Big data analytics is becoming new norm of organization eco-system to derive data driven decisions rather than opinion based decisions

This course on data analysis with Python started with basics and covered topics on preparing data for analysis, performing

simple statistical analysis,data visualization, predicting trends and patterns to have meaningful conclusions.

Course structure is nicely organized with step by step lectures with quizzes at interim levels aided by practice session.

Course has an interactive window which is similar to Jupyter NoteBook so that learner can practice their learning within the online course itself.

Moving forward to applicate these leanings in automating domain specific tasks in my portfolio.

Thanks to ASHOK LEYLAND for providing opportunity to learn Digital online courses.

The "Data Analysis with Python" course, presented by IBM on the Coursera platform, proved to be an exceptional and enriching educational journey. This meticulously crafted program offered a comprehensive initiation into the art of data analysis with Python, delving into pivotal libraries such as NumPy, Pandas, and Matplotlib. The course thoughtfully integrated hands-on assignments and real-world projects, captivating learners with their relevance and engagement. Seasoned instructors with profound expertise and unwavering support added a layer of excellence to the learning experience. Undoubtedly, this course is a perfect entry point for aspiring data analysts keen on immersing themselves in the world of Python-based data analysis. Wholeheartedly recommended!

The "Data Analysis with Python" course provides an exceptional foundation in leveraging Python for comprehensive data analysis. With its rigorous curriculum, the course delves into essential techniques and tools, including Pandas, NumPy, and Matplotlib, equipping students with valuable skills for handling and interpreting complex datasets. The hands-on approach and real-world case studies enhance the learning experience, ensuring practical application of theoretical concepts. Expert instructors deliver content with clarity and precision, fostering a deep understanding of advanced data analysis methodologies. This course is an invaluable investment for professionals seeking to advance their analytical capabilities and drive data-informed decision-making.

Before enrolling in the IBM Data Science Professional Certificate program, it was stated that upon completion, you would receive a certificate titled "IBM Data Science Professional Certificate." However, after completing all the courses and downloading the certificate, I noticed that it is simply labeled "IBM Data Science." My concern is that the certificate does not reflect the professional certification that was advertised. I have gained substantial skills and knowledge through this course and specialization, and I feel somewhat misled by the discrepancy in the certificate's title. I request that the team address this issue and ensure that the certificate accurately reflects the "IBM Data Science Professional Certificate" as initially promised.

I would like to express my deep gratitude for the opportunity to participate in the Data Science course. The content was extremely informative and incredibly useful. I have gained valuable insights and practical skills that I am confident will be beneficial in my career. The tutors were very knowledgeable and provided comprehensive feedback and support throughout the course. The inclusion of real-world case studies and hands-on projects enriched the learning experience. I appreciate the well-structured and professionally organized course that allowed me to learn at my own pace. I look forward to applying the knowledge and skills I have gained from this course in my future endeavors. Thank you once again for a remarkable learning experience.

I read the book 'An Introduction to statistical analysis using R'. To reach to the concept of ridge regression it took about 3 months (as i can only spend an hours a day study hour) and page number > 200 for me to understand the statistical concepts of ridge regression, cross validation etc. And still I was tentative in R. Now, based on this video course and labs, the learning concepts and python implementation could just be done in 2 weeks time (spending 4 hrs on weekends). A lot of effort has been put in this course to make it sound simple. Thank you authors. Wish you continued motivation to design such courses.

Learning is a process of blending theory and practical in equal portions to provide intellectual inputs to get tangible outputs. This course is a perfect example of it, as it consists of ample hands-on lab sessions for each module, where anyone could practice what they have been taught through the videos. The videos are super explanatory, where even a beginner could learn from scratch with passion and love. I take this opportunity to thank all the instructors, resource providers and contributors, and wish you all the very best to keep your knowledge-sharing efforts with pride and joy.

This course gives me a brief understanding of data analysis based in the use of Python. Since I have already had a foundation of the basic knowledge of coding with other programming language, this course started with introducing several basic packages for data science followed with the use of each package. Also, in week 4 and week 5, the course provided me with the idea of generating statistical models to train our data sets. The thinking method of evaluating a model will help me a lot in my future studies in the field of machine learning and deep learning.

It is an excellent course for beginners in Data Analytics. It teaches you all basic concepts required for data analysis which includes data pre-processing, data wrangling, data formatting, data normalization, data binning, Exploratory data analysis and data modelling. It also teaches you descriptive statistics including, Correlation, ANOVA etc., It also helps you with basic data visualization, Linear regression, prediction, decision making, Model evaluation and refinement using Ridge Regression and Grid Search. I find it very useful for beginners.

To pass this course is really not easy as it doesn't just teach us how to code to fulfill the data analysis but it delivers a lot of relevant knowledge of statistics as well, including linear regression, polynomial regression, ridge regression, MSE, R2, ANOVA, etc. Coding is not difficult but understanding those methods of analysis is hard. so if you have little basis of statistics, you have to work harder. But I feel more confident after the course because I have gained one more skills. Keep on going and embrace the future.

The course nicely gives you a glimpse of the endless possibilities in the area of Analytics. It showcases how data can be easiely and speedily analyzed using Python if you are clear even with the basics of Python programming. It provides a prefect platform to gain skill sets needed to be a great Analyst.

The course is wonderfully desined, the material within seems self-explanatory and you won't have to struggle to grasp the concepts taught. Labs are awesome and so is the team who made the course what it is. Really loved it!

Honestly it is one of the best courses I've attended in Data Science. All the ambiguous concepts that I read in the internet and couldn't understand were clear in this course and I didn't have to struggle to get them. The way the course is structured, the visual materials, labs, quizzes and assignments ensure that you leave the the course with good theoretical and technical understanding. Thanks for making it easy to learn Data Science and python! I would definitely recommend this course if you want to have a good start.

Loved the course overall. Truly amazing! Professors did a really great job in making and structuring this course session by session.

A good course to learn know-how of Data Analysis using Python language so as to facilitate analysis and visualization of data to make effective decisions. I thank the professors to make this course interesting and worth it. Only thing is, videos can be made in a better way so as to facilitate people with non programming background. Maybe some basics of programming would help.

Become a Trustworthy Data Analyst

This course provides the knowledge and skills that form the foundation for data analysis. Students learn how to use Python Packages and gain experience creating dataframes and manipulating data sets for computation and visualization. Extensive work on building and evaluating models is included with explanatory lectures and hand-on labs to work with real data. Students' data analysis work will be supported by applying proper of model optimizations learned in the course.

A very good course in general, everything is explained in easiest to understand way that can be easily absorbed by students with little or no knowledge about data analysis and machine learning (mainly Linear Regression in this course). Although the exercises provided by this course is considered a lot, and more than enough to cover the exam and assignments of this course itself, but I hope the creator can put in some optional exercises to help us practice more and more and eventually being job ready.

A step-by-step and detailed introduction to data analysis using Python. It covers a 0 to 1 understanding from importing data to evaluating models, and offers hand-on labs to run codes. The content also includes all the packages and libraries necessary and essential to do data analysis. The courses are somehow in detail, if not, hard, but the tests and assignments are easier. I am sure I will always review the codes I have learned in the course in the future when I go deeper into data analysis.

I audited the course. I did not complete the quizzes because my goal was to get a very quick overview of pandas and scikit and pick up on basics. This was at the right level for me and did not go haphazardly. It did not try to convince me that something was simple, hard or not important.

I recommend this as a starting point for most who have little experience with Python but are well-versed in programming otherwise and want to get a look at a little of the ecosystem for ML using Python.

Fantastic Course for Aspiring Data Analysts! I recently completed the Data Analysis with Python course, and it was exceptional! The lessons were clear, practical, and easy to follow, making complex concepts simple to grasp. The hands-on approach with real-world datasets helped solidify my skills in using Pandas, NumPy, and Matplotlib for data manipulation and visualization. Whether you're a beginner or looking to enhance your Python skills, this course is a must. Highly recommended!

This course is great!!!

I just have a little suggestion/request that is on week 4, Pipeline theme, that they could detached it from polynomial regression and dedicate a couple minutes to detail it a little bit further on code, applications anda variations. (it was spent exact 1 min).

Besides it, it was a very practice course, I learnt a lot and certainly this course expanded my tool box in my portfolio and for sure I'll have to continue my practice to domain it properly!

I will say an excellent class! You will learn a lot essential data analysis methods, and the concepts.

Ok, it's never easy for someone who never learned such knowledges before, now encounters all those statistics concepts along with python code. But still, this class managed to use an easy way to explain all those abstract concepts. The forum also helps a lot to explain some difficulties. You might feel lost in the models, but once you learn it, you feel good.

I absolutely loved the course! It’s very well-structured, with clear translations and excellent instructors. The content is both practical and insightful, offering a great opportunity to revisit key Python functions and concepts that we sometimes overlook in daily practice. It really helped solidify my understanding and reminded me of important details I had forgotten. Highly recommend it for anyone looking to strengthen their Python skills!

Really good stuff in terms of outlining what is necessary in order to properly analyze the data. One thing to note is the powerpoint slides are off sometimes. Some of the stuff is not spelled correctly in the code.

Another issue is that x and y axis variables will be assigned, but be on the opposite axes (I.E when x = df['price'] but in the scatterplot it's actually the target variable, and thus on the y-axis.

This is an excellent course to begin with analyzing data in python. However, it would have been even more useful and interesting had it contained some more discussions on the topics like logarithmic transformation of features, when to apply it, how to do bi-variate and mutivariate analysis, exercises on topics like manipulation of dataframes using pivot, melt, crosstab etc.

Fantastic introduction to some of main python libraries and functions used in order to do anything related to data analysis, also a good entry point for machine learning, big data and other data science specialisations - highly recommended for anyone comfortable with high level scripting and basic oops concepts - if you don't then best take a basic course in python first...

This course is useful for statistics students who are not taught any programming languages before. It gives us a quick way to organize data. It is also an excellent online course with lab assignments by the end of the modules to practice the Python. I would say it would definitely benefit my career as data is increasingly available nowadays at any corporations.