|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-data-science-training-with-python-for-data-analysis/
课程评论:没有评论
课程名称:使用Python进行数据分析的完整数据科学培训 课程概述:本课程是一个全面的指南,旨在帮助您掌握数据科学的实用技巧,特别是通过Python进行的统计建模、数据可视化和机器学习等内容。整个课程持续12小时,是一个全面的数据科学培训营,适合希望在数据分析领域深入学习的学生。 为什么选择这门课程:这门课程涵盖了数据科学的所有实践方面,您通过学习这门课程可以不再需要其他相关课程或书籍。Python在大数据时代的应用非常广泛,学习如何存储、过滤、管理和处理数据,能够为您的职业发展提供竞争优势。完成这门课程,您将成为实用Python数据科学的专家。 课程讲师:Minerva Singh,牛津大学地理与环境学硕士,剑桥大学热带生态与保护博士,拥有多年分析现实数据和相关技术的经验,以及国际期刊的出版经历。 课程内容概述:课程分为12个部分,涵盖Python数据科学的各个方面,包括: - Python数据科学的全面介绍,使用Anaconda等强大框架 - Jupyter笔记本的应用 - Numpy的基本分析工具:数组、运算、矩阵等 - Pandas数据结构和数据读取:CSV、Excel、JSON等 - 数据预处理和清理技术 - 各种类型的数据可视化方法 - 统计分析、推断及变量关系 - 机器学习概念(监督学习和无监督学习) 此外,课程将介绍如何创建人工神经网络和深度学习模型。 无论您的背景如何,课程均以易于理解的方式教授Python数据科学基本概念,重视实际操作与真实数据的应用。 课程目标: - 帮助学生从零基础提升至掌握高级数据科学技术 - 教授Python在统计分析和数据可视化中的实际应用 - 帮助学生理解统计和机器学习的基本概念,能够应用于实际数据分析 课程将以实践为主,着重于如何将不同技术应用于真实数据,并解释分析结果。每完成一节视频,您都将学习到新的概念或技巧,适用于自己的项目。 立即加入课程,开启您的数据科学学习之旅!
Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & MoreTHIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS: It's A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python! HERE IS WHY YOU SHOULD TAKE THIS COURSE:First of all, this course a complete guide to practical data science using Python...That means, this course covers ALL the aspects of practical data science and if you take this course alone, you can do away with taking other courses or buying books on Python-based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge & boost your career to the next level!THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!But, first things first, My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.Over the course of my research, I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.This gives the student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modelling to visualization to machine learning. Unlike other Python instructors, I dig deep into the statistical modelling features of Python and gives you a one-of-a-kind grounding in Python Data Science! You will go all the way from carrying out simple visualizations and data explorations to statistical analysis to machine learning to finally implementing simple deep learning-based models using PythonDISCOVER 12 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON DATA SCIENCE (INCLUDING):• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda• Getting started with Jupyter notebooks for implementing data science techniques in Python• A comprehensive presentation about basic analytical tools- Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, Broadcasting, etc.• Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data• How to Pre-Process and "Wrangle" your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.• Creating data visualizations like histograms, boxplots, scatterplots, bar plots, pie/line charts, and more!• Statistical analysis, statistical inference, and the relationships between variables• Machine Learning, Supervised Learning, Unsupervised Learning in Python• You'll even discover how to create artificial neural networks and deep learning structures...& MUCH MORE!With this course, you'll have the keys to the entire Python Data Science kingdom!NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:You'll start by absorbing the most valuable Python Data Science basics and techniques.I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real life.After taking this course, you'll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python. You'll even understand deep concepts like statistical modelling in Python's Statsmodels package and the difference between statistics and machine learning (including hands-on techniques). I will even introduce you to deep learning and neural networks using the powerful H2o framework!With this Powerful All-In-One Python Data Science course, you'll know it all: visualization, stats, machine learning, data mining, and deep learning! The underlying motivation for the course is to ensure you can apply Python-based data science on real data and put into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with actual examples of your data science abilities.HERE IS WHAT THIS COURSE WILL DO FOR YOU:This course is your one shot way of acquiring the knowledge of statistical data analysis skills that I acquired from the rigorous training received at two of the best universities in the world, a perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. This course will: (a) Take students without a prior Python and/or statistics background from a basic level to performing some of the most common advanced data science techniques using the powerful Python-based Jupyter notebooks. (b) Equip students to use Python for performing different statistical data analysis and visualization tasks for data modelling. (c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that students can apply these concepts for practical data analysis and interpretation. (d) Students will get a strong background in some of the most important data science techniques. (e) Students will be able to decide which data science techniques are best suited to answer their research questions and applicable to their data and interpret the results.It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpret the results. After each video, you will learn a new concept or technique which you may apply to your own projects. JOIN THE COURSE NOW!#data #analysis #python #anaconda #analytics