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LifestyleIntroduction to Data Science using Python (Module 1/3) (Udemy.com)
Learn Data science / Machine Learning using Python (Scikit Learn)
Created by: Rakesh Gopalakrishnan
Last updated March 2018
Our take
Based on the ratings of 7,661 students, a sample of their written reviews and the syllabus, as the course stood in March 2018. No course pays to be reviewed.
This is a free, two and a half hour orientation to data science rather than a course that teaches it in any depth. Rakesh Gopalakrishnan spends the first two sections on what data science is, where it sits in the business intelligence stack, and getting Anaconda and Jupyter installed. The last section, about 104 minutes across six lectures, walks through KNN, logistic regression, train-test split and k-fold cross validation in scikit-learn. It is module one of three, and it says so up front. The description is honest: a 101 level course meant to raise curiosity, not to go deep on any algorithm.
Reviewers like how clearly the concepts are explained and say the demos in Jupyter make the ideas concrete. Several describe themselves as complete newcomers, a fresher or someone with no statistics background, and they say it delivered the gentle start they wanted. The complaints are about depth and age. A two-star reviewer found it very basic and expected model evaluation metrics like ROC AUC that never appear, and a four-star reviewer asks the instructor to go a little deeper. The videos date from March 2018, and one reviewer hit ModuleNotFoundError and ImportError problems following the Anaconda setup, which is likely to get worse as package versions drift.
Value is easy to judge because it costs nothing. With 12 lectures and no quizzes, exercises or certificate, it is an afternoon, not a commitment. Next to the full bootcamps it is listed alongside, this is a sampler to help decide whether the field interests you before committing to one of them. Take it if that is the question. If you already know what a train-test split is, skip straight to a longer course.
Pros
- Free, and only 2.5 hours across 12 lectures, so it is a low-risk first look at the field
- Reviewers say the explanations are clear and easy to follow for people with zero background
- Hands-on demos in Jupyter cover KNN, logistic regression, train-test split and k-fold cross validation
- Explains where data science fits in the business intelligence stack and what the job actually involves
Cons
- Last updated March 2018, and one reviewer hit import errors following the Anaconda setup
- Very shallow by design: no evaluation metrics like ROC AUC, no quizzes, exercises or certificate
- Only module one of three, so it stops just as things get interesting
Really, I like the way he taught the concept in easy way. But I want you to go little deeper to more understanding.
Genuinely for absolute beginners with no coding or stats background; anyone past the buzzword stage will outgrow it in an hour.
What you will learn
- Understand the basics of Data Science and Analytics
- Understand how to use Python and Scikit learn
- Get a good understanding of all buzz words like "Data Science", "Machine learning", "Data Scientist" etc.
Course content
3 sections · 12 lectures · 2.5 hours of video
- 1Introduction 1 free preview2 lectures · 14 min
- 2Getting setup with Tools4 lectures · 34 min
- 3Getting Deeper into Machine learning frameworks and algorithms6 lectures · 1.7 hours
Who it is for
The instructor says it suits
- Anyone who wants to learn about Data Science from absolute scratch.
- Anyone who wants to switch or make a career in Data Science and Analytics
- Anyone who is curious to know what is Data Science and what does a Data Scientist do in his/her day job.
What you need before you start
- This course does not have any pre-requisities. All you need is a Windows or a MAC machine.
Quality Score
No CourseDuck member has rated this course yet. Taken it? Give each part a thumbs up or down.
Overall Score : 88 / 100
Course Description
Are you completely new to Data science?
Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?
Do you want to start or switch career to Data Science and analytics?
If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover in-depth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.
This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don't go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.
The other modules will cover more complex concepts.
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Instructor Details
- 4.4 Rating
7,661 Reviews
Rakesh Gopalakrishnan
I am a coder, manager, educator and a gamer. I love data and analytics. In my day job, I work with database technologies including SQL , Big Data and Tableau. I am passionate about technologies and love coding and managing teams. In my spare time I like to teach Big Data analytics, Databases, Programming etc. I am currently working on certain machine learning and Data Science projects and love to explore more in the Statistics field.
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Reviews
By Pankaj Totaram Sarkate on 12/7/2025
according to the Headline of this course I thought I will provide some additional information. It was a very basic course. I was expecting roc_auc for model accuracy
By Dorji Wangchuk on 9/16/2025
Very clear and well articulated lecture. Learned many things from the course. I have to do further learning to get full picture and more understanding of the course. The course is well above my expected level. Thank you
By Vincent Nnebedum on 11/11/2022
The course and the delivery were okay for me, except that because I am not data science or statistics person, and needed more time to flow. Time to read the refences are enormous. I also had some challenges when carrying your steps on my own. A couple of error messages (e.g. ModuleNotFoundError, ImportErrors etc) which I think is from the Anaconda distribution pack downloaded (e.g Jupyter bundle) that is incomplete. Thanks
By Chandranshu Pandey on 6/8/2021
Rakesh did a fantastic job of doing justice with course and generate enough interest in new comers to data analytics. He guided audience to achieve some satisfaction by following his directions and use basic models. I am now looking forward to his other courses.
By Janiel on 6/5/2021
I am immensely happy about this course, It actually gives what I need as a Fresher. I can say that this course is one of few courses that made me come halfway to finish., I am loving this course. It is very understandable, neat and helps me explore next as I can understand basics well here. Thanks Rajesh
By Vaithianathan Ramanathan on 7/13/2020
Yes this course was a pretty good fit for me. I work in the Oil and gas industry. I generate lot of data every data, I am trying to develop my data science skill set and find out how I could apply in my domain. The course was very interesting, all concepts explained clearly. Look forward to the next courses.
By Wajiha Saeed on 6/4/2020
It is a good basic start, but seems to be incomplete. It did not go far enough on algorithms. Couldn't find the third module that was suppose to cover that.
By Sandip Majumder on 6/2/2020
Nicely organized course. I am an experienced database developer, now working in project management. Wanted to know about data science. This is a great course that got me hooked into this subject. It was so engaging that I finished the course in 5 days. Usually I don;t feel very motivated if the courses are not engaging.
By Dennis Brininger on 5/15/2020
the tempo is good and I like that you are keeping the code simple. You have referred to a forum but I don't know where that is, your site or Udemy? I look forward to your next 2 modules on Intro to Data Science using Python.
By Dario López Portilla on 10/23/2018
I really learn a lot with Rakesh, he explain every single point clearly and you can follow him. This is a bit difficult area and every help is welcome. Thanks you Rakesh, Keep it going !! Best Regards, Darío
- Video Duration:
2.5h - Price: Free 11.99 Track Price
- Provider: Udemy
- Updated:
March 2018 - Level:
Beginner - Language:
English - Certificate:
No - Lectures:12 in 3 sections
- Students:168,500
- Subtitles:1 languages
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