释放数据潜力：数据掌握综合指南
学习内容：
数据分析师所需的完整数据分析技能。
使用 Python 模块读取和操作数据的不同方法
使用不同 Python 模块的数据可视化。
完成基础 顺利过渡到数据科学所需的技能
要求：
参加本课程没有先决条件。
描述：
“机器学习数据科学迷你硕士”是一门综合课程，旨在为参与者提供在数据科学和机器学习领域脱颖而出所需的基本技能和知识。
在整个课程中，学生将深入研究关键概念、技术和工具，这些概念、技术和工具对于利用数据提取有意义的见解并使用有吸引力的可视化技术进行分析至关重要。该课程首先介绍数据科学的基础，涵盖数据操作、清理和可视化等主题，使用流行的工具（如 Python、Pandas、matplotlib、Plotly、Seaborn 等库）进行数据操作、清理和可视化。
特别强调每个概念的实际应用，使用现实生活中的例子来分析数据集、识别模式和培养分析技能。在课程结束时，学生将在机器学习的数据科学方面打下坚实的基础，使他们能够自信地应对复杂的数据挑战，做出数据驱动的决策，并为各个领域做出有效贡献。
无论是有抱负的数据科学家还是希望提高技能的专业人士，本课程都能为个人提供必要的工具和专业知识，让他们在当今的数据驱动世界中茁壮成长。该课程经过精心策划，每个主题都以学生充分利用学习课程的时间的方式提供。
本课程适合谁：
对于希望担任数据分析师的学生。
对于希望过渡到数据科学的候选人。
对于希望将 Python 数据科学添加为技能的候选人。
对于希望过渡到数据科学的程序员。
Mini Masters in Data Science for Machine Learning
Published 4/2024
Created by Ganeshraj Shetty
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 91 Lectures ( 23h 38m ) | Size: 13.5 GB
Unlocking Data's Potential: A Comprehensive Guide to Data Mastery
What you'll learn:
Complete Data Analytical Skills required for a Data Analyst.
Different ways to read and manipulate data using Python modules
Data Visualization using different Python Modules.
Complete foundations Skills required to make a smooth transition into Data Science
Requirements:
There are no prerequisites for taking up this course.
Description:
The "Mini Masters in Data Science for Machine Learning" is a comprehensive course designed to equip participants with the fundamental skills and knowledge required to excel in the field of data science and machine learning. Throughout the program, students delve into key concepts, techniques, and tools essential for leveraging data to extract meaningful insights and perform analysis using attractive visualization techniques.The course begins with an introduction to the foundations of data science, covering topics such as data manipulation, cleaning, and visualization using popular tools like Python and libraries such as NumPy, Pandas, matplotlib, Plotly, Seaborn, etc. Special emphasis is placed on the practical application of each concept using real-life examples to analyze datasets, identify patterns, and develop analytical skills.By the end of the program, students will have developed a strong foundation in data science for machine learning, enabling them to confidently tackle complex data challenges, make data-driven decisions, and contribute effectively to various domains. Whether aspiring data scientists or professionals looking to upskill, this course equips individuals with the necessary tools and expertise to thrive in today's data-driven world.The Course has been curated neatly and every topic has been delivered in such a way that student make the most out of the time spent on learning the course.
Who this course is for:
For Students who wish to work as a Data Analyst.
For Candidate who wish to make a transition into Data Science.
For Candidates who wish to add Python for Data Science as a Skill.
For Programmers who wish to transition into Data Science.
