Enroll Course: https://www.udemy.com/course/data-analysis-using-pandas-in-python-learn-by-exercise/
In the ever-evolving world of data science, proficiency in Python is paramount. For anyone looking to dive into data analysis or data science, a solid foundation in Python is essential. I recently completed the ‘Python for Data Analysis / Data Science: A Crash Course’ on Udemy, and I’m excited to share my experience and recommendations.
This course is meticulously structured to guide learners from the absolute basics to more advanced techniques. It kicks off with ‘Getting started with Python,’ covering installation of the Anaconda distribution and your first lines of code, along with a helpful walkthrough of the Spyder platform. This initial section is perfect for beginners, ensuring a smooth onboarding experience.
The heart of the course lies in its comprehensive ‘Working on Data’ section. Here, you’ll learn to run SQL in Python, understand data, and add comments effectively. The course tackles crucial data cleaning tasks, including missing value detection and treatment (using mean, median, and mode), filtering data, and selecting specific columns. The introduction to Jupyter IDE is a welcome addition, making interactive data exploration a breeze. The use of `iloc` for filtering and Group By analysis with transpose results provides practical skills for data manipulation.
‘Working on multiple datasets’ is another strong segment. It covers appending and concatenating dataframes, merging them, and efficiently removing duplicates and sorting. The course offers insightful methods for finding row-wise maximums using `idxmax` and iterating with `iterrows`. You’ll also learn to create derived fields based on numerical, character, and date fields, which are fundamental for feature engineering. Cross-tab analysis and date manipulation (first/last day, same day last month) are also covered, adding significant value.
The ‘Data visualization and some frequently used terms’ section brings your data to life. You’ll learn to create histograms, bar charts, line charts, pie charts, and box plots using both Jupyter and Spyder. The review of Python fundamentals like variable scope, casting, string slicing, and lambda functions, along with dropping columns, ensures a well-rounded understanding.
Finally, the ‘Some statistical procedures and other advance stuffs’ section elevates your skills. It includes outlier detection and treatment, creating Excel-formatted reports, pivot tables, and renaming columns. The practical application of reading/writing to SQLite databases and writing execution logs is highly beneficial. The course concludes with essential statistical procedures like linear regression and the chi-square test of independence, providing a robust introduction to statistical modeling in Python.
Overall, ‘Python for Data Analysis / Data Science: A Crash Course’ is an excellent resource for anyone looking to build a strong foundation in data analysis with Python. The instructor’s clear explanations and practical examples make complex topics accessible. I highly recommend this course to aspiring data analysts, data scientists, and anyone interested in harnessing the power of Python for data manipulation and visualization.
Enroll Course: https://www.udemy.com/course/data-analysis-using-pandas-in-python-learn-by-exercise/