts

Intermediate ChatGPT

Understanding Microsoft Azure

Introduction to Apache Airflow in Python

Introduction to Shell

Introduction to dbt

ETL and ELT in Python

Hypothesis Testing in Python

Data Literacy Case Study: Remote Working Analysis

Sampling in Python

Developing AI Systems with the OpenAI API

Introduction to Google Sheets

Skill Level

technology

topic

FAQs

How can I learn data science?

What skills are required for data science?

What can I use data science for?

Is data science a good career?

Is it difficult to become a data scientist?

Does data science require coding?

How long does it take to become a data scientist?

What topics can I study within data science?