Wikipedia defines Data Science as a field focused on extracting knowledge and insights from data by using scientific methods. In simple words, you have data, procedures, algorithms, and tools. You just need to extract knowledge from it. To do that efficiently, there’s a workflow that you must follow. The preliminary skill required to learn advanced level data science is knowledge of python, which is an interpreted high-level general-purpose programming language. Data is an integral part of every application, every website and theoretically everything on the internet. Therefore, it is evident that applications of data science are uncountable and it is impossible to make a list of uses of data science.
It would not be false to say that data science is used in everything in today’s world. Learning such a thing, that has a list of uses that never ends is extremely clever. The marketplace for data scientists is huge. Doing this course will help you to be an eligible candidate for many jobs. You might be wondering; it must cost a fortune to be trained for data science. Let me tell you, you can learn this without any fee. There are numerous courses on data science that are available for free, moreover are approved by Forbes.
1. A Crash Course in Data Science by Class Central
This is a multiple module course, each module covers different topics in order to give you full details of data science. This one-module course of the duration of one week is of the Executive Data Science Specialization. This is an intensive introduction to what you need to know about data science itself. You will learn important terminology and how successful organizations use data science from Jeff Leek, Brian Caffo and Roger Peng. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. This is a focused course designed to rapidly get you up to speed in the field of data science. The key features of the course are Free Online Course (Audit), present in English, Paid Certificate Available, 1 week long, and you get 7 hours’ worth of material.
2. Introduction to Data Science by Alison
This course is an introductory course to Data Science. You will be introduced to the field of data science and the methodologies used in the data science process. You will also learn about machine learning and some of its algorithms. The course starts off by introducing you to data science, where you will learn that data science is an interdisciplinary field that uses scientific processes and systems to extract knowledge or insights from data in its various forms. After which, you will be introduced to some of the main algorithms that are used in machine learning. You will learn about regression and classification, and about two commonly used clustering algorithms. You will also be introduced to Azure Machine Learning (ML) Studio and why you would use Azure ML Studio for your data science projects. The prerequisites of the course are that the student needs to have basic knowledge of python. In order to get certified after completion of the course, one needs to pass all the assessment courses with an 80 per cent and above score. Alison graduates get an option to get either an immediately downloadable certificate or a hard copy shipped for free.
3. Python for Data Science by EdX
This a free course offered by edX but for a verified certificate one needs to pay an amount of 350 U.S dollars. The course duration for this course is ten weeks with an effort needed of about ten hours per week. This is a self-paced coursed offered by The University of California, San Diego. The instructors for this course are Leo Porter (Assistant Teaching Professor, Computer Science and Engineering. UC San Diego) and Ilkay Altintas (Chief Data Science Officer at the San Diego Supercomputer Centre. UC San Diego). The prerequisite of the program is a high school or undergraduate equivalent knowledge of Python or any programming language. The course covers the basic process of data science, Python and Jupyter notebooks, an applied understanding of how to manipulate and analyse uncurated datasets.
4. Python for Data Science: Intermediate by Dataquest
Python Dataquest provides two courses on data science. One is fundamental and the other as intermediate. As the name suggests the main difference in both the courses is the level of the course. In this course, you will learn how to clean and prepare data in Python, a critical skill for any data analyst or data scientist job. To do this, you will need to dig into some real-world data about the artwork at the Museum of Modern Art and learn to manipulate text, clean messy data, and more. You’ll also get to practice summarizing numeric data and formatting strings in Python. Next, you will dive into object-oriented programming (OOP) and how it relates to data science. Then, you’ll apply this new understanding by building your own class. Finally, you will learn how clean, standardize, and analyse data and time data using Python’s DateTime module.
At the end of the course, you will need to combine all the skills you learned to create a portfolio project focussed around Hacker News post titles to find out what types of posts are most likely to be successful at what times. By the end of this course, you’ll be able toClean and analyse text data, Understand object-oriented programming in Python, Work with dates and times. All this information was about the intermediate course, while the fundamental course covers only the fundamentals of data science.
5. CS109 Data Science by Harvard University
This course is offered by John A. Paulson School of Engineering and Applied Science which is under Harvard University. This Lecture dates back to 2015 and that makes it come under so many courses which are updated. The courses are based on the books, Python for Data Analysis, O’Reilly Media, Machine Learning for Hackers, O’Reilly Media and Data Science for Business. The instructors are from the school’s department of computer science and statistics, who are Hanspeter Pfister (Computer Science), Joe Blitzstein (Statistics), Verena Kaynig-Fittkau (Computer Science). The course covers data munging, scraping, sampling, cleaning in order to get an informative, manageable data set; data storage and management in order to be able to access data – especially big data – quickly and reliably during subsequent analysis; exploratory data analysis to generate hypotheses and intuition about the data; prediction based on statistical tools such as regression, classification, and clustering; and communication of results through visualization, stories, and interpretable summaries.
6. Introduction to Data Science in Python by Coursera
This is an introductory course to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the NumPy library. The course will introduce data manipulation and cleaning techniques using the popular python panda’s data science library and introduce the abstraction of the Series and Data Frame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses.
7. Introduction to Data Science Using Python by Udemy
This is 4.4 stars, three-part course, that is the program contains 3 such courses. This course does not have any pre-requisites. All you need is a Windows or a MAC machine. The course is free and therefore students gets access to only video resources. The course covers understanding of the basics of Data Science and Analytics, understanding how to use Python and Scikit learn, understanding of Data Science, Machine learning, Data Scientist etc.
8. IBM Data Science Professional Certificate by Coursera
This course offered by IBM (International Business Machine)is a program consisting of 9 online courses that will provide you with the latest job-ready tools and skills, including open-source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modelling, and machine learning algorithms. You will be able to learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. When you successfully complete these courses, you will have a built portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in data science. The instructors for the course are well experience postgraduates from around the world.