We are living in an era of open-source technologies. With open source becoming a talk of the past as now we have also entered the age where Anyone can learn Anything from Anywhere across the globe (The AAAs). With so many skills and jobs being created in the industry today, there is a clear gap between what we studied and what we are working on. To fill this gap is where the internet has a major role to play.
As we move ahead, we dive deep into the free courses out there to learn Python, R language and Data Science.
What are R and Python?
When comes to programming you already have heard about python as a popular language. Programming in R, which started as ‘r-project’ is language and an environment used extensively in statistical computing and graphics. Data science thrives on programming and modelling of the solution and is built and fine-tuned by programming. Many professionals prefer python and some R as programming. So, it is expected out of a Data scientist to have a tech-proficiency of any of the two languages.
Now let us explore how you can start this journey of learning these open-source technologies for free.
Free resources for Python:
1. Python Fundamentals for Beginners (Great Learning)
The Great Learning Academy offers a free online course by which one can get familiarised with python. This course is targeted at beginners. Detailed lectures have been provided, which are easy to comprehend making it easier for beginners to grasp. The quiz is provided at the end of the topic to gauge and consolidate the knowledge. One important thing here to note is it provides in-depth knowledge and hands-on experience on advanced concepts of python programming.
2. w3schools Python Tutorial
Well, this platform has way more than just python. They have exercises, examples and a bunch of tutorials ranging from some of the basic concepts to some of the advanced ones as well. What makes it more interesting is they have touched upon some of the popular python packages such as Numpy, Matplotlib, Scipy and Machine learning concepts as well. This will not guard you to a larger project but is a great launchpad.
3. The official Microsoft course on learning python is on Youtube.
This is one of the best resources on YouTube and is completely free. The course is well structured and starts from scratch on how to set up an environment and goes on to more advanced concepts towards the end of it. If you are a visual learner, consider hitting this up. They give small exercises in the middle and go over the solutions in the next video. It might go a bit too fast in the beginning but it can become your primary resource easily.
4. Automate the boring stuff with python
Automate the boring stuff by a python! Is an online project which has a free book by the name. written by Al Sweigart this website has the whole readable copy of the book in hyperlinks. The best thing about this is it has touched upon the practicality of python as a language. With some cool examples of web scraping, sending email and text messages this book adds a ton of interest to the learners. The book also talks about the type of errors and has a good emphasis on debugging as well. Though this will cover the tip of the ice-burg getting to the many tips is what becomes important to many enthusiast learners and this book does the job well.
5. Python.org
Yes, last but not least we should not forget the core of python that is the documentation in python.org. This documentation is written by the people who made it and given an extremely detailed manner. The tutorial section in python.org has different levels classification as Beginner, Moderate, Advanced and General. This will help everyone democratize the learning process and raise the learning curve to rise. It has also the examples given to add to the weightage. There is more to it, with the Audio Visuals and a Podcast on Python and about those who make python great. This might appeal a little to the beginner initially as it has poor UI as well.
Learn R for free:
Let us now walk through the free resources to learn the mathematicians programming language.
1. R Programming by Johns Hopkins University
This is one of the best courses out there in Coursera, starting up with the content first will give you a rough idea of what to expect, which is a must-go thru for every course you select. So, this is in Coursera and you won’t be charged anything to learn but if you want to take the certification there are some fees. As the course is introductory and enough for R language and its usage in Data Science, you can finish the course quickly. In this course, you will learn about the historical aspect as well as where the R currently stands.
2. R Basics — R Programming Language Introduction
This is another Udemy free R programming course that is ideal for learning R programming from the ground up. There are over 4 hours of content and two articles in this course. Its step-by-step method is ideal for novices, and Martin has done an excellent job of keeping this course interactive and straightforward. You’ll begin by installing the R and RStudio interfaces, as well as add-on packages, and learning how to use the R exercise database and R assistance tools. Following that, you’ll discover other ways to input data, as well as the basics of coding, such as fundamental R functions, loops, and other graphical tools, which is R’s strength.
3. Learn Data Science With R
This is an R programming course that will teach you how to do data science with R. It spans over 8.5 hours of content and covers the majority of R ideas relevant to data scientists. You will study the fundamentals of Data Science, such as what is Data Science, data types, Vectors, Factors, Lists, Matrices, Data Frames, and reading data from files using RJDBC, RODBC, and ROracle, as well as what is Data Science. Ram Reddy, the instructor, is a data scientist and the creator of RRITEC, a firm dedicated to assisting scientists in better understanding and visualising their data.
4. Learn R for Business Analytics from Basics
R is growing on business analytic platforms apart from statistics, Machine learning and Data Science. Giving strong competition to the giants like SAS, SPSS and other business analytics packages, this has stood out as a strong contender. This will help you leverage R’s capability to learn Business Analytics. This course is a hands-on journey starting with how to import Data in R, plot charts to analyse the model results. This is one of the courses to not miss.
5. R, ggplot, and Simple Linear Regression
This is an older but still popular free R programming course on Udemy that teaches you how to do Data Science with R. This course will teach you how to get started with R programming and how to use ggplot2, an outstanding graphics application for R. You’ll also discover Data Science concepts like the fundamentals of simple linear regression along the way. Anyone interested in R, ggplot, or data science can enrol in this course because there are no prerequisites. The course begins with an introduction to R and RStudio, followed by an explanation of R and ggplot abilities as they relate to comprehending linear regression.
Data science free courses:
1. Unnati Program | Intel
Renowned company for semiconductors, Intel has launched Unnati Program to equip with the right data-centric skills for engineering students. In addition to this Intel is planning to set over 100 Intel Data-centric labs in a year across the talent segments of engineering institutions and universities, focusing on research and innovation which is the core aspect. The program focuses on combining the Intel FPGA (field-programmable gate array) hardware and software to accelerate the processor-intensive task of data science, this will give it an added advantage. Intel co-branded certificates will be awarded to all the students at the end. So, to sum up, this course is a great opportunity for students who can work with the integration of both hardware and software easily without shedding much from their pocket, as we all know data science needs strong processing power.
2. Machine Learning for Beginners | Microsoft x MIT
Microsoft with a tie-up from MIT has launched its course for beginners. The 12-week course comprises 26 lesson curriculums. It teaches with the help of Scikit-learn on classic machine learning concepts. Covers are exhaustive and cover an introduction to the advanced concepts with good delivery. Introduction concepts such as introduction to Machine learning, History of ML, Fairness of ML, Techniques in ML, Intro to regression and more advanced concepts from classifications, clustering, NLP tasks, translation, sentiment analysis, forecasting etc.
3. AI Search Methods for Problem Solving | IIT Madras
Indian Institute of Technology, Madras course on AI hosted by the National Programme on Technology Enhanced Learning (NPTEL) platform will give you a good start for ‘Search methods’ of AI. It is designed and delivered by Prof Deepak Kehmanu of the Department of Computer Science and Engineering. Enrolment for the course is free and you may take the test for Rs. 1000 at the end to take up the test. If you want to learn from the faculty from the IIT directly this is a go-to course, which will offer you a unique topic with AI Search Methods.
4. Data Science: Machine Learning | Harvard x EdX
The machine learning course provided by Harvard in one of the MOOCs platforms Edx is the hot cake. It is most sought by the professionals as it emphasises more on the research part. With a major focus on data analysis and stats and aimed at bringing better projects out of it, this course stands out. The project of building a recommendation system for movies and teaching the science behind popular data science techniques. At the end of this course, one will be learning about the basics of ML, important algorithms of ML, performing cross-validation to deter overtraining, regularisation and its usefulness. It will take you an estimated eight weeks with a total spend of around two to four hours every week.
5. Data Science Specialisation | Coursera
This course from Coursera offered by John Hopkins University has helped learners launch their careers in Data Science. It is a Specialisation course that takes up to 11 months to complete. Takers of this course will be learning the R language from scratch. In its syllabus, you will learn to analyse and visualise data, navigate data science to the pipeline. Major focuses are given on the use of GitHub to manage the project which helps make the process more practical. Instructors of the course are Jeff Leek- Associate Professor of Biostatistics, Roger D Peng– Associate Professor of Biostatistics, and Brian Caffo– Professor of Biostatistics.
Now, at the end of the blog, you will be left with so many resources and ideas, we would suggest completing one course and going out and looking for their application in real-world use cases. This way you will be better off and have a practical approach to the things learnt. It is one thing that you learn and gets certified but it is a different thing to bring in you’re learning in building something that solves problems and is of practical use. we hope you have got something to take back.
Practising is as important as you completing the courses, so get your hands dirty! Practice Python or R and then implement the Data science models. When you’re running thru the courses make sure you do reverse engineer the project (going from end to the start!) and know all the libraries used, the code structure and flow properly. This exercise will help you build a project of your own. Having said all this, we want to wish you all the best on your journey
