Estadísticas para la Ciencia de Datos con Python from Coursera

Este curso de Estadística para la Ciencia de Datos está diseñado para presentarle los principios básicos de los métodos y procedimientos estadísticos utilizados para el análisis de datos. Después de completar este curso, tendrá conocimientos prácticos de temas cruciales en estadística que incluyen: recopilación de datos, resumen de datos utilizando estadísticas descriptivas, visualización de datos, examen de relaciones entre variables, distribuciones de probabilidad, valores esperados, pruebas de hipótesis, introducción a ANOVA (análisis de la varianza), análisis de regresión y correlación. Adoptará un enfoque práctico para el análisis estadístico utilizando Python y los Notebooks Jupyter, las herramientas elegidas por los científicos y analistas de datos. Al final del curso, completará un proyecto para aplicar varios conceptos en el curso a un problema de Ciencia de Datos que involucre un escenario inspirado en la vida real y demostrará una comprensión del pensamiento y razonamiento estadístico fundamental. El objetivo es desarrollar una comprensión clara de los diferentes enfoques para diferentes tipos de datos, desarrollar una comprensión intuitiva, realizar evaluaciones apropiadas de los métodos propuestos, utilizar Python para analizar nuestros datos e interpretar el resultado con precisión. Este curso es adecuado para una variedad de profesionales y estudiantes que deseen comenzar su viaje en roles basados en datos y estadísticas, como Científicos de Datos, Analistas de Datos, Analistas de Negocios, Estadísticos e Investigadores. No requiere ningún conocimiento de informática o estadística. Recomendamos encarecidamente tomar el curso Python para Ciencia de Datos antes de comenzar este curso para familiarizarse con el lenguaje de programación Python, los notebooks Jupyter y las bibliotecas. También se proporciona un repaso opcional en Python. Después de completar este curso, un alumno podrá: ✔Calcular y aplicar medidas de tendencia central y medidas de dispersión a datos agrupados y no agrupados. ✔ Resumir, presentar y visualizar datos de una manera clara, concisa y que proporcione una visión práctica para los no estadísticos que necesitan los resultados. ✔Identificar las pruebas de hipótesis apropiadas para usar en conjuntos de datos comunes. ✔ Realizar pruebas de hipótesis, pruebas de correlación y análisis de regresión. ✔ Demostrar competencia en análisis estadístico utilizando Python y Notebooks Jupyter.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a clear understanding of statistics for analysis

Focuses on fundamental concepts, building a strong foundation

Taught by experienced instructors recognized for their expertise

Designed for beginners with no prior knowledge of statistics

Emphasizes practical applications of statistics through Python

Reviews summary

Practical data science statistics with python

This course on data science statistics using Python is well received by students. Students like the practical approach and the fact that the course is relevant to their professional lives. However, some students report skepticism about what they may receive after registering for the program.

This course provides a good overview of statistical tools.

"Buen panorama general de las principales herramientas estadísticas."

This course is practical and hands-on.

"Excellent course! Very practical."

"Adoptará un enfoque práctico para el análisis estadístico utilizando Python y los Notebooks Jupyter."

Some students did not receive their credential.

"Very good course very complete, however I was bored because despite having confirmed my interest in obtaining the credential in credly, I never received it and it seems very important to be able to share in my social networks."

Some students had issues with the IBM Watson Studio.

"Me gustó mucho el curso, pero me fue muy dificil crear Watson Studio."

"Mala experiencia, solo deja hacer las tareas con IBM WATSON STUDIO, y cobran demasiado por la suscripcion"

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 Estadísticas para la Ciencia de Datos con Python with these activities:

Review linear algebra and calculus

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Refresh your understanding of linear algebra and calculus. This will provide a strong foundation for comprehending the mathematical concepts underlying statistical methods.

Browse courses on Linear Algebra

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Review textbooks or online resources on linear algebra and calculus.
Complete practice problems to reinforce your understanding.

Work through Python tutorials for data science

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Strengthen your Python skills for data science. This will enable you to confidently apply Python to the statistical methods and analyses covered in the course.

Browse courses on Python

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Enroll in a Python tutorial for data science.
Complete the tutorial modules and practice exercises.

Read Probability and Statistical Inference

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Solidify your existing knowledge of probability and inference. This will establish an intuitive understanding of the related topics to be covered in the course.

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Read Chapters 1 & 2 to review the basic concepts of probability and random variables.
Complete the practice exercises at the end of each chapter.
Create a summary of the key concepts covered in the chapters.

Three other activities

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Engage in peer study sessions

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Foster collaboration and enhance understanding through peer discussions. This will provide opportunities to clarify concepts, share perspectives, and support each other's learning.

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Form a study group with classmates.
Meet regularly to discuss course materials, solve problems, and prepare for assessments.

Practice solving statistical problems

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Develop your problem-solving skills in statistics. This will enhance your ability to apply statistical techniques effectively to real-world data.

Browse courses on Data Analysis

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Find practice problems from textbooks or online resources.
Attempt to solve the problems independently.
Check your solutions against provided answer keys or consult with a tutor if needed.

Create a data visualization project

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Apply your understanding of data visualization techniques to communicate insights from data. This will strengthen your ability to present statistical findings effectively.

Browse courses on Data Visualization

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Choose a dataset and identify key insights to visualize.
Select appropriate data visualization techniques to convey the insights.
Create the data visualization using a tool like Tableau or Python.
Present your visualization and insights to a peer or mentor for feedback.

Career center

Learners who complete Estadísticas para la Ciencia de Datos con Python will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians apply statistical methods to data to draw conclusions, make predictions, and help solve problems. This course will help build a foundation of how to use Python in solving these problems.

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Actuary

Actuaries use statistics to assess and manage risk. As such, they need to be able to summarize, interpret, and analyze data. This course may be useful for building a foundation for working as an actuary.

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Epidemiologist

Epidemiologists study data to understand the causes of disease and promote public health. This course may be useful for learning the basics of using statistics in epidemiology.

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Health Scientist

Health Scientists use statistics to analyze data on health outcomes, public health issues, and more. This course may be useful for learning the basics of using statistics in health science.

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Machine Learning Engineer

Machine Learning Engineers use statistics to build and deploy machine learning models. This course will help build a foundation for understanding and using statistics in machine learning.

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Market Researcher

Market Researchers collect, analyze, and interpret data to predict market trends. This course may be useful for learning the basics of using statistics in market research.

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Data Scientist

Data Scientists use statistics to develop machine learning models which are used for making predictions and getting insights from data. A key part of being a Data Scientist is the ability to summarize and visualize data. This course may be useful for learning how to do this, and then a Data Scientist can use this knowledge to present their findings to decision-makers who are not data scientists.

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Data Analyst

A key component of Data Analyst work is presenting data to non-technical stakeholders. Statistics are critical for conveying insights effectively. This course may be useful for building the foundation of a Data Analyst.

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Data Engineer

Data Engineers rely on statistics to help with data preprocessing and feature engineering. This course may be helpful for getting an understanding of how to use statistics in data engineering.

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Quantitative Analyst

Quantitative Analysts (Quants) use statistical models to analyze data and make predictions in the financial industry. This course may be useful for building a foundation of statistics.

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Research Scientist

Research Scientists use statistical principles to design experiments and draw conclusions from data. This course may be useful for learning the basics of statistics as applied to research.

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Financial Analyst

Financial Analysts use statistics to evaluate investments and make recommendations. This course may be useful for learning how to use statistics to analyze financial data.

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Business Analyst

Business Analysts rely on data to help companies understand their performance. This course may be useful for learning how to interpret data and communicate insights to help improve business outcomes.

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Public Relations Specialist

Public Relations Specialists use statistics to evaluate the effectiveness of public relations campaigns. This course may be useful for learning the basics of using statistics in public relations.

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Software Engineer

Software Engineers may use statistics in their work, particularly in data science or machine learning applications. This course may be of use in building a foundation for using statistics in software engineering.

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