Foundations of Data Science and Statistical Methods
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Foundations of Data Science and Statistical Methods
This course is part of CompTIA DataX Study Guide Specialization
Instructor: Wiley Skills Network
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What you'll learn
Understand the fundamental concepts, workflows, and real-world applications of data science.
Apply basic statistical methods to analyze and interpret datasets effectively.
Implement data collection, ingestion, and storage techniques for structured data management.
Skills you'll gain
- Data Analysis
- Statistical Analysis
- Probability Distribution
- Machine Learning
- Data Science
- Linear Algebra
- Data Collection
- Statistics
- Data Quality
- Statistical Methods
- Probability & Statistics
- Statistical Machine Learning
- Data Storage
- Data Management
- Exploratory Data Analysis
- Data Pipelines
- Data-Driven Decision-Making
Details to know
April 2026
4 assignments
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There are 4 modules in this course
In this course, you will gain a comprehensive understanding of data science and its key statistical methods. Data science plays a crucial role in making data-driven decisions in today's business, technology, and research environments.
You will develop essential skills such as data exploration, collection, and statistical analysis, helping you draw meaningful insights from datasets. By applying these skills, you will be well-equipped to handle complex data challenges in real-world scenarios. This course combines theoretical concepts with practical applications to ensure learners are prepared for hands-on data analysis tasks. Through practical exercises, you'll be able to apply these methods to solve real-world data problems effectively. Ideal for individuals new to the field, this course will benefit aspiring data scientists, analysts, and anyone interested in understanding the power of data. No prior experience is required, making it accessible to those starting their data science journey. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. From CompTIA DataX Study Guide Copyright Β© 2024 by John Wiley & Sons, Inc. All rights, including for text and data mining, AI training, and similar technologies, are reserved. Used by arrangement with John Wiley & Sons, Inc.
In this section, we define data science, differentiate it from machine learning (ML) and artificial intelligence (AI), examine key real-world applications, and discuss best practices throughout the data science life cycle.
What's included
1 video6 readings1 assignment
1 videoβ’Total 1 minute
- What Is Data Science? - Overview Videoβ’1 minute
6 readingsβ’Total 80 minutes
- Introductionβ’10 minutes
- Other Specialized Approachesβ’15 minutes
- Signal Processingβ’15 minutes
- Data Understandingβ’15 minutes
- Common Tools and Techniquesβ’15 minutes
- Exam Essentialsβ’10 minutes
1 assignmentβ’Total 15 minutes
- Understanding Core Data Science Principlesβ’15 minutes
In this section, we cover essential mathematics for data science, including calculus, probability distributions, inferential statistics, and linear algebra, highlighting their roles in data analysis, modeling, and algorithm development.
What's included
1 video11 readings1 assignment
1 videoβ’Total 1 minute
- Mathematics and Statistical Methods - Overview Videoβ’1 minute
11 readingsβ’Total 160 minutes
- Introductionβ’15 minutes
- The Chain Ruleβ’15 minutes
- Probability Distributionsβ’15 minutes
- Skewness and Kurtosisβ’15 minutes
- Inferential Statisticsβ’10 minutes
- Hypothesis Testingβ’10 minutes
- Paired-Samples T-Testβ’10 minutes
- Real World Scenario: Investigating Diet Preferencesβ’30 minutes
- Vector Operationsβ’15 minutes
- Matrix Operationsβ’15 minutes
- Exam Essentialsβ’10 minutes
1 assignmentβ’Total 10 minutes
- Foundations of Mathematics and Statisticsβ’10 minutes
In this section, we identify common data sources, explain data acquisition and ingestion methods such as batch and streaming, and evaluate structured and unstructured storage solutions for effective data management and analytics.
What's included
1 video8 readings1 assignment
1 videoβ’Total 1 minute
- Data Collection and Storage - Overview Videoβ’1 minute
8 readingsβ’Total 80 minutes
- Introductionβ’10 minutes
- Real World Scenarioβ’10 minutes
- Synthetic Dataβ’10 minutes
- Common Licensing Typesβ’10 minutes
- Loadβ’10 minutes
- HDF5β’10 minutes
- Compressed Formatsβ’10 minutes
- Summaryβ’10 minutes
1 assignmentβ’Total 10 minutes
- Data Handling and Management Fundamentalsβ’10 minutes
In this section, we explore Exploratory Data Analysis (EDA) techniques for various variable types, interpret common data visualizations, and address frequent data quality issues critical for robust data science analysis.
What's included
1 video8 readings1 assignment
1 videoβ’Total 1 minute
- Data Exploration and Analysis - Overview Videoβ’1 minute
8 readingsβ’Total 85 minutes
- Introductionβ’10 minutes
- Histogramβ’10 minutes
- Bar Chartβ’10 minutes
- Violin Plotβ’10 minutes
- Principal Component Analysisβ’10 minutes
- Nonstationarityβ’15 minutes
- Sparse Dataβ’10 minutes
- Exam Essentialsβ’10 minutes
1 assignmentβ’Total 10 minutes
- Data Exploration and Analysis Fundamentalsβ’10 minutes
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