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Ivan Vega,
Joseph Lozada,
and
Daniel Roca
Gain new skills & unlock the power of Predictive Data Analysis with Udacity's online training course. Master the techniques used by data professionals today.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Data transformation
- Basic descriptive statistics
- Data visualization
- Power BI Proficiency
- Chart types
- Data wrangling
- Elementary algebra
Read more
Gain new skills & unlock the power of Predictive Data Analysis with Udacity's online training course. Master the techniques used by data professionals today.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Data transformation
- Basic descriptive statistics
- Data visualization
- Power BI Proficiency
- Chart types
- Data wrangling
- Elementary algebra
Read more
Gain new skills & unlock the power of Predictive Data Analysis with Udacity's online training course. Master the techniques used by data professionals today.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Data transformation
- Basic descriptive statistics
- Data visualization
- Power BI Proficiency
- Chart types
- Data wrangling
- Elementary algebra
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/7h1x2z/predictive
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What's inside
Syllabus
This lesson will introduce predictive analytics concepts and a general overview of when to use it in your organization. We will also review the project and the primary tool we will use: Power BI.
Read more
Syllabus
This lesson will introduce predictive analytics concepts and a general overview of when to use it in your organization. We will also review the project and the primary tool we will use: Power BI.
Read more
We will review one Machine Learning technique that involves numeric data: Regression. We will learn about its main features, how to interpret it and how to construct it using Power BI.
We will learn additional ML techniques: Classification and Clustering. We will review its main features, some clustering techniques, how to interpret them and how to construct clusters with Power BI.
We will review what a time-series is. Also, we will review deterministic and probabilistic forecasting methods, how to read and interpret time-series and how to create forecasts using Power BI.
We will review additional ML and AI-powered tools using Power BI: we will focus on Power BI dataflows and its AutoML features and use two AI-powered visuals: the Q&A and the smart narratives.
In this project, students will use the data of past flights departing from the State of New York to analyze and give recommendations about flight delays.
In this lesson, you'll learn how to set up a Windows desktop workspace to use Power BI
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills that directly translate into career progression in data science
Course content is relevant for data professionals, analysts, and data scientists alike
Taught by experts in Predictive Data Analysis who are recognized for their work in the field
Part of a larger data science course collection offered by Udacity
Save this course
Save Predictive Data Analysis to your list so you can find it easily later:
Save
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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 Predictive Data Analysis with these
activities:
Create a Course Study Guide
Show steps
Organize and summarize key concepts to enhance retention and understanding.
Show steps
-
Review course materials
-
Identify important topics and concepts
-
Create summaries and notes
Review Statistics
Show steps
Review statistics concepts for a better understanding of the course materials.
Browse courses on
Descriptive Statistics
Show steps
-
Read a textbook chapter on statistics
-
Complete practice problems
-
Take a practice test
Participate in Power BI workshops
Show steps
Attend workshops to develop hands-on skills and industry best practices.
Show steps
-
Find Power BI workshops
-
Register and attend workshops
-
Practice and implement learned techniques
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Power BI
Show steps
Practice using Power BI to reinforce the concepts learned in the course.
Show steps
-
Download and install Power BI
-
Complete tutorials on Power BI
-
Practice creating visualizations and reports
Learn Time Series Forecasting
Show steps
Explore time series forecasting techniques to enhance understanding of the course materials.
Browse courses on
Time Series Analysis
Show steps
-
Find tutorials on time series forecasting
-
Follow the tutorials and complete exercises
-
Apply the techniques to real-world data
Create a Predictive Analytics Report
Show steps
Create a comprehensive report that includes data analysis, visualization, and recommendations.
Show steps
-
Gather and clean data
-
Perform data analysis and modeling
-
Create visualizations and dashboards
-
Develop recommendations and insights
Create a Course Study Guide
Show steps
Organize and summarize key concepts to enhance retention and understanding.
Show steps
Show steps
Show steps
- Review course materials
- Identify important topics and concepts
- Create summaries and notes
Review Statistics
Show steps
Review statistics concepts for a better understanding of the course materials.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Read a textbook chapter on statistics
- Complete practice problems
- Take a practice test
Participate in Power BI workshops
Show steps
Attend workshops to develop hands-on skills and industry best practices.
Show steps
Show steps
Show steps
- Find Power BI workshops
- Register and attend workshops
- Practice and implement learned techniques
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Power BI
Show steps
Practice using Power BI to reinforce the concepts learned in the course.
Show steps
Show steps
Show steps
- Download and install Power BI
- Complete tutorials on Power BI
- Practice creating visualizations and reports
Learn Time Series Forecasting
Show steps
Explore time series forecasting techniques to enhance understanding of the course materials.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Find tutorials on time series forecasting
- Follow the tutorials and complete exercises
- Apply the techniques to real-world data
Create a Predictive Analytics Report
Show steps
Create a comprehensive report that includes data analysis, visualization, and recommendations.
Show steps
Show steps
Show steps
- Gather and clean data
- Perform data analysis and modeling
- Create visualizations and dashboards
- Develop recommendations and insights
Career center
Learners who complete Predictive Data Analysis will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists research and develop new and innovative data analysis techniques that can be used to solve real-world problems. Predictive Data Analysis (PDA) is a subfield of data science that focuses on developing models that can predict future events. Studying PDA can open up a rewarding career as a Data Scientist. This course will help you master the techniques used by Data Scientists today, including data transformation, descriptive statistics, data visualization, and machine learning algorithms like regression and classification.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building, deploying, and maintaining machine learning models. PDA is a fundamental skill for Machine Learning Engineers, as it allows them to develop models that can predict future events and make decisions. Studying PDA can help you gain the skills you need to succeed as a Machine Learning Engineer. This course will help you develop a solid foundation in PDA.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems and identify opportunities. The ability to predict future events is a valuable skill for Data Analysts to have. By studying PDA, you can open up doors to new opportunities for you as a Data Analyst. PDAs can be a cornerstone in building effective solutions to address business problems.
Statistician
Statistician
Statisticians use data to understand the world around us. Probability, an essential subfield of statistics, is a key concept behind predictive modeling. PDA is a subfield of statistics that focuses on developing models that can predict future events. This course will help you become more familiar with PDA and enhance your expertise as a Statistician.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. The ability to predict future events can give Financial Analysts a leg up in the competitive world of finance. This course will help you gain the skills you need to succeed as a Financial Analyst.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. PDA can help Business Analysts gain insights into future trends. This course will help you develop the skills you need to succeed as a Business Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to optimize business processes. The ability to predict future events can help Operations Research Analysts make better decisions that improve efficiency.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that stores and processes data. PDA is a valuable skill for Data Engineers to have, as it allows them to design systems that can handle large volumes of data and quickly generate predictions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. PDA is a valuable skill for Software Engineers to have, as it allows them to develop applications that can make predictions and learn from data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. PDA is a valuable skill for Quantitative Analysts to have, as it allows them to develop models that can predict future market movements.
Actuary
Actuary
Actuaries use data to assess risk and determine insurance premiums. PDA is a valuable skill for Actuaries to have, as it allows them to develop models that can predict future events and assess risk.
Epidemiologist
Epidemiologist
Epidemiologists use data to study the distribution and determinants of health-related states or events in specified populations. Time-series analysis, a key component of PDA, is widely used in the field of epidemiology to estimate trends and predict future outbreaks.
Market Research Analyst
Market Research Analyst
Market Research Analysts use data to understand consumer behavior and trends. PDA is a valuable skill for Market Research Analysts to have, as it allows them to develop models that can predict future demand.
Risk Manager
Risk Manager
Risk Managers use data to identify and assess risks. PDA is a valuable skill for Risk Managers to have, as it allows them to develop models that can predict future events and assess risk.
Insurance Underwriter
Insurance Underwriter
Insurance Underwriters use data to assess risk and determine insurance premiums. PDA is a valuable skill for Insurance Underwriters to have, as it allows them to develop models that can predict future events and assess risk.
For more career information including salaries, visit:
OpenCourser.com/course/7h1x2z/predictive
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
Predictive Data Analysis.
Provides a comprehensive overview of machine learning techniques, with a focus on R programming. It valuable resource for students who want to learn more about the practical applications of machine learning.
Provides a comprehensive overview of machine learning techniques, with a focus on Python programming. It valuable resource for students who want to learn more about the practical applications of machine learning.
Provides a comprehensive overview of deep learning techniques, with a focus on Python programming. It valuable resource for students who want to learn more about the practical applications of deep learning.
Provides a comprehensive overview of statistical learning techniques. It valuable resource for students who want to learn more about the theoretical foundations of statistical learning, as well as the practical applications of statistical learning.
Save
Provides a comprehensive overview of deep learning techniques. It valuable resource for students who want to learn more about the theoretical foundations of deep learning, as well as the practical applications of deep learning.
Provides a practical introduction to machine learning techniques, with a focus on Python programming. It valuable resource for students who want to learn more about the practical applications of machine learning.
Save
Provides a comprehensive overview of data mining techniques. It valuable resource for students who want to learn more about the theoretical foundations of data mining, as well as the practical applications of data mining.
Save
Provides a comprehensive overview of the big data revolution, and how it is changing the way we live, work, and think. It valuable resource for students who want to learn more about the impact of big data on society.
Provides a comprehensive overview of the Fourth Industrial Revolution, and how it is changing the way we live, work, and think. It valuable resource for students who want to learn more about the impact of the Fourth Industrial Revolution on society.
Provides a comprehensive overview of pattern recognition and machine learning techniques. It valuable resource for students who want to learn more about the theoretical foundations of machine learning.
Provides a business-oriented introduction to data science, and will help students understand how to use data to make better decisions. It is written in a clear and engaging style, and is packed with real-world examples.
Provides a comprehensive overview of reinforcement learning techniques. It valuable resource for students who want to learn more about the theoretical foundations of reinforcement learning.
Provides a comprehensive overview of Bayesian reasoning and machine learning techniques. It valuable resource for students who want to learn more about the theoretical foundations of machine learning.
Good introduction to the topic of predictive analytics, and will provide students with the foundational knowledge they need to succeed in this course. It is written in a clear and concise style, and is packed with real-world examples.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7h1x2z/predictive
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Effort
4 weeks
Level
Introductory
Via
Udacity
Instructors
Ivan Vega
Joseph Lozada
Daniel Roca
Language
English
Good to know
Develops skills that directly translate into career progression in data science
Course content is relevant for data professionals, analysts, and data scientists alike
Taught by experts in Predictive Data Analysis who are recognized for their work in the field
Part of a larger data science course collection offered by Udacity
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