Go to Course: https://www.coursera.org/learn/python-data-analysis
### Course Review: Introduction to Data Science in Python on Coursera If you're looking to dive into the world of data science, one great introductory course to consider is "Introduction to Data Science in Python" offered on Coursera. This course lays a solid foundation in both the Python programming language and fundamental data manipulation techniques, making it an excellent choice for beginners and those looking to augment their skill set. #### Overview The course offers an engaging overview of Python as it pertains to data science. Learners are introduced to essential programming techniques, including the use of lambdas, reading and manipulating CSV files, and utilizing the Numpy library. A significant focus of the course is on data manipulation and cleaning — skills that are crucial for any data scientist. One of the highlights is the introduction of the Pandas library. This powerful toolkit is essential for data analysis and manipulation, and the course takes learners through its two main abstractions: Series and DataFrame. By the end of the course, you will be comfortable using these structures to perform various data operations. #### Syllabus Breakdown 1. **Fundamentals of Data Manipulation with Python** - The journey begins with an overview of the field of data science, the fundamental concepts of Python, and familiarity with Coursera’s Jupyter Notebook environment. - This week sets a solid groundwork for all future learning, breaking down programming concepts that are particularly relevant in data science contexts. 2. **Basic Data Processing with Pandas** - This week dives into Pandas, the quintessential data manipulation library in Python. Here, learners will explore how to read data into DataFrame structures, conduct queries, and understand indexing. - The approach is hands-on, with practical exercises to solidify these concepts, making new learners feel at ease. 3. **More Data Processing with Pandas** - Building on previous lessons, you'll gain deeper knowledge of merging DataFrames, generating summary tables, and grouping data logically. Discussions on metrics for analysis broaden your understanding of real-world data challenges. - The capstone of this week is a significant programming assignment, allowing you to apply what you've learned in a practical way. 4. **Answering Questions with Messy Data** - The final week shifts focus towards statistical techniques vital for data analysis, such as distributions, sampling, and t-tests. - By linking theory to practice, this week promises insightful discussions about the intersection of data and the scientific method, emphasizing the role of data in modern discovery. #### Recommendation I highly recommend "Introduction to Data Science in Python" for anyone eager to embark on a data science journey. The structure of the course is commendable, progressively building complexity while ensuring foundational concepts are thoroughly taught. The use of Jupyter Notebooks for practical applications is particularly effective, making coding accessible and manageable. Additionally, the supporting resources provided by Coursera, including forums and instructor interaction, enrich the learning experience, allowing students to clarify doubts and share insights. In summary, whether you're starting your career in data science or looking to enhance your programming and analytical abilities, this course is an excellent investment of your time. It prepares you well for further studies in data analysis, machine learning, or specific Python applications in the data domain. Embrace the opportunity to step into the engaging and rewarding field of data science through this informative course.
Fundamentals of Data Manipulation with Python
In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.
Basic Data Processing with PandasIn this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.
More Data Processing with PandasIn this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.
Answering Questions with Messy DataIn this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such a
Assignments are tough compared to the course lecture material. Therefore, alot of self learning is required other than the lectures. There should be more study material covered in the course videos
I found this course appealing because it was more practical based.it helped me alot in getting hands on experience and most of all I have learned how to solve real world problem with python libraries
It's very useful specially for new learner because it only dives into the part of python that data science need. I strongly recommend to anyone even if you don't have experience in programming before.
Um curso intenso e bastante prazeroso. Gostei de todas as etapas, os videos funcionam bem e estão construidos numa base introdutória, mas o desafio é pesquisar e pesquisar. Muito interessante mesmo!
overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .