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Intro to Data Science

Dave Holtz and Cheng-Han Lee

The Introduction to Data Science class will survey the foundational topics in data science, namely:

The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science.

This course is also a part of our Data Analyst Nanodegree.

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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Taught by Dave Holtz, Cheng-Han Lee, who are recognized for their work in data science
Explores foundational data science topics, equipping learners for diverse applications
Provides a comprehensive overview for those seeking to sample and apply data science techniques
Introduces breadth of data science topics, allowing for exploration of interests
Prerequisites recommended, encouraging learners to enhance their foundational skills

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Reviews summary

Broad introduction to data science

According to learners, this course provides a solid introduction to the core concepts of data science. Students particularly appreciate the hands-on labs and projects which offer practical experience with essential tools like Pandas, Matplotlib, and scikit-learn. While many find it a great starting point and a good overview, some note that topics are covered briefly and lack depth, suggesting it serves best as a foundation for further study. Learners also advise ensuring you meet the stated prerequisites in Python and statistics, as the course can be challenging for complete beginners. Overall, it's seen as a valuable first step into the field.
Learn practical tools like Pandas, SciPy.
"Working with Pandas and Matplotlib in the labs was very useful."
"Introduced me to scikit-learn and regression in a practical way."
"The course covers essential libraries used in the industry."
Hands-on labs reinforce concepts effectively.
"The coding exercises and the final project were incredibly helpful for applying what I learned."
"I really enjoyed the labs; they made the theory click into place."
"Learning by doing through the assignments was the best part."
Surveys essential data science topics.
"Gives a great overview of the foundational topics in data science."
"I felt like I touched on all the important areas I needed to know before diving deeper."
"Excellent way to see the landscape of data science tools and methods."
Topics are introduced but not explored deeply.
"As promised, it's a survey, so don't expect deep dives into any single topic."
"I had to do a lot of extra reading to understand some concepts fully."
"The brevity of some sections was frustrating; I wanted more detail."
Assumes prior Python and stats knowledge.
"Definitely need a solid grasp of Python before starting this course."
"Coming in with weak statistics made some parts challenging."
"The course moves fast if you're not already comfortable with the basics."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Intro to Data Science with these activities:
Explore data science concepts
Gain a foundational understanding of data science concepts and their applications.
View Data Science para Negócios on Amazon
Show steps
  • Read chapters 1-4 of the provided textbook to gain an overview of data science
  • Complete the quizzes and exercises at the end of each chapter to test your understanding
Review essential mathematical concepts
Build a solid foundation in mathematical concepts crucial for understanding data science techniques.
View Mathematics for Machine Learning on Amazon
Show steps
  • Read chapters 1-3 of the provided textbook to review basic linear algebra and calculus
  • Complete the practice exercises at the end of each chapter
Brush up on programming skills
Ensure proficiency in programming, a fundamental skill for data science.
Browse courses on Programming
Show steps
  • Review the basics of Python or R, the primary languages used in data science
  • Solve coding challenges on platforms like LeetCode or HackerRank to practice problem-solving skills
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a data science study group
Enhance understanding and retention by engaging in discussions and sharing knowledge with peers.
Show steps
  • Find a study group or create one with classmates
  • Meet regularly to discuss course material, work on assignments together, and ask questions
Develop a data science project plan
Foster critical thinking and problem-solving skills by creating a plan for a data science project.
Browse courses on Data Science Project
Show steps
  • Identify a problem or opportunity that can be addressed using data science
  • Define the project goals and objectives
  • Gather and explore relevant data
Practice data analysis and visualization techniques
Develop proficiency in data analysis and visualization, essential skills for exploring and presenting insights.
Browse courses on Data Analysis
Show steps
  • Download a dataset and use Python or R to clean and analyze it
  • Create visualizations such as histograms, scatterplots, and bar charts to represent the data
Participate in data science projects for non-profits
Gain practical experience and make a meaningful contribution by applying data science skills to real-world problems.
Browse courses on Data Science Projects
Show steps
  • Identify non-profit organizations that require data science assistance
  • Offer your skills and collaborate with them on a data-driven project
Develop a personalized data science learning plan
Tailor your learning journey by creating a personalized plan that aligns with your goals and interests.
Browse courses on Personalized Learning
Show steps
  • Reflect on your current knowledge and skills in data science
  • Identify areas where you need improvement
  • Develop a plan to bridge the gaps and enhance your abilities

Career center

Learners who complete Intro to Data Science will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists will carry out many different tasks in unison. They will collect, clean, and analyze raw data, turning it into a usable format. Common tools used for wrangling data include Python, R, SQL, Hive, and Hadoop. Data Scientists use statistical algorithms to predict trends, make recommendations, and solve problems.
See salaries and explore the career path for Data Scientist
Data Analyst
Data Analysts use their knowledge of data analysis, programming, and databases to solve business problems. Common programming languages include: Python, R, SQL, Hive, and Hadoop. Data Analysts use statistical algorithms to predict trends, make recommendations, and solve problems.
See salaries and explore the career path for Data Analyst
Machine Learning Engineer
Machine Learning Engineers are responsible for the design, development, and deployment of Machine Learning models. Common languages include: Python, R, SQL, Hive, and Hadoop. They also design and implement the pipelines that take raw data and prepare it for Machine Learning models.
See salaries and explore the career path for Machine Learning Engineer
Data Engineer
Data Engineers are responsible for the design, development, and maintenance of data pipelines. This includes collecting, cleaning, and transforming data so that it can be used by other applications and systems. Common tools include: Python, R, SQL, Hive, and Hadoop.
See salaries and explore the career path for Data Engineer
Statistician
Statisticians collect and analyze data to solve problems and support decision-making. They are employed in a variety of industries and use a variety of programming languages including: Python, R, SQL, Hive, and Hadoop.
See salaries and explore the career path for Statistician
Database Administrator
Database Administrators are responsible for the maintenance and performance of databases. This includes creating and managing databases, as well as backing them up and restoring them. Common database tools include: SQL, NoSQL, and Hadoop.
See salaries and explore the career path for Database Administrator
Business Analyst
Business Analysts advise businesses on how to improve their performance. They use data to identify opportunities and problems, and then develop and implement solutions. Common tools include: Python, R, SQL, Hive, and Hadoop.
See salaries and explore the career path for Business Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations.
See salaries and explore the career path for Quantitative Analyst
Data Architect
Data Architects design and manage the data infrastructure of an organization. This includes planning and implementing data architectures, as well as developing and maintaining data governance policies.
See salaries and explore the career path for Data Architect
Risk Analyst
Risk Analysts identify, assess, and mitigate risks for organizations. They use a variety of tools and techniques to analyze data and make recommendations for risk management.
See salaries and explore the career path for Risk Analyst
Information Security Analyst
Information Security Analysts protect an organization's data and information systems from unauthorized access, use, disclosure, disruption, modification, or destruction.
See salaries and explore the career path for Information Security Analyst
Financial Analyst
Financial Analysts provide research and analysis on financial markets and companies to help investors make informed investment decisions.
See salaries and explore the career path for Financial Analyst
Marketing Analyst
Marketing Analysts analyze market data to help businesses develop and execute marketing campaigns.
See salaries and explore the career path for Marketing Analyst
Software Engineer
Software Engineers design, develop, and maintain software applications. This includes writing code, testing it, and debugging it. Common programming languages include: Java, Python, C++, and C#. Software Engineers may also specialize in a particular area, such as data science or machine learning.
See salaries and explore the career path for Software Engineer
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in a variety of industries, such as manufacturing, logistics, and healthcare.
See salaries and explore the career path for Operations Research Analyst

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Intro to Data Science.
Cover image
Deep Learning
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A comprehensive textbook in deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. Provides a deep understanding of the principles and applications of deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
The Elements of Statistical Learning
Save
A classic textbook in statistical learning, covering topics such as linear models, regression, classification, and data mining. Provides a rigorous treatment of the underlying mathematical and statistical principles.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
An Introduction to Statistical Learning
Save
A comprehensive textbook that covers the fundamentals of statistical learning, including supervised and unsupervised learning, model selection, and regularization techniques. Provides a strong theoretical foundation for data science.
An Introduction to Statistical Learning: with...
Hardcover
$$$
Cover image
Reinforcement Learning, second edition
Save
A classic textbook in reinforcement learning, covering topics such as Markov decision processes, dynamic programming, and policy optimization. Provides a theoretical foundation for understanding and applying reinforcement learning techniques.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Data Science from Scratch
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Provides a practical introduction to data science, covering topics such as data cleaning, exploration, modeling, and visualization. Offers hands-on exercises and projects to reinforce learning.
Data Science from Scratch: First Principles with...
Paperback
$$
Data Science from Scratch: First Principles with...
Kindle Edition
$$
Data Science from Scratch: First Principles with...
Paperback
$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
A practical guide to machine learning using popular Python libraries. Covers topics such as data preprocessing, feature engineering, model training, and evaluation. Provides hands-on examples and exercises to enhance understanding.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Natural Language Processing with Python
Save
A comprehensive guide to natural language processing using Python, covering topics such as text preprocessing, text classification, and machine translation. Provides hands-on examples and exercises.
Natural Language Processing with Python: Analyzing...
Paperback
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(中文) Python Natural Language Processing(Chinese Edition)
Paperback
$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$
Cover image
Python Data Science Handbook
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A practical guide to supervised machine learning using Python, covering topics such as model selection, hyperparameter tuning, and ensemble methods. Provides hands-on examples and exercises.
Python Data Science Handbook: Essential Tools for...
Paperback
$$$
Cover image
Python for Data Analysis
Save
A comprehensive guide to Python for data analysis, covering topics such as data manipulation, visualization, and statistical modeling. Provides a solid foundation for data science programming.
Python for Data Analysis: Data Wrangling with...
Paperback
$$
Python for Data Analysis
Kindle Edition
$$
Python for Data Analysis: Data Wrangling with...
Paperback
$$$
Cover image
Data Visualization
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A beginner-friendly guide to data visualization, covering topics such as choosing appropriate charts, using color effectively, and designing impactful dashboards. Provides hands-on exercises to develop practical skills.
Data Visualization: A Successful Design Process: A...
Paperback
$
Data Visualization: Representing Information on...
Kindle Edition
$$$
Data Visualization Design and Utilization (Korean...
Paperback
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Cover image
Data Science for Business
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A business-oriented introduction to data science, covering topics such as data collection, analysis, and visualization. Provides insights into how data science can drive business decisions and improve outcomes.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
Big Data, Big Analytics
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Examines the challenges and opportunities of big data analytics, covering topics such as data storage, data processing, and data visualization. Provides insights into how big data can transform industries and drive business outcomes.
Big Data, Big Analytics: Emerging Business...
Hardcover
$$
Big Data, Big Analytics: Emerging Business...
Kindle Edition
$$
Cover image
Doing Data Science
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Discusses the impact of data science on business strategy and decision-making. Examines case studies and provides practical advice on how to leverage data to improve business performance.
Doing Data Science: Straight Talk from the Frontline
Paperback
$$
(中文) 数据科学实战 (图灵程序设计丛书) (Chinese Edition)
Kindle Edition
$
Cover image
Ethics and Data Science
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Examines the ethical implications of data science, covering topics such as bias, privacy, and the responsible use of data. Provides a framework for ethical decision-making in data science projects.
Ethics and Data Science
Kindle Edition
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Via
Udacity
Instructors
Dave Holtz
Cheng-Han Lee
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Taught by Dave Holtz, Cheng-Han Lee, who are recognized for their work in data science
Explores foundational data science topics, equipping learners for diverse applications
Provides a comprehensive overview for those seeking to sample and apply data science techniques
Introduces breadth of data science topics, allowing for exploration of interests
Prerequisites recommended, encouraging learners to enhance their foundational skills
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