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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Importing and Managing Financial Data in R

Learn how to access financial data from local files as well as from internet sources.

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Over 3 hoursApplied FinanceJoshua UlrichCourse

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Learn how to pull character strings apart, put them back together and use the stringr package.

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Over 3 hoursSoftware DevelopmentCharlotte WickhamCourse

Analyzing US Census Data in Python

Learn to use the Census API to work with demographic and socioeconomic data.

IntermediateSkill Level

Over 3 hoursExploratory Data AnalysisLee HachadoorianCourse

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Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.

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Advanced NLP with spaCy

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

IntermediateSkill Level

Over 3 hoursMachine LearningInes MontaniCourse

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Learn how to use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.

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Over 3 hoursData ManipulationJasmin LudolfCourse

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Over 3 hoursApplied FinanceVictoria ClarkCourse

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Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.

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Over 3 hoursData VisualizationAgata Bak-GeerinckCourse

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Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.

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Over 3 hoursData ManipulationChristopher BruffaertsCourse

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Apply financial analysis in KNIME with real-world data, enhancing data preparation and workflow skills.

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Over 3 hoursApplied FinanceAndrew LoganCourse

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Learn to streamline your machine learning workflows with tidymodels.

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Over 3 hoursMachine LearningDavid SvancerCourse

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Learn the bag of words technique for text mining with R.

IntermediateSkill Level

Over 3 hoursMachine LearningTed KwartlerCourse

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Master data cleaning in Java using statistical methods, transformations, and validation for reliable apps.

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Over 3 hoursImporting & Cleaning DataDennis LeeCourse

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Over 3 hoursData VisualizationDev BhosaleCourse

Financial Trading in R

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.

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Over 3 hoursApplied FinanceIlya KipnisCourse

Feature Engineering in R

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

IntermediateSkill Level

Over 3 hoursMachine LearningJorge ZazuetaCourse

Intermediate Portfolio Analysis in R

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

IntermediateSkill Level

Over 3 hoursApplied FinanceRoss BennettCourse

Case Studies in Statistical Thinking

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

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Over 3 hoursProbability & StatisticsJustin BoisCourse

Machine Learning for Marketing Analytics in R

In this course youll learn how to use data science for several common marketing tasks.

IntermediateSkill Level

Over 3 hoursMachine LearningVerena PfliegerCourse

Equity Valuation in R

Learn the fundamentals of valuing stocks.

IntermediateSkill Level

Over 3 hoursApplied FinanceClifford AngCourse

Case Study: Inventory Analysis in Tableau

Enhance your Tableau skills with this case study on inventory analysis. Analyze a dataset, create calculated fields, and create visualizations.

IntermediateSkill Level

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Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.

IntermediateSkill Level

Over 3 hoursApplied FinanceBrent AllenCourse

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Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.

IntermediateSkill Level

Over 3 hoursData EngineeringRichie CottonCourse

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Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

IntermediateSkill Level

Over 3 hoursProbability & StatisticsEbunOluwa AndrewCourse

Case Study: Analyzing Healthcare Data in Tableau

Practice Tableau with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.

IntermediateSkill Level

Over 3 hoursData VisualizationMaarten Van den BroeckCourse

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GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

IntermediateSkill Level

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Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!

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Over 3 hoursData ManipulationJohn MillerCourse

FAQs

Importing and Managing Financial Data in R

String Manipulation with stringr in R

Analyzing US Census Data in Python

Developing R Packages

Advanced NLP with spaCy

Time Series Analysis in PostgreSQL

Financial Forecasting in Python

Case Study: Supply Chain Analytics in Tableau

Market Basket Analysis in R

Case Study: Financial Analysis in KNIME

Modeling with tidymodels in R

Text Mining with Bag-of-Words in R

Cleaning Data in Java

Case Study: Ecommerce Analysis in Tableau

Financial Trading in R

Feature Engineering in R

Intermediate Portfolio Analysis in R

Case Studies in Statistical Thinking

Machine Learning for Marketing Analytics in R

Equity Valuation in R

Case Study: Inventory Analysis in Tableau

Loan Amortization in Google Sheets

Introduction to Spark with sparklyr in R

Analyzing Survey Data in Python

Case Study: Analyzing Healthcare Data in Tableau

Nonlinear Modeling with Generalized Additive Models (GAMs) in R

Case Studies: Building Web Applications with Shiny in R

Programming with dplyr

Case Study: Analyzing City Time Series Data in R

Pandas Joins for Spreadsheet Users

Skill Level

technology

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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?