Combining and Analyzing Complex Data

Course Feature
  • Cost
    Free
  • Provider
    Coursera
  • Certificate
    Paid Certification
  • Language
    English
  • Start Date
    3rd Jul, 2023
  • Learners
    No Information
  • Duration
    No Information
  • Instructor
    Richard Valliant, Ph.D.
Next Course
2.5
0 Ratings
This course will teach you how to combine and analyze complex data using survey weights, linear and logistic regressions, record linkage, and statistical matching. You will learn how to use R® software to estimate descriptive statistics and model parameters. Additionally, the course will cover ethical issues related to combining datasets, such as obtaining informed consent from individuals. By the end of the course, you will have the skills to effectively analyze complex data.
Show All
Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [May 25th, 2023]

This course is ideal for those who want to learn how to combine and analyze complex data. It will provide learners with the skills to use survey weights to estimate descriptive statistics, such as means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Learners will also gain an understanding of the basics of record linkage and statistical matching, as well as the ethical considerations that come with combining datasets. The course will also cover the use of R® software for data analysis.

By the end of the course, learners will have a comprehensive understanding of how to combine and analyze complex data. They will be able to use survey weights to estimate descriptive statistics, as well as model parameters for linear and logistic regressions. They will also be able to use R® software for data analysis, and understand the ethical considerations that come with combining datasets. Additionally, learners will have a good understanding of the basics of record linkage and statistical matching.

[Applications]
After this course, participants can apply their knowledge to combine and analyze complex data. They can use survey weights to estimate descriptive statistics, such as means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Participants can also use the software capabilities covered in the course, such as R®, to analyze data. Additionally, participants can use the basics of record linkage and statistical matching to combine data from different sources. Finally, participants can use the ethical considerations discussed in the course to ensure that informed consent is obtained from persons to allow their data to be linked, and to understand the legal requirements in different countries.

[Career Paths]
1. Data Analyst: Data Analysts are responsible for collecting, organizing, and analyzing data from various sources. They use statistical methods to interpret data and create reports and visualizations to present their findings. They must be able to identify trends and patterns in data and use their findings to make recommendations to their organization. Data Analysts are in high demand as organizations increasingly rely on data-driven decision making.

2. Data Scientist: Data Scientists are responsible for developing and implementing algorithms and models to analyze large datasets. They use machine learning and artificial intelligence to uncover insights from data and develop predictive models. Data Scientists must have a strong understanding of mathematics, statistics, and computer science, as well as the ability to communicate their findings to stakeholders.

3. Business Intelligence Analyst: Business Intelligence Analysts are responsible for collecting, organizing, and analyzing data from various sources to help organizations make better decisions. They use data mining, data warehousing, and other techniques to uncover insights from data and create reports and visualizations to present their findings. Business Intelligence Analysts must have strong analytical and problem-solving skills, as well as the ability to communicate their findings to stakeholders.

4. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines and data warehouses. They must have a strong understanding of databases, data structures, and software engineering principles. Data Engineers must be able to design and implement efficient data architectures that can scale to meet the needs of their organization.

[Education Paths]
1. Bachelor of Science in Data Science: This degree path focuses on the development of skills in data analysis, data mining, and machine learning. Students will learn to use various software tools to analyze and interpret data, as well as develop strategies for data-driven decision making. This degree path is becoming increasingly popular as businesses and organizations rely more heavily on data-driven insights.

2. Master of Science in Business Analytics: This degree path focuses on the application of data analysis and predictive modeling to business problems. Students will learn to use data to identify trends, develop strategies, and make decisions. This degree path is becoming increasingly popular as businesses and organizations rely more heavily on data-driven insights.

3. Doctor of Philosophy in Data Science: This degree path focuses on the development of advanced skills in data analysis, data mining, and machine learning. Students will learn to use various software tools to analyze and interpret data, as well as develop strategies for data-driven decision making. This degree path is becoming increasingly popular as businesses and organizations rely more heavily on data-driven insights.

4. Master of Science in Data Analytics: This degree path focuses on the application of data analysis and predictive modeling to business problems. Students will learn to use data to identify trends, develop strategies, and make decisions. This degree path is becoming increasingly popular as businesses and organizations rely more heavily on data-driven insights.

Show All
Pros & Cons
  • Clear explanations
  • Practical examples
  • Excellent teachers
  • Good instructional material
  • Poor instructional material
  • Lack of clarity
  • No enough guides
Show All
Recommended Courses
free statistical-predictive-modelling-and-applications-16222
Statistical Predictive Modelling and Applications
1.5
Edx 21 learners
Learn More
This course provides an introduction to statistical predictive modelling and its applications. You will learn linear and logistic regression, as well as naive Bayes, and how to apply them to real-world scenarios. Through online learning and skill training, you will gain the knowledge and skills to use these techniques to predict outcomes and make decisions.
free introduction-to-r-software-16223
Introduction To R Software
1.5
Swayam 42 learners
Learn More
This course introduces students to the R software, a free software used for mathematical and statistical manipulations. It covers the basics of the R programming language and its built-in functions. Lectures and online learning will help students gain the skills needed to use the software for data analysis, simulations, and programming. The course is intended for UG students of Science and Engineering, students of humanities with basic mathematical background, and working professionals in analytics. A mathematics background up to class 12 is recommended. All industries involved in mathematical and statistical computations, programming, and simulations will benefit from this course.
free anova-and-experimental-design-16224
ANOVA and Experimental Design
1.5
Coursera 0 learners
Learn More
This course introduces students to the analysis of variance (ANOVA) and experimental design. Students will learn about linear regression models, randomization, blocking, factorial design, and causality. The course is part of the Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. This interdisciplinary degree is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
free data-science-inference-and-modeling-16225
Data Science: Inference and Modeling
4.0
Edx 1,009 learners
Learn More
This course will teach you the fundamentals of data science, including statistical inference and modeling, through a motivating case study on election forecasting. You will learn how to use R to define estimates and margins of errors, understand confidence intervals and p-values, and apply Bayesian modeling. At the end of the course, you will be able to recreate a simplified version of an election forecast model and apply it to the 2016 election.
Favorites (0)
Favorites
0 favorite option

You have no favorites

Name delet
arrow Click Allow to get free Combining and Analyzing Complex Data courses!