Fundamentals of Data Analysis for Big Data faq

star-rating
4.1
learnersLearners: 1,300
instructor Instructor: / instructor-icon
duration Duration: duration-icon

This course provides an introduction to the fundamentals of data analysis for big data. Learn how to run basic univariable analysis, simple regression analysis, instrumental variables analysis, probit/logit analysis, fixed effects regression, and difference-in-differences analysis on a dataset. Gain the skills to analyze and interpret data to make informed decisions.

ADVERTISEMENT

Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Paid

providerProvider:

Udemy

certificateCertificate:

No Information

languageLanguage:

English

start dateStart Date:

Self Paced

Course Overview

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

Updated in [June 30th, 2023]

This course provides an introduction to the fundamentals of data analysis for big data. Students will learn how to run basic univariable analysis of means, medians, and percentiles on a dataset, as well as how to run simple regression analysis, instrumental variables analysis, probit/logit analysis, fixed effects regression, and difference-in-differences analysis on a dataset. By the end of the course, students will have a better understanding of the various methods of data analysis and how to apply them to their own datasets.

[Applications]
The application of the Fundamentals of Data Analysis for Big Data course can be used to gain a better understanding of the data analysis process. It can be used to gain insights into the relationships between variables, as well as to identify potential areas of improvement. Additionally, the course can be used to develop skills in running basic univariable analysis of means, medians, and percentiles, as well as more advanced techniques such as regression analysis, instrumental variables analysis, probit/logit analysis, fixed effects regression, and difference-in-differences analysis. These skills can be applied to a variety of data analysis tasks, such as forecasting, predictive modeling, and optimization.

[Career Path]
One job position path that is recommended for learners of this course is a Data Analyst. A Data Analyst is responsible for collecting, organizing, and analyzing data to help inform business decisions. They use a variety of tools and techniques to identify trends, patterns, and relationships in data sets. They also create reports and visualizations to communicate their findings to stakeholders.

The development trend for Data Analysts is to become more specialized in their field. As the amount of data available to businesses continues to grow, Data Analysts are expected to become more knowledgeable in specific areas such as machine learning, artificial intelligence, and predictive analytics. They will also need to be able to use more advanced tools and techniques to analyze data and draw meaningful insights. Additionally, Data Analysts will need to be able to communicate their findings in a clear and concise manner to stakeholders.

[Education Path]
The recommended educational path for learners of this course is a degree in Data Science or Big Data Analytics. This degree typically involves courses in mathematics, statistics, computer science, and data analysis. Students will learn how to use data to solve problems, develop algorithms, and create models. They will also learn how to interpret and visualize data, as well as how to use data to make decisions.

The development trend of this degree is to focus on the application of data science and analytics to real-world problems. This includes the use of machine learning, artificial intelligence, and natural language processing to analyze large datasets. Students will also learn how to use data to create predictive models and develop strategies for decision-making. Additionally, they will learn how to use data to create visualizations and communicate insights.

Pros & Cons

Pros Cons
  • pros

    Provides better understanding of statistics in simple terms.

  • pros

    Logical and direct explanation of each term.

  • pros

    Instructor is knowledgeable and speaks well.

  • cons

    Assumes strong understanding of statistics for advanced analysis.

  • cons

    Lack of real-life examples and exercises.

  • cons

    Course should be more Big Data intensive.

Course Provider

Provider Udemy's Stats at AZClass

Discussion and Reviews

0.0   (Based on 0 reviews)

Start your review of Fundamentals of Data Analysis for Big Data

faq FAQ for Big Data Courses

Q1: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q2: How many people have enrolled in this course?

So far, a total of 1300 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q3: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
Watch the video preview to understand the course content.
(Please note that the following steps should be performed on Udemy's official site.)
Find the course description and syllabus for detailed information.
Explore teacher profiles and student reviews.
Add your desired course to your cart.
If you don't have an account yet, sign up while in the cart, and you can start the course immediately.
Once in the cart, select the course you want and click "Enroll."
Udemy may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Big Data courses and certifications, our extensive collection at azclass.net will help you.

close

To provide you with the best possible user experience, we use cookies. By clicking 'accept', you consent to the use of cookies in accordance with our Privacy Policy.