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所在平台: Udemy |
课程主页: https://www.udemy.com/course/bigdata-analysis-python/
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课程名称：数据科学与数据分析实战项目 课程概述：本课程提供了9个精心设计的数据分析项目，全部使用Python语言解决。Python以其在数据分析领域的多功能性和有效性而闻名。课程为希望成为数据分析师的人士提供了宝贵的实践经验和实用见解，帮助学习者在复杂的实际数据分析场景中自信而熟练地应对挑战。此外，对于考虑转向充满活力和回报丰厚的数据分析领域的学习者，这些项目奠定了坚实的基础，帮助他们掌握必要的技能与知识。 这些项目不仅适合学生学习，也可作为学术提交的潜在作品。通过完成这些项目，学生可以展示他们在数据分析技术方面的能力，提升学术资质。我们还提供了所有项目的源代码和数据集，促进学习者深入研究材料并通过动手实验强化理解。每个项目都配有清晰简洁的说明，使各种水平的学习者都能轻松上手。 项目涵盖范围广泛，涉及多个主题，包括气象数据分析、汽车数据分析、警察数据分析、Covid数据分析、伦敦住房数据分析、人口普查数据分析、Udemy数据分析、Netflix数据分析和销售数据分析。课程中使用的命令示例包括：reset_index()、loc[]、info()、drop()、str.strip().str.replace()、duplicated()、drop_duplicates(inplace=True)、round()、to_datetime()、groupby()等。 通过这些项目和命令，学习者不仅能够掌握数据分析的基本技能，还深入理解驱动数据分析领域的基本原则和方法论。无论您是追求数据分析师职业、希望增强学术作品集，还是渴望扩展Python数据分析知识和技能，本课程都为您量身定制，满足您的需求与抱负。
In this comprehensive course, we present to you 9 meticulously crafted Data Analytics projects, meticulously solved using Python, a language renowned for its versatility and effectiveness in the realm of data analysis.These projects serve as an invaluable resource for individuals embarking on their journey towards a career as a Data Analyst, offering practical insights and hands-on experience essential for success in the field.Moreover, for those contemplating a transition into the dynamic and rewarding domain of data analytics, these projects provide a solid foundation, equipping learners with the requisite skills and knowledge to navigate the complexities of real-world data analysis scenarios with confidence and proficiency.Designed with students in mind, these projects are not only educational but also serve as potential submissions for academic institutions. By working through these projects, students can demonstrate their proficiency in data analysis techniques and enhance their academic credentials.As part of our commitment to fostering a supportive learning environment, we provide access to the source code and datasets for all projects, enabling learners to delve deeper into the material and reinforce their understanding through hands-on experimentation.Each project is accompanied by clear and concise explanations, ensuring accessibility for learners of all levels. Whether you're a novice exploring the fundamentals of data analysis or a seasoned professional seeking to expand your skill set, you'll find these projects to be both engaging and enlightening.Central to the completion of these projects is the utilization of the Python Pandas Library, a powerful toolset for data manipulation and analysis. By leveraging the capabilities of Pandas, learners gain practical experience in handling and analysing data efficiently, setting the stage for success in their future endeavours.For further elucidation on the concepts and techniques covered in each project, we encourage learners to peruse the descriptions provided for each video lecture, where additional insights and guidance await.Now, let's delve into the diverse array of projects awaiting you: Project 1 - Weather Data AnalysisProject 2 - Cars Data AnalysisProject 3 - Police Data AnalysisProject 4 - Covid Data AnalysisProject 5 - London Housing Data AnalysisProject 6 - Census Data AnalysisProject 7 - Udemy Data AnalysisProject 8 - Netflix Data AnalysisProject 9 - Sales Data AnalysisSome examples of commands used in these projects are:* reset_index() - To convert the index of a Series into a column to form a DataFrame.* loc[ ] - To show any row's values.* info() - To provide the basic information about the dataframe.* drop() - To drop any column or row from the dataframe.* str.strip().str.replace(r'/s+', ' ', regex=True) - To remove extra spaces in any text column.* duplicated() - To show all the duplicate records from a dataframe.* drop_duplicates(inplace=True) - To remove the duplicate records from the dataframe.* round() - To round-off the values of a numerical column.* to_datetime() - To convert the datatype of date column into datetime format.* groupby() - To make the group of all unique values of a column.* std() - To check the standard deviation of any numerical column.* var() - To check the variance of any numerical column.* mean() - To check the mean of any numerical column.* agg() - Using agg() with groupby().* head() - It shows the first N rows in the data (by default, N=5).* columns - To show all the column names of the dataframe.* unique() - In a column, it shows all the unique values. It can be applied on a single column only, not on the whole dataframe.* nunique() - It shows the total no. of unique values in each column. It can be applied on a single column as well as on the whole dataframe.* describe() - To show some summary about the columns.* astype() - To change the datatype of any column.* dtype - To check the datatype of any column.* value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only.* plot(kind='bar') - To draw the bar graph.* type() - To the type of any variable.* plt.figure(figsize = ()) - To set the size of any figure.* plt.title(), plt.xlabel(), plt.ylabel() - To set the Title, x-axis label, y-axis label.* sort_values(ascending = False) - To sort the values in descending order.* dt.month - To create a new column showing Month only.* shape - It shows the total no. of rows and no. of columns of the dataframe* index - This attribute provides the index of the dataframe* dtypes - It shows the data-type of each column* count - It shows the total no. of non-null values in each column. It can be applied on a single column as well as on the whole dataframe.* isnull( ) - To show where Null value is present.* dropna( ) - It drops the rows that contains all missing values.* isin( ) - To show all records including particular elements.* str.contains( ) - To get all records that contains a given string.* str.split( ) - It splits a column's string into different columns.* dt.year.value_counts( ) - It counts the occurrence of all individual years in Time column.* sns.countplot(df['Col_name']) - To show the count of all unique values of any column in the form of bar graph.* max( ), min( ) - It shows the maximum/minimum value of the seriesThrough these projects and commands, learners will not only acquire essential skills in data analysis but also gain a deeper understanding of the underlying principles and methodologies driving the field of data analytics. Whether you're pursuing a career as a Data Analyst, seeking to enhance your academic portfolio, or simply eager to expand your knowledge and skills in Python-based data analysis, this course is tailored to meet your needs and aspirations.
