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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Dealing with Missing Data in Python

Learn how to identify, analyze, remove and impute missing data in Python.

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Over 3 hoursData ManipulationSuraj DonthiCourse

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Learn how to structure your PostgreSQL queries to run in a fraction of the time.

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Over 3 hoursSoftware DevelopmentAmy McCartyCourse

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Master SQL Server programming by learning to create, update, and execute functions and stored procedures.

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Learn how to approach and win competitions on Kaggle.

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Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.

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Learn how to detect fraud using Python.

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Over 3 hoursData VisualizationShivali MalhotraCourse

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BasicSkill Level

Over 3 hoursData VisualizationTimo GrossenbacherCourse

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Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

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Over 3 hoursProbability & StatisticsAlexander RamírezCourse

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Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.

BasicSkill Level

Over 3 hoursData VisualizationNicholas StrayerCourse

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Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

IntermediateSkill Level

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Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.

IntermediateSkill Level

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Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

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Introduction to Text Analysis in R

Analyze text data in R using the tidy framework.

IntermediateSkill Level

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Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

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Using Python and NumPy, learn the most fundamental financial concepts.

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Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.

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FAQs

Dealing with Missing Data in Python

Statistical Techniques in Tableau

Improving Query Performance in PostgreSQL

Writing Functions and Stored Procedures in SQL Server

Winning a Kaggle Competition in Python

Intermediate Data Visualization with Seaborn

Streaming Data with AWS Kinesis and Lambda

RNA-Seq with Bioconductor in R

Fraud Detection in Python

Case Study: Analyzing Job Market Data in Tableau

Math for Finance Professionals

Communicating with Data in the Tidyverse

Foundations of Probability in Python

AI-Assisted Travel Planning

Data Transformation in Alteryx

Visualization Best Practices in R

Monitoring Machine Learning Concepts

Building Web Applications with Shiny in R

Statistical Thinking in Python (Part 2)

Introduction to Text Analysis in R

Improving Query Performance in SQL Server

Case Study: Exploratory Data Analysis in R

Introduction to Financial Concepts in Python

Multi-Modal Models with Hugging Face

Intermediate Importing Data in R

Fine-Tuning with Llama 3

Time Series Analysis in SQL Server

Case Study: Analyzing Job Market Data in Power BI

Time Series Analysis in R

Data Ingestion and Semantic Models with Microsoft Fabric

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?