Published on July 9th, 2019
Hollywood rules our hearts and that is probably because of the amazing sci-fi effects that they show in each movie of theirs. Data science is the stream that makes it possible and this has led to an increase in its popularity as a career option. In simple terms, if we try to explain Data Science it is the use of tools, algorithms, and machine learning principles to find out patterns from the existing data available.
In other words, scientists working on the same, use a multitude of techniques in order to study the data and generate results. It is not just one realm that we are talking about here. There are multiple disciplines that come together here, analyze the data and provide substantial results.
In the beginning, these tasks were assigned to mathematicians and statisticians but the trend seems to have changed now. Now data experts use machine learning and artificial science to find out what information the data has to provide.
This has led to a sudden surge in the way people perceive data science as a career choice. To compensate a large number of free online courses have cropped up which make you aware of every aspect related to the field.
Here, we share with you 12 best free online courses on data science. After careful analysis, we have only picked those courses which according to us provide sufficient exposure to the student. Along with we also share the probability of getting a great placement after joining this field hence read on.
It is a ten-course introduction to data science, developed and taught by leading professors. The course is offered by Johns Hopkins University on the Coursera platform.
What You Will Learn:
- Use R to clean, analyze, and visualize data.
- Navigate the entire data science pipeline from data acquisition to publication.
- Use GitHub to manage data science projects.
- Perform regression analysis, least squares, and inference using regression models.
Skill You Will Gain:
- Machine Learning
- R Programming
- Regression Analysis
Earn A Certificate
When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.
In this course, you’ll get an introduction to Data Analytics and its role in business decisions. You’ll learn why data is important and how it has evolved. You’ll be introduced to “Big Data” and how it is used. You’ll also be introduced to a framework for conducting Data Analysis and what tools and techniques are commonly used. Finally, you’ll have a chance to put your knowledge to work in a simulated business setting. This course was created by PricewaterhouseCoopers.
Skills You Will Gain:
- Data-Informed Decision-Making
- Big Data
- Data Analysis
- Data Visualization (DataViz)
Explore data visualization and exploration concepts with experts from MIT and Microsoft, and get an introduction to machine learning.
This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence.
What You’ll Learn
- Explore the data science process
- Probability and statistics in data science
- Data exploration and visualization
- Data ingestion, cleansing, and transformation
- Introduction to machine learning
- The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning
This class will teach you the end-to-end process of investigating data through a machine learning lens, and you’ll apply what you’ve learned to a real-world data set.
This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.
Find out the truth about what Data Science is. Hear from real practitioners telling real stories about what it means to work in data science.
Module 1 – Defining Data Science
- What is data science?
- There are many paths to data science
- Any advice for a new data scientist?
- What is the cloud?
- “Data Science: The Sexiest Job in the 21st Century”
Module 2 – What Do Data Science People Do?
- A day in the life of a data science person
- R versus Python?
- Data science tools and technology
Module 3 – Data Science In Business
- How should companies get started in data science?
- Tips for recruiting data science people
- “The Final Deliverable”
Module 4 – Use Cases For Data Science
- Applications for data science
- “The Report Structure”
Module 5 – Data Science People
- Things data science people say
- “What Makes Someone a Data Scientist?”
- This course is free.
- It is self-paced.
- It can be taken at any time.
- It can be audited as many times as you wish.
A Professional Certificate program is a series of online courses that help you become job-ready. Some Professional Certificates prepare you to launch a career in a specific field like IT support, while others help you to pass an industry certification exam. To begin, enroll in the program or choose a single course you’d like to start with.
Earn the Certificate: When you finish all of your courses, you’ll receive a shareable electronic Certificate that you can add to your resume and LinkedIn.
There Are 9 Courses In This Professional Certificate:
- What is Data Science?
- Open Source tools for Data Science
- Data Science Methodology
- Python for Data Science
- Databases and SQL for Data Science
- Data Analysis with Python
- Data Visualization with Python
- Machine Learning with Python
- Applied Data Science Capstone
- Taught by Feynman Prize winner Professor Yaser Abu-Mostafa.
- The fundamental concepts and techniques are explained in detail. The focus of the lectures is real understanding, not just “knowing.”
- Lectures use incremental viewgraphs (2853 in total) to simulate the pace of blackboard teaching.
Learn Python, R, SQL, data visualization, data analysis, and machine learning. Try any of its 60 free missions now and start your data science journey.
Full Course Catalogue
Parts of this course are based on textbook Witten and Eibe, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 1999 and 2nd Edition (2005), (W&E). The course will be using Weka software and the final project will be a KDD-Cup-style competition to analyze DNA microarray data. The course is organized as 19 modules (lectures) of 75 minutes each.
Detailed Course Outline
The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to making use of data. This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).
What You Will Learn
- How the Microsoft Data Science curriculum works
- How to navigate the curriculum and plan your course schedule
- Basic data exploration and visualization techniques in Microsoft Excel
- Foundational statistics that can be used to analyze data
This is the first stop in the Data Science curriculum from Microsoft. It will help you get started with the program, plan your learning schedule, and connect with fellow students and teaching assistants. Along the way, you’ll get an introduction to working with and exploring data using a variety of visualization, analytical, and statistical techniques.
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.