Python Data Science: Data Prep & EDA with Python
Learn Python + Pandas for data cleaning, profiling & EDA, and prep data for machine learning & data science with Python
Lo que serás capaz de hacer
This is a hands-on, project-based course designed to help you master the core building blocks of Python for data science and machine learning . We'll start by introducing the fields of data science and machine learning, discussing the difference between supervised and unsupervised learning, and reviewing the Python data science workflow we'll be using throughout the course. From there we'll do a deep dive into the data prep & EDA steps of the workflow. You'll learn how to scope a data science project, use Python and Pandas to gather data from multiple sources and handle common data cleaning issues, and perform exploratory data analysis (EDA) using techniques like filtering, grouping, and visualizing data. Throughout the course, you'll play the role of a Jr. Data Scientist for Maven Music , a streaming service that’s been struggling with customer churn. Using the skills you learn throughout the course, you'll use Python to gather, clean, and explore the data to provide insights about their customers. Last but not least, you'll practice preparing data for data science and machine learning models by joining multiple tables, adjusting row granularity, and engineering useful fields and features. COURSE OUTLINE: Intro to Data Science & Machine Learning Introduce the field of data science, review essential skills, and introduce each phase of the data science workflow Scoping a Project Review the process of scoping a data science project, including brainstorming problems and solutions, choosing techniques, and setting clear goals Gathering Data Read flat files into a Pandas DataFrame in Python, and review common data sources & formats, including Excel spreadsheets and SQL databases Cleaning Data Identify and convert data types, find and fix common data quality issues like missing values, duplicates, and outliers, and create new columns for analysis Exploratory Data Analysis (EDA) Explore datasets to discover insights by sorting, filtering, and grouping data, then visualize it using common chart types like scatterplots & histograms MID-COURSE PROJECT Put your skills to the test by cleaning, exploring, and visualizing data from a brand-new data set containing Rotten Tomatoes movie ratings Preparing for Modeling Structure your data so that it’s ready for machine learning models by creating a numeric, non-null table and engineering new features FINAL COURSE PROJECT Apply all the skills learned throughout the course by gathering, cleaning, exploring, and preparing multiple data sets for Maven Music __________ Ready to dive in? Join today and get immediate, LIFETIME access to the following: 8.5 hours of high-quality video 16 homework assignments 7 quizzes 2 projects (1 mid-course, 1 final) Data Science in Python: Data Prep & EDA ebook (190+ pages) Downloadable project files & solutions Expert support and Q&A forum 30-day Udemy satisfaction guarantee If you're an aspiring data scientist or business intelligence professional looking for an introduction to the world of machine learning and data science with Python and Pandas, this is the course for you. Happy learning! -Alice Zhao (Python Expert & Data Science Instructor, Maven Analytics ) __________ Looking for our full business intelligence stack? Search for " Maven Analytics " to browse our full course library, including Excel, Power BI, MySQL , Tableau and Machine Learning courses! See why our courses are among the TOP-RATED on Udemy: "Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C. "This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M. "Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.
- Master the core building blocks of Python for data science BEFORE applying machine learning algorithms
- Scope data science projects by clearly defining the goals, techniques, and data sources needed for your analysis
- Import and export flat files, Excel workbooks, and SQL database tables using Pandas
- Clean data by converting data types, handling common data issues, and creating new columns for analysis
- Perform exploratory data analysis (EDA) by sorting, filtering, grouping, and visualizing data to discover patterns and insights
- Prepare data for machine learning models by joining tables, aggregating rows, and applying feature engineering techniques
Temario
UNIDAD 1 - Getting Started · 7 leccionesUNIDAD 1 - Getting Started
UNIDAD 1 - Getting Started
- Course Introduction
- About This Series
- Course Structure & Outline
- READ ME: Important Notes for New Students
- DOWNLOAD: Course Resources
- Introducing the Course Project
- Setting Expectations
UNIDAD 2 - Intro to Data Science · 15 leccionesUNIDAD 2 - Intro to Data Science
UNIDAD 2 - Intro to Data Science
- Section Introduction
- What is Data Science?
- Data Science Skill Set
- What is Machine Learning?
- Common Machine Learning Algorithms
- Data Science Workflow
- Step 1: Scoping a Project
- Step 2: Gathering Data
- Step 3: Cleaning Data
- Step 4: Exploring Data
- Step 5: Modeling Data
- Step 6: Sharing Insights
- Data Prep & EDA
- Key Takeaways
- Intro to Data Science
UNIDAD 3 - Scoping a Project · 14 leccionesUNIDAD 3 - Scoping a Project
UNIDAD 3 - Scoping a Project
- Section Introduction
- Project Scoping Steps
- Think Like an End User
- Brainstorm Problems
- Brainstorm Solutions
- Supervised vs Unsupervised Learning
- Identify Data Requirements
- Data Structures
- Model Features
- Data Sources
- Data Scope
- Summarize the Scope
- Key Takeaways
- Scoping a Project
UNIDAD 4 - Installing Jupyter Notebook · 10 leccionesUNIDAD 4 - Installing Jupyter Notebook
UNIDAD 4 - Installing Jupyter Notebook
- Section Introduction
- Why Python?
- Installing Anaconda
- Launching Jupyter Notebook
- The Notebook Interface
- Edit vs Command Mode
- The Code Cell
- The Markdown Cell
- Helpful Resources & Key Takeaways
- Installing Jupyter Notebook
UNIDAD 5 - Gathering Data · 14 leccionesUNIDAD 5 - Gathering Data
UNIDAD 5 - Gathering Data
- Section Introduction
- Data Gathering Process
- Data Sources
- Structured vs Unstructured Data
- The Pandas DataFrame
- Reading Flat Files
- DEMO: Reading Flat Files
- Reading Excel Files
- Connecting to a SQL Database
- Quickly Exploring a DataFrame
- ASSIGNMENT: Gathering Data
- SOLUTION: Gathering Data
- Key Takeaways
- Gathering Data
UNIDAD 6 - Cleaning Data · 51 leccionesUNIDAD 6 - Cleaning Data
UNIDAD 6 - Cleaning Data
- Section Introduction
- Data Cleaning Overview
- Data Types
- Converting to DateTime
- Converting to Numeric
- DEMO: Converting Data Types
- ASSIGNMENT: Converting Data Types
- SOLUTION: Converting Data Types
- Data Issues Overview
- Finding Missing Data
- DEMO: Finding Missing Data
- Handling Missing Data
- Removing Missing Data
- Imputing Missing Data
- Resolving Missing Data
- ASSIGNMENT: Missing Data
- SOLUTION: Missing Data
- Finding Inconsistent Text & Typos
- Handling Inconsistent Text & Typos
- Updating Values Based on a Logical Condition
- Mapping Values
- Cleaning Text
- ASSIGNMENT: Inconsistent Text & Typos
- SOLUTION: Inconsistent Text & Typos
- Finding Duplicate Data
- Handling Duplicate Data
- ASSIGNMENT: Duplicate Data
- SOLUTION: Duplicate Data
- Finding Outliers
- Histograms
- Box Plots
- Standard Deviation
- Handling Outliers
- DEMO: Review Cleaned Data
- ASSIGNMENT: Outliers
- SOLUTION: Outliers
- Creating New Columns
- Creating Numeric Columns
- DEMO: Creating Numeric Columns
- ASSIGNMENT: Creating Numeric Columns
- SOLUTION: Creating Numeric Columns
- Creating DateTime Columns
- DEMO: Creating DateTime Columns
- ASSIGNMENT: Creating DateTime Columns
- SOLUTION: Creating DateTime Columns
- Creating Text Columns
- DEMO: Creating Text Columns
- ASSIGNMENT: Creating Text Columns
- SOLUTION: Creating Text Columns
- Key Takeaways
- Cleaning Data
UNIDAD 7 - Exploratory Data Analysis · 31 leccionesUNIDAD 7 - Exploratory Data Analysis
UNIDAD 7 - Exploratory Data Analysis
- Section Introduction
- Exploratory Data Analysis Overview
- Filtering
- DEMO: Filtering
- Sorting
- DEMO: Sorting
- Grouping
- DEMO: Grouping
- ASSIGNMENT: Exploring Data
- SOLUTION: Exploring Data
- Data Visualization Overview
- Data Visualization with Pandas
- DEMO: Data Visualization with Pandas
- Pair Plots
- DEMO: Pair Plots
- Distributions
- DEMO: Distributions
- Common Distributions
- The Normal Distribution
- ASSIGNMENT: Distributions
- SOLUTION: Distributions
- Scatter Plots
- DEMO: Scatter Plots
- Correlations
- DEMO: Correlations
- ASSIGNMENT: Correlations
- SOLUTION: Correlations
- Data Visualization in Practice
- EDA Tips
- Key Takeaways
- Exploratory Data Analysis
UNIDAD 8 - Mid-Course Project · 4 leccionesUNIDAD 8 - Mid-Course Project
UNIDAD 8 - Mid-Course Project
- Mid-Course Project Overview
- SOLUTION: Exploring Data
- SOLUTION: Creating New Columns
- SOLUTION: Visualizing Data
UNIDAD 9 - Preparing for Modeling · 35 leccionesUNIDAD 9 - Preparing for Modeling
UNIDAD 9 - Preparing for Modeling
- Section Introduction
- Case Study: Preparing for Modeling
- Data Prep for EDA vs Modeling
- Model Preparation Steps
- Creating a Single Table
- Appending
- DEMO: Appending
- Joining
- DEMO: Joining
- Types of Joins
- DEMO: Types of Joins
- DEMO: Creating a Single Table
- ASSIGNMENT: Creating a Single Table
- SOLUTION: Creating a Single Table
- Preparing Rows for Modeling
- DEMO: Preparing Rows for Modeling
- ASSIGNMENT: Preparing Rows for Modeling
- SOLUTION: Preparing Rows for Modeling
- Preparing Columns for Modeling
- Dummy Variables
- DEMO: Dummy Variables
- Preparing DateTime Columns
- DEMO: Preparing DateTime Columns
- ASSIGNMENT: Prepare Columns for Modeling
- SOLUTION: Prepare Columns for Modeling
- Feature Engineering
- Feature Transformations
- Feature Scaling
- Proxy Variables
- Feature Engineering Tips
- ASSIGNMENT: Feature Engineering
- SOLUTION: Feature Engineering
- PREVIEW: Applying Algorithms
- Key Takeaways
- Preparing for Modeling
UNIDAD 10 - Final Course Project · 5 leccionesUNIDAD 10 - Final Course Project
UNIDAD 10 - Final Course Project
- Final Project Overview
- SOLUTION: Gathering Data
- SOLUTION: Cleaning Data
- SOLUTION: Exploratory Data Analysis
- SOLUTION: Preparing for Modeling
UNIDAD 11 - Wrapping Up · 1 leccionesUNIDAD 11 - Wrapping Up
UNIDAD 11 - Wrapping Up
- BONUS LESSON
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
11 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
7 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Maven Analytics (2,000,000+ Learners).
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 11 unidades · 187 lecciones · 55 h
- 12 meses de acceso
- 7 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Maven Analytics (2,000,000+ Learners); INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.


