Companies across all the industries in the world are always looking for data science personnel to help them garner insights from big data. The hiring experts are constantly on the lookout for personnel with high skills regarding programming, data mining, statistical modelling etc.
With the huge gap existing between required skills and talent available, these industries have become more resilient towards finding skilled data scientists and scraping out the less talented ones. One way for the people going into data science to enhance their knowledge is taking up the data science courses online, these data science courses help one to learn about the sector and acquire the in-demand skills.
Below we have listed some of the best free online data science courses available: (List is in random order)
Data Science Essentials
From: Microsoft through edX.
Duration: 6 weeks.
Cost: Free/ ₹7, 076 to add a verified certificate.
Instructors:
Senior Content Developer
Microsoft Learning Experiences
Managing Director
Quantia Analytics, LLC
Associate Professor
MIT and Duke
Prerequisites: Basic mathematics and fundamental knowledge about R or Python.
About the course:
Microsoft offers this course as a part of Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence. This course provides fundamental concepts in data acquisitions, exploration preparation and visualisation with practical application-oriented examples.
edX offers financial assistance for the course for anyone who wants to earn the Verified Certificates.
Concepts taught:
- Exploring data science
- Data exploration and visualisation
- Probability and statistics in data science
- Introduction to machine learning
- Data ingestion, cleansing, transformation
- Use R, python and Microsoft Azure Machine Learning with the other course content
Apply here. (The next dates are yet to be announced)
Data-Driven Decision Making
Offered by: PwC through Coursera.
Duration: Approximately 11 hours to complete.
Cost: Free
Instructors:
Alumni / Former Principal
PwC.
About the course:
This course is the part of PwC’s Data Analysis and Presentation Skills: the PwC Approach Specialisation.
This course gives you an introduction to Data Analytics and its applications in business decisions, introduction to Big Data and how it is used, introduction to frameworks for Data Analytics, tools and techniques used for it. Not only does this course contain the data analytics concepts, but also you can test your knowledge in a simulated real-world business setting.
This is a beginner course that is offered in Korean, French, and Japanese languages apart from English and is self-paced with a flexible deadline.
Concepts covered:
- Introduction to Data Analytics
- Technology and types of data
- Data analysis techniques and tools
- Data-driven decision-making project
Apply here.
CS109 Data Science
Offered by: Harvard
Prerequisites: Python (Only Python is used throughout the course), fundamental knowledge of how the data science libraries work.
About the course:
This data science course offered by Harvard isn’t on a platform like edX or Coursera and doesn’t provide any certification. This might be one of the best courses for beginners to get started with data science and is completely free.
The course contains a list of videos, lecture slides, lab videos & notebook and is a 13-week intensive program that wrapping up course content: (Listed in order of the course chronology)
- Web Scraping, Regular Expressions, Data Reshaping, Data Cleanup, Pandas.
- Exploratory Data Analysis
- Pandas, SQL and the Grammar of Data
- Statistical Models
- Storytelling and Effective Communication
- Bias and Regression (With more regression)
- Classification, k-Nearest Neighbors, Cross-Validation, Dimensionality Reduction, PCA, MDS
- SVM, Evaluation
- Decision Trees and Random Forests
- Ensemble Methods and Best Practices
- Best Practices and Recommendations
- MapReduce, Spark
- Bayes Theorem, Bayesian Methods and Text Data
- Clustering
- Effective Presentations
- Experimental Design
- Deep Networks
- Guest Lecture: Building Data Science
Start here.
Data Science Foundations
Offered by: IBM on their portal.
Duration: Approx. 13-20 hours
Course Staff:
Introduction to Data Science:
Shingai Manjengwa (@Tjido) - CEO of Fireside Analytics, Inc.
Data Science tools: Saeed Aghabozorgi - PhD is a Data Scientist in IBM
Polong Lin: Data Scientist at IBM in Canada
Data Science methodology: John B. Rollins, Ph.D., P.E., - Data Scientist at IBM
Polong Lin, Data Scientist and Lead Data Science Advocate at IBM in Canada
Prerequisites: R programming language fundamentals (recommended)
About the course:
IBM offers this data science course for free on its platform, where they offer a learning space for many other courses like these and gives badges for your portfolio. This course is suitable for both intermediate and beginners.
The course contains two badges; one covers the basics of data science and the other badge you get after completing every course on the program.
The course contents are:
Apply here.
Machine Learning
Offered by: Stanford on Coursera
Duration: Approx. 56 hours for completion.
Instructors:
CEO/Founder Landing AI; Co-founder, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain.
Prerequisites: Not required, (although basic maths and programming languages recommended)
About the course:
Machine learning is a part of data science and helps in drawing aspects from the statistics and algorithms to extract data from multiple sources, hence understanding ML will help with data science applications. This course, in particular, entails the essential and effective machine learning techniques and some of the best practices in the world.
The course provides a basic introduction to machine learning, data mining and statistical pattern recognition.
The course content includes:
- Introduction
- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Logistic Regression
- Regularization
- Neural Networks: Representation
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning
- Dimensionality Reduction
- Anomaly Detection
- Recommender Systems
- Large Scale Machine Learning
- Application Example: Photo OCR
By the end, you will gain skills like logistic regression, artificial neural network, and ML algorithms.
Apply here.
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