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Dave Holtz
and
Cheng-Han Lee
The Introduction to Data Science class will survey the foundational topics in data science, namely:
Read more
The Introduction to Data Science class will survey the foundational topics in data science, namely:
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/ve1ltk/udacity
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Good to know
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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
Save this course
Save Intro to Data Science to your list so you can find it easily later:
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Reviews summary
Wide-ranging intro to data science
This course introduces learners to a wide range of concepts related to data science, including preparing data, using Python to analyze data, and visualizing data. The course gathers positive reviews from learners who appreciate the wide-breadth of the course, but it also gets feedback that it does not go into enough depth on any one topic. Although the course assumes that learners have basic programming and statistics knowledge, it is part of Udacity's Data Analyst Nanodegree program and thus is likely appropriate for adult learners who may be starting a new career as data analysts.
Exposure to Python packages in scipy stack
"One of the best parts about this course is getting some exposure to some Python packages in the scipy stack"
Assumes basic Python and statistics knowledge
"Intro to data science is an intermediate level course that assumes basic Python programming skills and knowledge of statistics."
Wide-ranging course
"The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth."
"It brings introduction in many areas, but it does not go into depth to any area."
Over-reliance on online grader
"Though the course uses interesting examples for teaching concepts in relation to data science, the over reliance of the online grader for practice often makes learning redundant. Big part of learning programming is experimentation which the grader does not allow for."
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Explore data science concepts
Show steps
Gain a foundational understanding of data science concepts and their applications.
View
Data Science para Negócios
on Amazon
Show steps
Show steps
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
Show steps
Build a solid foundation in mathematical concepts crucial for understanding data science techniques.
View
Mathematics for Machine Learning
on Amazon
Show steps
Show steps
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
Show steps
Ensure proficiency in programming, a fundamental skill for data science.
Browse courses on
Programming
Show steps
Show steps
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
Show steps
Enhance understanding and retention by engaging in discussions and sharing knowledge with peers.
Show steps
Show steps
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
Show steps
Foster critical thinking and problem-solving skills by creating a plan for a data science project.
Browse courses on
Data Science Project
Show steps
Show steps
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
Show steps
Develop proficiency in data analysis and visualization, essential skills for exploring and presenting insights.
Browse courses on
Data Analysis
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Tailor your learning journey by creating a personalized plan that aligns with your goals and interests.
Browse courses on
Personalized Learning
Show steps
Show steps
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 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.
Data Analyst
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.
Machine Learning Engineer
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.
Data 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.
Statistician
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.
Database Administrator
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.
Business Analyst
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.
Risk Analyst
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.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations.
Data Architect
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.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect an organization's data and information systems from unauthorized access, use, disclosure, disruption, modification, or destruction.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze market data to help businesses develop and execute marketing campaigns.
Financial Analyst
Financial Analyst
Financial Analysts provide research and analysis on financial markets and companies to help investors make informed investment decisions.
Software Engineer
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.
Operations Research Analyst
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.
For more career information including salaries, visit:
OpenCourser.com/course/ve1ltk/udacity
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.
Save
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.
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.
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.
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.
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.
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.
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.
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.
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.
Save
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.
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.
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.
Save
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ve1ltk/udacity
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Instructors
Dave Holtz
Cheng-Han Lee
Good to know
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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