Data Analysis Foundation Courseware.

Format:

Book

Author/Creator:

o., Van Haren Learning Solutions a.

Language:

English

Physical Description:

1 online resource (137 pages)

Edition:

1st ed.

Place of Publication:

Hertogenbosch : Van Haren Publishing, 2024.

Summary:

#html-body [data-pb-style=ETNQQNT]{justify-content: flex-start;display: flex;flex-direction: column;background-position: left top;background-size: cover;background-repeat: no-repeat;background-attachment: scroll}This Data Analysis Foundation Courseware enable a Data Analysis Foundation course, providing a comprehensive introduction to data concepts and the essence of data analysis, subsequently delving into the fundamental principles of Data Analysis, such as statistics and probability. Moreover, this course concentrates on extensively used data analysis techniques like regression and a step-by-step guide to executing them. Data Analysis refers to the systematic application of statistical and mathematical techniques to gain insights, explore, and scrutinize data in order to identify patterns and draw meaningful conclusions that assist businesses in making informed decisions based on data. This process typically encompasses various stages, including data collection, exploration, cleansing, pre-processing, and data organization. Frequently, data analysis is an ongoing, iterative procedure wherein data is continuously collected and analyzed simultaneously. There are two primary approaches to data analysis. Quantitative techniques involve working with numerical data and employ statistical measures, percentages, and calculations. These methods utilize algorithms, mathematical analysis tools, and software to manipulate data and reveal concealed business value. For instance, quantitative data analysis is employed to evaluate market data, aiding a company in determining an appropriate price for a new product. Qualitative data analysis entails working with non-numerical data, specifically categorical variables. Qualitative data analysis is also applicable in various business processes, including identifying themes and patterns and addressing research inquiries, aiming to enhance a product.

Contents:

Intro

Self-Reflection

Timetable

Data Analysis Deep Dive

Section 1: Introduction to Data

Data in practical applications

Formats and sources of data

The 7 Vs of big data

Structured, semi-structured, and unstructured data

Data Processing Techniques

Section 2: Fundamentals of Data Analysis

Importance of Data Analysis

Application of data analysis

The process of analyzing data

Numerical data

Categorical data

Section 3: Descriptive Data &amp

Statistics

Descriptive statistics in data analysis

Frequency

Measures of central tendency

Measures of dispersion

Data skewness and kurtosis

Outliers

Missing values

Section 4: Probability

Probability overview

Axioms of probability

Conditional probability and Bayes' theorem

Section 5: Probability Distributions

Discrete probability distributions

Continuous probability distributions

Performing distributions in Excel

Importance of data distribution for data analysis

Section 6: Executing Data Analysis

Covariance and correlation

Univariate, bivariate, and multivariate analysis

Linear regression

Simple linear regression

Multiple linear regression

Knowledge Check Answers

Sample exam

Syllabus.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789401810715

9401810710