Data Science Fundamentals

Data Science Fundamentals, available at $19.99, has an average rating of 4.44, with 13 lectures, based on 9 reviews, and has 468 subscribers.

You will learn about Learn how to understand Data Science and Concepts Understand the pillars for Data Science Identify the cloud components and Data Science components Learn the architecture for Data Science in the Enterprise This course is ideal for individuals who are Marketing, Operations, Infrastructure Teams, Sales, Data Teams It is particularly useful for Marketing, Operations, Infrastructure Teams, Sales, Data Teams.

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Summary

Title: Data Science Fundamentals

Price: $19.99

Average Rating: 4.44

Number of Lectures: 13

Number of Published Lectures: 13

Number of Curriculum Items: 13

Number of Published Curriculum Objects: 13

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to understand Data Science and Concepts

Understand the pillars for Data Science

Identify the cloud components and Data Science components

Learn the architecture for Data Science in the Enterprise

Who Should Attend

Marketing, Operations, Infrastructure Teams, Sales, Data Teams

Target Audiences

Marketing, Operations, Infrastructure Teams, Sales, Data Teams

On this training we are going to explore and understand the concepts and details of Data Science. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

Data science is a “concept to unify statistics, data analysis, informatics, and their related methods” in order to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.

We also are going to take a peek at the Big Data Concepts and details of Big Data. A data scientist is someone who creates programming code, and combines it with statistical knowledge to create insights from data.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (columns) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.

Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on”. Scientists, business executives, medical practitioners, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, healthcare analytics, geographic information systems, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology, and environmental research.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Overview of Data Science

Lecture 1: Overview of Data Science

Chapter 3: How to become a Data Scientist

Lecture 1: How to become a Data Scientist

Chapter 4: Data Science Skills

Lecture 1: Data Science Skills

Chapter 5: Applications of Data Science

Lecture 1: Applications of Data Science

Chapter 6: The 5 Vs of Big Data

Lecture 1: The 5 Vs of Big Data

Chapter 7: Understanding Data Science and Database Concepts

Lecture 1: Understanding Data Science and Database Concepts

Chapter 8: Data Science and Business Intelligence

Lecture 1: Data Science and Business Intelligence

Chapter 9: Data Science Landscape

Lecture 1: Data Science Landscape

Chapter 10: Example Data Science to Predict Application force alerts for early warnings.

Lecture 1: Example Data Science to Predict Application force alerts for early warnings.

Chapter 11: Data Science and Decision Making for the Enterprise

Lecture 1: Data Science and Decision Making for the Enterprise

Chapter 12: Business Intelligence and Data Science

Lecture 1: Business Intelligence and Data Science

Chapter 13: Understanding Data Science and Data Warehouse

Lecture 1: Understanding Data Science and Data Warehouse

Instructors

Juan Sebastian Garcia
CHFI – CCNA – ENA – ACE

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3 stars: 1 votes

4 stars: 4 votes

5 stars: 4 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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