Top 10 MOOCs for Data Science………
Including all the individuals now attempting to move into the field of data science, it’s no wonder that “data science education” has become a pretty big commodity these days. Almost every week I hear about a new training program, a lot of camps, a concentration show, or a Massive Open Online Course (MOOC) promising to jump-start your career in this exciting field.
I understand how overwhelming all of these choices are to learners who are new to the area. Cognitive science has a term for making too many choices: over-choice or overloading. This will result in a decision-making paralysis. In this post, I want to help you resolve any decision uncertainty that you will face by providing a list of what I feel are the top MOOCs for data science in 2019.
The following list contains my best MOOC learning tools designed to open the door for you to join the field of data science.
Within this article, we have held just one course, writing a separate post for specializations or degrees relevant to data science. There are also some interesting potential courses on this list.
Some general guidance on the source details:
1. The degree of the course shall be determined by taking into account the criteria, the effort needed, and the length of the course.
2. All courses are focused on the basic history of statistics.
3. The courses are structured with a degree of experience, i.e. beginner courses are listed in advance of expert-level courses.
4. Tools are known to be programming languages or software tools used in the course.
Top 10 MOOCs for Data Science:
Retrieval technologies are commonly used in current applications, whether it’s web search, movie recommendation, document search. The algorithms you will learn include content-based filtering, collaborative user filtering, collaborative item-item filtering, dimensional reduction, and interactive critical-based recommendations.
- Below is a list of the courses included in the specialization: Evaluation and metrics for recommender systems:
1.Python Programming,
2.collaborative algorithms for a recommendation, and
3, CSci 2033, and CSci 3.
- Level: Beginners-Intermediate.
- Effort: 8–10 hrs/week.
- Status: Self-paced
- Duration: 8 weeks
- Prerequisite: None
- Tools: No restriction.
Applied Data Science with Python Specialization by the University of Michigan
— A5-course series that specializes in data science using the Python language and related libraries. If you also have an interest in data engineering, which is mostly based on Python, this would be a good course to take. The courses are offered on the Coursera website. The show takes 3–4 months to complete with a time commitment of 3–4 hours a week. Learners can inspect the content of the course for free or pay a charge of $49 a month to view the material for receiving a certificate.
Below is a list of the courses included in the specialization:
Guide to computer engineering and programming utilizing Python
- Applied visualization, charting and data representation in Python
- Applied Machine Learning in Python
- Applied Text Mining in Python
Applied Social Network Analysis in Python
Level: Beginners-Intermediate.
- Effort: 3–4 hours a week
- Status: Self-paced
- Duration: 3–4 months
- Prerequisite: None
- Tools: No restriction
It’s a perfect course to cover all the bases of bioinformatics. It is a two-part course dealing with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis, and metagenomics. Later, in Part II, it includes pattern search, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions.
Below is a list of the courses included in the specialization:
Genetic Analysis
- Bioinformatics Analysis
- Evolution
Comparative Genomics
Level: Intermediate-Expert Effort: 12–18 hrs/week
- Status: On-demand Duration: 10 weeks
- Prerequisite: None Tools: No restriction
Neural Networks for Machine Learning (University of Toronto)
If you want to explore the current “hot topic” of deep learning, you should explore this course. Taught by Prof. Geoffrey Hilton, whose work has revolutionized the field. The course includes all sections right from the perceptron to the auto-encoders. The course will clarify the new learning techniques that are responsible for recent developments in the field of the neural network; Include useful new procedures for learning multiple layers of non-linear features, and give you the skills and understanding needed to apply these procedures in many other domains. If you want to know in-depth, consider following these courses: CS224d: Deep Learning for Natural Language Processing and Nvidia’s Deep Learning Courses.
• Below is a list of the courses included in the specialization:
- Recurrent neural networks
- Stacking RBMs to make Deep Belief Nets
- Machine learning and neural nets
- Hopfield nets and Boltzmann machines
- The backpropagation learning procedure. Learning the weights of a linear neuron
- Optimization: how to speed up learning. We investigate mini-batch gradient descent as well as adaptive learning speeds.
Recent applications of deep neural nets
We Level: Intermediate-Expert
- Effort: 7–9 hrs/week
- Status: Archived
- Duration: 8 weeks
- Prerequisite: None
- Tools: Octave.
If you’re looking to build the next distributed network, which is expected to turn big data into insights, this is the path you should follow. The course involves MapReduce, PageRank, locality-sensitive hashing, and many more sophisticated algorithms. It’s a lengthy course that requires a great deal of effort and coding. While encoding assignments are optional, it is highly recommended that they are completed.
• Below is a list of the courses included in the specialization:
- MapReduce
- Link Analysis — PageRank
- Locality-Sensitive Hashing — Basics + Applications
- Distance Measures
- Data Stream Mining
- Analysis of Large Graphs
- Recommender Systems
- Dimensionality Reduction
- Clustering
- Computational Advertising
- Support-Vector Machines
- Decision Trees
- MapReduce Algorithms
- More About Link Analysis — Topic-specific PageRank, Link Spam.
More About Locality-Sensitive Hashing.
Level: Expert Effort: 8–10 hrs/week
- Status: Upcoming Duration: 7 weeks
- Requirements: Calculus, Data Structure
- Tools: C++ or JavaTools: C++ or Java.
Convex Optimization (Stanford University)
Advance course, if you’re interested in optimization problems. This course focuses on the identification and resolution of convex optimization problems that occur in applications. The syllabus includes convex sets, functions, and optimization issues; fundamentals of convex analysis; least square, linear and quadratic programs, semidefinite programming, minimax, extreme length, and other issues; Optimum conditions, duality theory, alternative theorems, and implementations; indoor point methods; signal processing systems, statistics and computer learning, control, and mechanical engineering, digital and analog circuit design and finance.
Below is a list of the courses included in the specialization:
Programming Methodology
- Programming Abstractions
- Programming Paradigms
- Machine Learning
Introduction to Robotics.
Level: Intermediate-Expert Effort: 8–10 hrs/week
- Status: Archived
- Duration: 10 weeks
- Prerequisite: Probability, Optimization
- Tools: Matlab.
Process Mining: Data science in Action (Eindhoven University of Technology)
Process Mining: Data science in Action (Eindhoven University of Technology)
Source mining is a missing link between model-based source analysis and data-driven analysis techniques. Via practical data sets and easy-to-use applications, the course offers data science information that can be applied directly to the study and development of processes in several domains. The course discusses the primary research techniques used in process mining. Participants must study different algorithms for process exploration. They can be used to automatically learn process models from raw event data.
Below is a list of the courses included in the specialization:
Petri Net
- Process Modeling
- Process Mining
Data Mining.
Level: Intermediate-Expert
- Effort: 4–6 hrs/week
- Status: Upcoming
- Duration: 8 weeks
- Prerequisite: None
- Tools: Prom, Disco.
Process Mining: Data science in Action (Eindhoven University of Technology)
Process Mining: Data science in Action (Eindhoven University of Technology)
Source mining is a missing link between model-based source analysis and data-driven analysis techniques. Via practical data sets and easy-to-use applications, the course offers data science information that can be applied directly to the study and development of processes in several domains. The course discusses the primary research techniques used in process mining. Participants must study different algorithms for process exploration. They can be used to automatically learn process models from raw event data.
Below is a list of the courses included in the specialization:
Petri Net
- Process Modelling
- Process Mining
Data Mining
Level: Intermediate-Expert
- Effort: 4–6 hrs/week
- Status: Upcoming
- Duration: 8 weeks
- Prerequisite: None
- Tools: Prom, Disco.
Data Analysis: Take It to the MAX (DelftX) (1 Sep 2015 onwards)
Data Analysis: Take It to the MAX (DelftX) (1 Sep 2015 onwards)
Even in the age of big data, there is a large number of data analysts who rely heavily on spreadsheets to gather insights and to keep them relevant. It is an excellent course for those who want to use excel to improve analytical skills. You’ll take a deep dive into data analysis with spreadsheets: PivotTables, Vlookups, named ranges, what-if analyses, make perfect graphs-Each of them will be discussed in the first weeks of the course. After that, you should examine the consistency of the spreadsheet model and, in particular, how to ensure that your spreadsheet stays error-free and reliable. Finally, you’ll also look at how Python, a programming language, can help us analyze and manipulate data in spreadsheets.
Below is a list of the courses included in the specialization:
Introduction to Computer Science and Programming Using Python
- CS50’s Introduction to Computer Science
- Machine Learning for Musicians and Artists
- Algorithms, Part I
- Cryptography I
- CS188.1x: Artificial Intelligence
- Software Security
Introduction to Artificial Intelligence.
Level: Intermediate
- Effort: 4–6 hrs/week
- Status: Upcoming
- Duration: 8 weeks
- Prerequisite: Basic Spreadsheet Exp.
- Tools: MS-Excel, python.
Coding the Matrix: Linear Algebra through Computer Science Applications (Brown University)
Coding the Matrix: Linear Algebra through Computer Science Applications (Brown University)
Linear algebra is an important building block not only for computer science but also for machine learning, graphics, and statistics. This is a fantastic course that guides you through actual examples and excellent python assignments. You can write programs that integrate basic matrix and vector functionality and algorithms, and use them to analyze real-world data to perform tasks such as two-dimensional graphic transformation, face recognition, image transformations such as blurring and edge recognition, eye perspective elimination, tumor classification as malignant or benign, integer factorization, error correction codes, and hidden sharing. Another, the simpler course is LAFF at the University of Texas Austin.
Below is a list of the courses included in the specialization:
The Function
- The Field
- The Vector
- The Vector Space
- The Matrix
- Dimension
- Gaussian Elimination
- The Inner Product
Orthogonalization
Level: Beginner-Intermediate
- Effort: 10–14 hrs/week
- Status: Archived
- Duration: 10 weeks
- Prerequisite: None
- Tools: Python.
CONCLUSION:
I hope that these learning opportunities can help you narrow down the multitude of alternatives that can be very daunting, and I hope that you will gain some great new information that you can apply to your career.
Happy learning to read!