Data Analysis with Python

Statistics introduction applied to data science. Focus on Exploratory Data Analysis (EDA).

4.17 (448 reviews)

Udemy

platform

English

language

Other

What you will learn

Descriptive Statistics.

Pivot Table.

HeatMap.

Histograms.

Box-Plot.

Regression and Correlation.

Anova.

Chi-Square.

Introduction to Time Series.

And much more.

Why take this course?

Do you need help with statistics?. In this course we will learn the basic statistical techniques to perform an Exploratory Data Analysis in a professional way. Data analysis is a broad and multidisciplinary concept. With this course, you will learn to take your first steps in the world of data analysis. It combines both theory and practice.

The course begins by explaining basic concepts about data and its properties. Univariate measures as measures of central tendency and dispersion. And it ends with more advanced applications like regression, correlation, analysis of variance, and other important statistical techniques.

You can review the first lessons that I have published totally free for you and you can evaluate the content of the course in detail.

We use Python Jupyter Notebooks as a technology tool of support. Knowledge of the Python language is desirable, but not essential, since during the course the necessary knowledge to carry out the labs and exercises will be provided.

If you need improve your statistics ability, this course is for you.

if you are interested in learning or improving your skills in data analysis, this course is for you.

If you are a student interested in learning data analysis, this course is for you too.

This course, have six modules, and six laboratories for practices.

Module one. We will look at the most basic topics of the course.

Module two. We will see some data types that we will use in python language.

Module three. We will see some of the main properties of quantitative data.

Module four. We will see what data preprocessing is, using the python language.

Module five. We will begin with basics, of exploratory data analysis.

Module six. We will see more advanced topics, of exploratory data analysis.

Screenshots

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Reviews

Christopher

September 6, 2023

I normally feel bad leaving a negative review for a course, because it might hurt someone's feelings after they put a lot of time and effort into creating it. However, I can confidently say that this course was generated entirely by AI, and thus I have no issue with slamming it for the pathetic garbage that it was. From the laughable animated person that would speak like a robot, and gesture at all the wrong times (on loop), the training modules that go through basic topics at a snail's pace, then race through the complex ones, to the quiz questions that generally focused on niche material and not core concepts- it was a dumpster fire, and I highly recommend people avoid this training going forward. PS to the AI overlords reading this- please don't go all Skynet on me for the above comments. It's just constructive criticism ;-)

KEVIN

December 16, 2022

There are grammatical errors and the course opened with a computerized voice, or what seems like a computerized voice.

Jason

November 3, 2022

I found the brief and concise nature of this short course to be beneficial. There was enough Pandas covered to take on my next project.

Shanmugam

November 2, 2022

Contents are bite sized and well prepared. Find narration in earlier series are too fast and hard to see the lab demo. Thanks

Krista

October 25, 2022

would be better with narration instead of just music, voice is mechanical which results in some mispronounced words

Darryl

October 13, 2022

I have a PhD in analytical methods and have been teaching statistics for 25 years. Never have I or anyone else I know taught that nominal (categorical or ordinal) variables are qualitative. This is utterly distracting at best. Because we enumerate them in some way, they act as quantitative variables. Yes, they are qualities, and some might consider them nonparametric, but to call them qualitative is inconsistent to the point of being somewhat ludicrous. The example of low, medium, and high income is a particularly problematic one, as such ordinal variables--more often than not--derive from a ratio level variable through ordinal coding. Further, there are considerable misuses of terms, such as analyzes for analyses, that create added distraction. I have only completed the first four lectures. If this is what I can expect going forward, I will have a very hard time recommending this course to others.

Mehmet

August 6, 2022

This short training is designed to teach very effectively within a short time. Positive: The lecture was great I think, because: - supplied datasets - links to the topics for the ones, who would like to go deeper into the theory - Pdf's for the presentation. I love this one, because I don't need to take notes while listening the video lecture. - Pre-filled python codes. This is great, because it includes many examples and excersizes for students. Negative: The voiceover is from a machine I think. A real human voice would be preferred, but I guess machine voiceover is still better than a hearing a human with bad accent.

Mark

June 17, 2022

Setup of Jupyter was not clear - where to find the Lab1_Dataset (was under resources later (in Step7) when the instructions are explained in Step 2 or 3. Also wasn't clear that you don't need to create the account in step 5 (that website no longer works) if you have installed Anaconda

Subhojit

June 16, 2022

It would have been better if you had taken a dataset and shown how the EDA is done for a ML project. Eg. if there is a categorical variable, with 1 or 2 categories with 80% of the data then how to deal with it. What would be our logic to club them and how. Just my personal suggestion.

Zakaria

March 2, 2022

This was a very useful course where i learned the Basics of how to use Python in Statistics and the functions of Python Libraries .

Yandisa

February 18, 2022

I found the course real amazing. I thought your explanations were on point and it was showing practical everything you explained mathematically

Clint

January 10, 2022

Most of the sections were quite informative and within reason of that which I expected to see. The sections which I would like to see done differently were parts 1 & 2 of EDA. I would like to have seen more application worked in with the lecture as opposed to the uninterrupted lecture of extended duration. The content of the EDA performance was interesting but was unfortunately quite a bit to take-in without the opportunity to apply in practical exercises. I think if there was an even distribution of application exercises in the EDA sections, content comprehension and retention would increase significantly. The way in which this was done in sections 1-5 was quite helpful. Application exercises are key.

ASIFIWE

September 15, 2021

This course provide great intuition of data analysis with python. it is great to start with to increase the required knowledge as data analyst.

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