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Data Analysis with Python

Applied Data Science,

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

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Rating 4.3 based on 988 ratings
Length 8 weeks
Effort This course requires approximately two hours a week for six weeks.
Starts Dec 14 (6 weeks ago)
Cost $38
From IBM via Coursera
Instructor Joseph Santarcangelo
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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What people are saying

data science

We'll see Fantastic review on basic python libraries and methods for Data Science.

The course is overall very helpful to learn Data Science with Python while it does require foundations for statistics for this module, so it appears difficult to understand some mathmetical concepts for beginners.

great course to get an understanding of python for data science More explanations would be great.

This course is much better than " python for data science", much more clear and detail.

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.

Very nice lessons for beginners to use python for data science.

should have more thorough examples Challenging, but very rewarding course with code that will help you with all your data science endeavors down the road.

Great experience I am working through the IBM Data Science Certificate courses (in order) and this is easily the best one I have taken so far.

It is a great gateway course into the world of data science.

It is mainly focussed on theoritical concepts of data science which have wide range of applications .

Overall good GOOD Good Course ,learn mant things about Data science in details The statistics background needed for the course need to be better explained.

Super nice overview of applied data science techniques and Python tools!

This is an excellent course for beginners in the data analysis and data science fields as it explains deep technical concepts in layman terms along with the Python code for the same.

Though found the ending modules a bit challenging, its a great course This course is very beneficial those who want to build carrier in data science.

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data analysis with python

Great Cource One of the best course for data analysis with python.

I highly recommend this course to all student who wants to learn data analysis with python.

Thank you so much for creating this is great learning and useful course that I got for Data Analytics.This course is very beneficial for all to enhance the knowledge about data analysis with Python.Thank you sir.

This course was more difficult than the others, and so i guess this is why employers prefer potential employees hold a PhD, or at least maintain a high algebraic/calculus/statistical aptitude Amazing course to start data analysis with python.

A well and vast explanation and review on Statistical Data Analysis with Python.

Really this course shows the full path to master the Data Analysis with Python.

Great experience The Data Analysis with Python gives you every introduction to become comfortable with Python as a Data Scientist The most useful course on studying statistics in short time Very good Course although more hands-on needed Amazing :) There are lots of mistakes throughout the courses Great course for introductory data analysis with Python.

A must take course very handy at giving the foundation of data analysis with python and what a nice introduction to linear regression with the library sklearn.

Good Start for Data Analysis with Python best course ever This course is probably the most concise and well explained course I have ever taken on the subject.

Very clear and effective course to get the basic principles of performing Data Analysis with Python.

Great Course........excellent introduction to data analysis with python.

Awesome learning Data Analysis with Python.

This is a tough class, yet it provides me with lots of technical skills regarding data analysis with Python I have learned a lot if things regarding data analysis .thanks to the teachers for helping me to understand how to analysis the data in different ways.thank you why is sharing of the notebook worth 3 points?

Excellent course that provides a great skills-focused overview on how to do data analysis with Python.

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machine learning

My first Machine learning course.

Did this after Andrew Ng's Machine Learning to learn to do the same things in Python.

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.

This was pretty good, I think maybe the best in the IBM machine learning certificate.

First go for module 8 - Machine Learning and come to this.

Through out the course i have learned alot like data visualisation mainly.I think i have completed successfully basics for machine learning.

My experience for this course on data analysis was really enriching where I learned various machine learning techniques and applied to real practical datasets.

It covers most important aspects of data preprocessing in machine learning using python libraries.

or at least reference to related learning materials to be given Complete coverage for all important topics with examples very nice nice This is good for somebody who has just started machine learning and wants some hands on experience in very few time.

Yes, you do have labs but there you are forced to write code in way so that you don't encounter problems later in the notebook.In a project or an assignment work, you have to play with variables and confusions and errors out of wonderland show up which lead to greater clarification.The course in itself is great and undoubtedly good in functioning as a prerequisite for Machine Learning and surely I'd recommend it to anyone who asks for an opinion.

Really helpful introduction to machine learning and predictive models!

greatcourse aerg This course is very good start for students who are planning to go into machine learning specifically.Students who have no Idea about regression and math find bit hard but little more effort from student side is needed.

At the end you will have a zeroth tool for machine learning.

Great :D The courses from IBM on Data Analysis, Visualization, Machine Learning are great.

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linear regression

Yes I can comfortably use linear regression now!

Like linear regression, Why gridsearch was covered I wonder.

Its a great introduction a clear explanation about Machine Learning, generation of linear regression models and all the things to do before to the analysis front the data.

This course will show you how to use various python packages to perform different kinds of regression (simple linear regression, multivariate regression, polynomial regression).

Excellent, Learning Linear Regression with an excellent approach.

Linear Regressions are needed some more clarity Very useful!Basic analysis course!

hard but good Very useful analytical techniques were learned such as cleaning the data, multiple linear regression, and working with test and training data.

This also helps me to know how to create multiple, poly, and linear regression model The course structure and videos are nice, but THERE ARE SO MANY ERRORS in the videos.

I believe I need more practice in these items (Linear Regression, Polynomials, Ridge, Fit, Predict, etc.)

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good course for beginners

Very good course for beginners and to learn basics.

Example (Week 3) instead of"from sklearn.metrics ..." the video comes up with "from sklearn.metrixs ..." It is a good course for beginners but I feel that the quizzes could have been a bit more challenging.

This is the good course for beginners.

It is a good course for beginners, topics are explained enough The course was good and the environment provided for executing the code was very good.

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hard to follow

Week 4 is somewhat hard to follow - Here, an overview over the different concepts would really help This course helped me to enhance my knowledge of data analysis using Python.

The Labs Do a great work in helping out A lot of information, it is at times hard to follow.

The amount of information was a lot, and I'm thankful for the notebooks I have now with steps on doing things, but the material could've been presented in a more cohesive way, this was hard to follow.

Final assignment is quite messy Extremely interesting BUT it gets long and hard to follow.

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ridge regression

Things got really fast mid of 4th week onwards for me, like explaining ridge regression and very complex topics without any proper introduction has left me kind of clueless.

excellent course i loved it Would be better if some underlying theory of advanced topics is covered, such as Ridge Regression etc.

In the end linear regression, and ridge regression is also introduced.

Linear Regression, ridge regression, etc are too advanced for new joiners who struggle with basic python.

The lesson need more explanations on Polynomial Regression, Pipeline, Ridge Regression.

For example, including theory about ridge regression, instead of just mentioning how to implement it in Python.

This course is very use for regression model end to end scratch of evaluation and easily understand the coding theory explanation but ridge regression is somewhat improvement is needed.Finally, I suggested to this course for learning data analysis with python.Thanks for wonder full opportunity to learn this course in course-era team... A good course which gives exposure to various data analysis practice through python.

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final project

the week 5 "Quizz") -No final project to conclude and summarize up our learning linter was used by authors.

I would like to see a final project in this course.

It gives you what you need The questions for the final project have some glitches (Question 3 does not accept a picture).

Great course, I loved the final project because it really puts what you've learned to test.

Week 6 is the Final ProjectWeek 7 is one statement about your certificate.

Usually in most courses, the final project will be in end of the final week.

Just as well, the Final Project was botched, the software and questions were depreciated and even written wrong by the creator.

Great but it has lots of information and require simple statical background Very good lectures, but the final project takes way longer to set up than to complete: finding the link to the final assignment and making it work in Watson took me too much time.

So many problems with the lessons and the final project.

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at times

Labs have typos and can be confusing at times though, the only thing that could be improved.

I think the instructor assumed that people taking the course would know a lot about Regression, Correlation and some other statistical functions, that it was hard to understand or follow at times.

The explanations were rushed at times and quite a bit was not easy to follow.

use of names was a bit confusing at times compared to final assignment, but otherwise very helpful and enjoyable Grate course.

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too fast

that was great The course like every other course in the specialization is a little too fast for me.

Videos were way too fast.

Sometimes it is too fast and the explanations are very short.

As compared to other courses this course seems to be too fast Excellent course Great course to deep dive into the data analysis using python.

Maybe a bit too fast for those who are not familiar with various kinds of regressions...Other parts are great!

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watson studio

Very valuable <3 Worst mooc I ever taken, they just advertise about IBM To shortGoes to fast in some aspects, the theory is completely missing in this course The final assignment for this course is frustrating because it uses Watson Studio instead of the learning environment we've used up to that point in the course.

Additionally, i found having to use Watson Studio for the assignment / labs as opposed to plain Jupyter a little annoying.

But some code of Lab Nothbook might not give desired output and need modification, and material about Watson Studio might need upgraded.

Regplot does not execute in the Watson Studio despite proper coding.

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Cloud Architect - Azure / Machine Learning $75k

Watson Machine Learning Engineer $81k

Machine Learning Software Developer $103k

Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k

RESEARCH SCIENTIST (MACHINE LEARNING) $147k

Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k

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Rating 4.3 based on 988 ratings
Length 8 weeks
Effort This course requires approximately two hours a week for six weeks.
Starts Dec 14 (6 weeks ago)
Cost $38
From IBM via Coursera
Instructor Joseph Santarcangelo
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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