Data Preparation and Analysis from Coursera

This course introduces the necessary concepts and common techniques for analyzing data. The primary emphasis is on the process of data analysis, including data preparation, descriptive analytics, model training, and result interpretation. The process starts with removing distractions and anomalies, followed by discovering insights, formulating propositions, validating evidence, and finally building professional-grade solutions. Following the process properly, regularly, and transparently brings credibility and increases the impact of the results.

This course will cover topics including Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. Besides, this course will review statistical theory, matrix algebra, and computational techniques as necessary.

This course prepares students ready for and capable of the data preparation and analysis process. Besides developing Python codes for carrying out the process, students will learn to tune the software tools for the most efficient implementation and optimal performance. At the end of this course, students will have built their inventory of data analysis codes and their confidence in advocating their propositions to the business stakeholders.

Required Textbook: This course does not mandate any textbooks because the lecture notes are self-contained.

Optional Materials: A Practitioner's Guide to Machine Learning (abbreviated PGML for Reading)

Software Requirements: Python version 3.11 or above with the latest compatible versions of NumPy, SciPy, Pandas, Scikit-learn, and Statsmodels libraries.

To succeed in this course, learners should possess a basic knowledge of linear algebra and statistics, basic set theory and probability theory, and have basic Python and SQL skills. A few courses that can help equip you with the database knowledge needed for this course are: Introduction to Relational Databases, Relational Database Design, and Relational Database Implementation and Applications.

What's inside

Syllabus

Module 1: Process of Preparing and Analyzing Data

Welcome to Data Preparation and Analysis! Module 1 guides students through the art of crafting informative and visually appealing histograms, a fundamental aspect of data visualization. Students will learn techniques for measuring the location and scale of data, understanding the origins and impacts of noise and missing values in datasets. This module also introduces the CRISP-DM Process, a structured approach to data mining, along with Gartner's Analytics Ascendancy Model for advanced data analysis. Additionally, students will explore the distinction between raw data and processed information, a key concept for effective data interpretation and decision-making.

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Taught by recognized experts in the field

Develops core data analysis skills for data preparation, descriptive analytics, model training, and result interpretation

Emphasizes practical applications of data analysis techniques

Covers a comprehensive range of data analysis topics, including EDA, feature screening, segmentation, association rules, nearest neighbors, clustering, decision trees, linear regression, logistic regression, and performance evaluation

Provides clear and detailed lecture notes that are self-contained

Requires a strong foundation in linear algebra, statistics, and Python programming

Reviews summary

Practical data analysis with python

According to learners, this course on neutralData Preparation and Analysisneutral is seen as a positivehighly practical and comprehensivepositive program for mastering essential techniques using neutralPythonneutral. Students frequently highlight the positiveengaging hands-on projects and labspositive as crucial for applying concepts to positivereal-world scenariospositive. While the positiveinstructor's explanationspositive are largely praised for clarity, some indicate that a warningstronger background in linear algebra and statisticswarning than specified is beneficial, as certain warningtheoretical sections can be dense or fast-pacedwarning. Recent feedback points to positivecontinuous improvements and updatespositive, making it a valuable asset for positivecareer-focused data professionalspositive.

Generally clear, though theoretical parts can be dense or fast-paced.

"The instructor clarified statistical concepts, making them very digestible."

"Some sections, particularly on advanced statistical theory, felt a bit rushed or could have used more hands-on examples beyond the basic Python implementations."

"I struggled with the pace in later modules (like Decision Trees and Performance Evaluation)."

"The lectures were not always clear, and the theoretical parts lacked sufficient examples."

Course quality has improved, reflecting instructor responsiveness.

"The latest reviews indicate improvements, and I definitely felt the quality was high."

"The materials were always up-to-date with current libraries."

"I believe the instructor has continuously refined the content based on past reviews."

Covers a wide array of essential data analysis and ML techniques.

"The content is incredibly well-structured, starting from foundational concepts like EDA and moving into more complex topics like various regression models..."

"A good overview of essential data analysis techniques. The CRISP-DM process introduction was a nice touch, giving a structured approach."

"Good course, covered a lot of ground in data analysis. The modules on regression and classification were detailed enough."

Highly relevant for professionals aiming for a data analysis career.

"This course is a must for anyone serious about a career in data analysis. I feel much more confident in handling real-world datasets and interpreting results for stakeholders."

"I loved the emphasis on interpreting results and communicating them effectively, which is crucial for my job."

"This course truly prepares you to be a data analyst, not just a coder."

Highly practical, with effective Python labs and real-world scenarios.

"The Python labs were incredibly helpful, allowing me to apply what I learned immediately."

"The hands-on projects were challenging but incredibly rewarding."

"The course does a great job bridging the gap between theory and practical application using Python."

"I gained practical tools and strategies that I could apply immediately to my work."

Minor inconsistencies in provided code snippets noted by some.

"I noticed a few minor inconsistencies in the provided code snippets in Module 3 that needed some debugging on my end."

"Occasionally, I had to spend time debugging minor errors in the course's provided code examples."

Requires a strong foundation in math, statistics, and programming.

"The course... assumes a stronger background in linear algebra and statistics than I possessed..."

"I struggled with the pace in later modules... it feels like it requires a very strong math background to truly grasp everything."

"Some of the theoretical explanations were hard to follow without prior advanced mathematical exposure."

"I found myself needing to consult external resources for a deeper dive into certain mathematical underpinnings."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Preparation and Analysis with these activities:

Review Intermediate Python and Statistics

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Recall the relevant materials on Python and Statistics that you already know to minimize the learning curve when working on the harder concepts in this course.

Browse courses on Python

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Review the Python syntax and semantics.
Explore various statistical concepts like mean, median, and variance.

Review of Introduction to Applied Linear Algebra by Gilbert Strang

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Supplement the matrix algebra concepts covered in this course with insights from a renowned author.

View Linear Algebra for Everyone on Amazon

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Review the concepts of matrices, vectors, and linear transformations.
Solve systems of linear equations and matrix equations.

Reinforce Your Understanding of CRISP-DM and Gartner's Analytics Ascendancy Model

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Become more proficient in the CRISP-DM process and Gartner's Analytics Ascendancy Model to enhance your data analysis and mining skills.

Browse courses on CRISP-DM

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Review the official CRISP-DM website and documentation.
Explore Gartner's website for resources on the Analytics Ascendancy Model.
Complete online tutorials or courses on CRISP-DM and the Analytics Ascendancy Model.

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Practice Exploratory Data Analysis Using Python

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Build your proficiency in EDA techniques by solving hands-on exercises and assignments.

Browse courses on Exploratory Data Analysis

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Load and explore a dataset using Python.
Plot histograms and scatterplots to visualize data.

Participate in Discussion Forums

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Engage with other students by sharing insights and asking questions on course topics.

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Join discussion forums.
Post questions and comments to initiate discussions.

Create a Data Analysis Project

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Develop a data analysis project from start to finish to practice the process of data preparation and analysis.

Browse courses on Data Analysis

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Choose a dataset
Clean and prepare the data
Explore the data
Build a model
Evaluate the model

Guided Tutorials on Machine Learning with Python

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Expand your understanding of different concepts and algorithms used in Machine Learning with the help of structured tutorials.

Browse courses on Machine Learning

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Explore tutorials on supervised and unsupervised learning algorithms.
Implement and practice these algorithms using Python code.

Strengthen Your Correlation Metrics Calculation Skills

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Improve your ability to calculate and interpret correlation metrics using Python, enhancing your data analysis capabilities.

Browse courses on Statistical Analysis

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Review the theory behind correlation metrics.
Practice calculating correlation metrics using Python code.
Apply correlation metrics to real-world datasets and interpret the results.

Develop a Blog Post Explaining Feature Importance in Machine Learning

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Enhance your understanding of feature importance techniques by explaining them to others.

Browse courses on Feature Importance

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Summarize the concept of feature importance.
Describe various methods for calculating feature importance.
Illustrate with examples and provide code snippets.

Participate in Kaggle Competitions

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Test and showcase your data analysis skills by participating in real-world competitions.

Browse courses on Machine Learning

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Join Kaggle and explore available competitions.
Select a competition that aligns with your interests and skill level.
Build and submit your models.

Build a Data Analysis Dashboard Using Dash

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Solidify your understanding of data visualization by creating an interactive dashboard.

Browse courses on Data Visualization

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Connect to a data source and extract relevant data.
Design and develop interactive visualizations using Dash.
Deploy the dashboard and share it with others.

Career center

Learners who complete Data Preparation and Analysis will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist is responsible for developing and deploying machine learning models to solve business problems. This course provides a strong foundation in the principles of data analysis and machine learning, including data preparation, descriptive analytics, model training, and result interpretation. Additionally, the course covers a variety of data analysis techniques, such as Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. These skills are essential for success in the field of data science.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer is responsible for developing and deploying machine learning models to solve business problems. This course provides a strong foundation in the principles of data analysis and machine learning, including data preparation, descriptive analytics, model training, and result interpretation. Additionally, the course covers a variety of data analysis techniques, such as Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. These skills are essential for success in the field of machine learning engineering.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst is responsible for analyzing data to uncover insights that can help businesses make better decisions. This course provides a strong foundation in the principles of data analysis, including data preparation, descriptive analytics, model training, and result interpretation. Additionally, the course covers a variety of data analysis techniques, such as Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. These skills are essential for success in the field of data analysis.

See salaries and explore the career path for Data Analyst

Statistician

A Statistician is responsible for collecting, analyzing, and interpreting data. This course provides a strong foundation in the principles of statistics, including data analysis, probability, and hypothesis testing. Additionally, the course covers a variety of statistical techniques, such as Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. These skills are essential for success in the field of statistics.

See salaries and explore the career path for Statistician

Risk Analyst

A Risk Analyst is responsible for identifying, assessing, and mitigating risks to businesses. This course provides a strong foundation in the principles of data analysis and risk management. Additionally, the course covers a variety of data analysis techniques, such as Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. These skills are essential for success in the field of risk management.

See salaries and explore the career path for Risk Analyst

Business Analyst

A Business Analyst is responsible for analyzing business problems and recommending solutions. This course provides a strong foundation in the principles of data analysis and business intelligence. Additionally, the course covers a variety of data analysis techniques, such as Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. These skills are essential for success in the field of business analysis.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

An Operations Research Analyst is responsible for developing and deploying mathematical models to solve business problems. This course provides a strong foundation in the principles of data analysis and operations research. Additionally, the course covers a variety of data analysis techniques, such as Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. These skills are essential for success in the field of operations research.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

A Quantitative Analyst is responsible for developing and deploying quantitative models to solve business problems. This course provides a strong foundation in the principles of data analysis and quantitative finance. Additionally, the course covers a variety of data analysis techniques, such as Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. These skills are essential for success in the field of quantitative finance.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

A Data Engineer is responsible for designing, building, and maintaining data pipelines. This course provides a strong foundation in the principles of data engineering, including data extraction, transformation, and loading. Additionally, the course covers a variety of data engineering techniques, such as data warehousing, data mining, and data visualization. These skills are essential for success in the field of data engineering.

See salaries and explore the career path for Data Engineer

Data Architect

A Data Architect is responsible for designing and implementing data management solutions. This course provides a strong foundation in the principles of data architecture, including data modeling, data governance, and data security. Additionally, the course covers a variety of data architecture techniques, such as data warehousing, data mining, and data visualization. These skills are essential for success in the field of data architecture.

See salaries and explore the career path for Data Architect

Database Administrator

A Database Administrator is responsible for managing and maintaining databases. This course provides a strong foundation in the principles of database administration, including database design, database optimization, and database security. Additionally, the course covers a variety of database administration techniques, such as data warehousing, data mining, and data visualization. These skills are essential for success in the field of database administration.

See salaries and explore the career path for Database Administrator

Software Engineer

A Software Engineer is responsible for designing, developing, and testing software. This course provides a strong foundation in the principles of software engineering, including software design, software development, and software testing. Additionally, the course covers a variety of software engineering techniques, such as data warehousing, data mining, and data visualization. These skills are essential for success in the field of software engineering.

See salaries and explore the career path for Software Engineer

Web Developer

A Web Developer is responsible for designing, developing, and maintaining websites. This course provides a strong foundation in the principles of web development, including web design, web development, and web testing. Additionally, the course covers a variety of web development techniques, such as data warehousing, data mining, and data visualization. These skills are essential for success in the field of web development.

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Mobile App Developer

A Mobile App Developer is responsible for designing, developing, and testing mobile apps. This course provides a strong foundation in the principles of mobile app development, including mobile app design, mobile app development, and mobile app testing. Additionally, the course covers a variety of mobile app development techniques, such as data warehousing, data mining, and data visualization. These skills are essential for success in the field of mobile app development.

See salaries and explore the career path for Mobile App Developer

Data Visualization Specialist

A Data Visualization Specialist is responsible for designing and developing data visualizations. This course provides a strong foundation in the principles of data visualization, including data visualization design, data visualization development, and data visualization testing. Additionally, the course covers a variety of data visualization techniques, such as data warehousing, data mining, and data visualization. These skills are essential for success in the field of data visualization.

See salaries and explore the career path for Data Visualization Specialist