The demand for Data Scientists has been growing rapidly. The classic academic channels alone are not able to fulfil this demand. Increasingly large number of self-taught individuals who are passionate about data are learning and bootstrapping their skills through online education platforms and contributing significantly to data science initiatives in many organizations.
There are numerous resources for learning data science online; Coursera being the most popular choice. Whether you are a college graduate who wants to pursue a career in data science or an experienced professional looking to transition to or advance your data science career, you can benefit immensely from the cutting-edge Data Science Courses and Specialization programs offered by top-notch Universities on Coursera platform. In fact, you can create your own self-paced Data Science degree curriculum on Coursera. This will allow you to build the necessary skill set without interrupting your career. With Coursera’s annual subscription service Coursera Plus, this is now easier and cheaper than ever before.
Since, Coursera Plus allows one access to over 3000 courses and specializations for a yearly fee of $399, you could get top-rated Data Science education at a cost of $1.09 a day. Using Coursera Plus subscription to supplement your Data Science journey can prove to be the best bang for your buck. In this article, we will demonstrate how you can create a Data Science curriculum with Coursera Plus.
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Knowledge of following topics is required to build the essential Data Science skills. All these can be easily acquired with Coursera Plus. You can customize this curriculum based on your current skill level and goals.
- Mathematical Foundations
- Programming
- Data Preparation & Analysis
- Databases and SQL
- Statistics & Probability
- Data Visualization
- Machine Learning
- Deep Learning
- Big Data
Mathematical Foundations
Mathematics is the bedrock of Data Science. Most models and constructions in Data Science have mathematical underpinning. It is therefore important to understand maths basics that make things work under the hood. Following essential math skills will help you to become a better data scientist in all aspects:
- Linear Algebra
- Multivariable Calculus
- Statistics
Listed below are Coursera Courses and Specializations that you can take up to learn or brush up the above Math basics.
Data Science Math Skills by Duke University
This course by Duke University is a primer on core math concepts that data science is built upon. It introduces learners to the vocabulary, notation and concepts of algebra and pre-calculus, without any extra complexity. It is a true introductory course covering basic math that prepares learners for more advanced material. There is no prerequisite for taking this course.
The course is organized as 4 weekly modules that cover the following topics:
- Set theory, including Venn diagrams
- Properties of the real number line
- Interval notation and algebra with inequalities
- Sigma notation, Jagged S Symbol
- Statistical quantities, like mean and variance
- Math on the Cartesian (x,y) plane, slope and distance formulas
- Graphing functions and their inverses on the Cartesian plane
- The concept of Derivative
- Exponents, logarithms, and the natural log function
- Probability theory – definition and rules
- Binomial theorem and Bayes’ theorem
Level : Beginner
Duration : 13 hours
Rating : 4.5
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Introduction to Calculus by University of Sydney
This is one of the topmost Math courses on Coursera that introduces learners to the key foundations for applications of mathematics in data science and engineering. There are five modules in the course that focus on ideas and historical motivation for calculus. All throughout the course a good balance between theory and application is maintained with cascades of formative exercises in each module and a final summative quiz at the end of each module.
Following concepts in foundational mathematics are covered in this course:
- Familiarity with key ideas of precalculus, including real number line, decimal expansions and approximations
- Manipulation of equations and inequalities
- Sign diagrams
- Use of the Cartesian plane
- Functions – polynomial functions; exponential and logarithmic functions; trigonometric functions; inverse circular functions etc.
- Differential Calculus
- Preliminary methodology of tangents and limits
- Properties and applications of the derivative
- Methods of the integral calculus
- Properties of the Odd and even functions; and the logistic function
Level : Intermediate
Duration : 59 hours
Rating : 4.8
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Matrix Algebra for Engineers by Hong Kong University of Science and Technology
This course focuses completely on matrices, and concisely covers the linear algebra that an engineer should know. There is no discussion on derivatives or integrals in this course, but it is a good pick to understand and master the basics of matrix algebra that is really useful to make sense of why algorithms work the way they do.
The course is structured as 4 weekly modules that comprise of 38 short lecture videos in total, with a few problems to solve after each lecture. There is an abundance of assessment and practice quizzes, solutions to which can be found in instructor-provided lecture notes.
Topics covered in this Course are:
- Matrices – definition, operation on matrices, transposes and inverses
- Special matrices such as the Identity and Zero matrix
- Orthogonal and Permutation matrices
- Systems of Linear Equations
- Gaussian Elimination
- Compute a matrix inverse
- LU decomposition of a matrix
- Vector Spaces
- Linear Independence
- Span, Basis and Dimension
- Gram-Schmidt Process
- Four fundamental subspaces of a matrix
- Matrix formulation of the least-squares problem
- Two-by-Two and Three-by-Three Determinants
- Laplace Expansion
- Leibniz Formula
- The Eigenvalue Problem
- Finding Eigenvalues and Eigenvectors
- Matrix Diagonalization
Level : Beginner
Duration : 20 hours
Rating : 4.8
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Mathematics for Machine Learning Specialization by Imperial College London
This is a three course specialization provided by the Imperial College of London. The courses cover Linear Algebra, Multivariate Calculus & Principal Component Analysis. The aim of this specialization is to help learners get up to speed in the underlying mathematics, build an intuitive understanding, and relate it to Machine Learning and Data Science.
Following topics and skills are covered in this specialization:
- Linear Algebra – This course discusses what linear algebra is and how it relates to vectors and matrices. You’ll learn how vectors span space, how to transform vectors, matrix operations, and how to apply basis vectors’ concepts to matrices. You will also be introduced to eigenvectors and eigenvalues and how these are used in modelling to solve problems. You’ll learn to implement and analyze the PageRank algorithm with the help of eigenvalues.
- Multivariate Calculus – This course is an introduction to the multivariate calculus required to build many common machine learning techniques. It starts with the basics of calculus in an intuitive manner, and then moves on to the multivariate case and to the Taylor series. You’ll learn how neural networks work and even implement one.
- Dimensionality Reduction with Principal Component Analysis – This course applies the concepts from the first two courses to make learners understand Principal Component Analysis which is used to compress high-dimensional data. It covers some basic statistics of data sets, such as mean values and variances, dot and orthogonality between the vectors, projection in matrices and how to use these tools to derive PCA.
This is one of the most recommended programs to gain the prerequisite mathematical knowledge to advance in the field of machine learning and deep learning.
Level : Beginner
Duration : 4 months, 4 hours per week
Rating : 4.5
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Mathematics for Data Science Specialization by HSE
This specialization is offered by the National Research University – Higher School of Economics, one of the top research universities in Russia. It focuses on a wide range of mathematical tools and discusses how they are used in Data Science. Essential concepts of Discrete Mathematics, Calculus, Linear Algebra and Probability are covered. It not only teaches the theoretical concepts but also includes several practical examples and problems arising in Data Science and shows how to solve them in Python.
The specialization comprises of following 4 courses –
- Discrete Math and Analyzing Social Graphs – This course focuses on topics in Discrete Mathematics relevant to Data Analysis like Combinatorics, Probability Theory, Graphs. It also includes a project related to social network graphs.
- Calculus and Optimization for Machine Learning – This course provides necessary background in Calculus. It covers topics such as functional mappings, limits, differentiability, integration etc. to build up a base for the basic optimisation.
- First Steps in Linear Algebra for Machine Learning – This course teaches fundamentals of working with data in vector and matrix form, skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability.
- Probability Theory, Statistics and Exploratory Data Analysis – This course provides necessary knowledge of probability theory and statistics. The main focus of the course is random variable and its properties such as expected value, variance and correlations. It also covers practical aspects for working with probabilities, sampling, data analysis, and data visualization.
Level : Beginner
Duration : 6 months, 4 hours per week
Rating : 4.3
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Programming & Data Basics & Applications
Top two programming languages used for Data Science are Python and R. Apart from these, Java, C/C++, Scala, MATLAB, Julia are also used. As a beginner, it is best to focus on either Python or R and master it.
Learning how to work with data, like manipulate data, clean data, import and export data, scale data, structure data etc. is an important basic skill in Data Science. There are several toolkits that Python and R have for this purpose like Pandas, NumPy, stringr etc.
Python for Everybody Specialization by University of Michigan
This is one of the most popular courses on Coursera with close to a million student enrolments and is perhaps the best starting point in your data science journey to learn Python. This specialization is taught by Professor Charles Severance of the University of Michigan School of Information.
Learners are introduced to the fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. Apart from the basics of Python, learners also pick up important data skills, like gathering, cleaning, analysing and visualizing data, web scraping and working with SQL databases.
This specialization is structured as 5 courses that cover the following topics in detail:
- Python basics like variables, expressions, conditional code, loops and iterations etc.
- Python functions
- Python data structures such as lists, dictionaries, and tuples
- Using Python to scrape, parse, and read web data
- Access data using web APIs
- Using Databases with Python
- Capstone project to design and create your own applications for data retrieval, processing, and visualization
Level : Beginner
Duration : 3 months, 11 hours per week
Rating : 4.8
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Python 3 Programming Specialization by University of Michigan
This is another great choice to learn Python programming in depth. It is a series of 5 courses that teach Python step-by-step, starting with basics like variables, conditionals, and loops, and then moving to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance. By the end of this specialization, you’d have mastered Python and be able to work as an independent Python programmer.
Following topics are covered in this specialization:
- Python basics
- How to debug Python programs
- Python Functions, Files, and Dictionaries
- Data Collection and Processing with Python
- Python classes, instances, and inheritance
- Query Internet APIs for data and extract useful information from them
- Python libraries – pillow, tesseract, and opencv
This is a very hands-on specialization with abundant opportunities to practice. Every course includes a project at the end to apply the skills that you learn.
Level : Beginner
Duration : 5 months, 7 hours per week
Rating : 4.6
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Data Science: Foundations using R Specialization by Johns Hopkins University
This Specialization by Johns Hopkins University offers a great choice for learning the foundational data science tools and techniques, including getting, cleaning, and exploring data. It teaches programming in R language, and also covers how to conduct reproducible research and how to create visualizations to communicate results.
There are five courses in the specialization with a hands-on project at the end of each course. Also there are multitude of assessment quizzes and peer review assignments. Topics covered include:
- Introduction to tools that data analysts and data scientists work with such as version control, markdown, git, GitHub, R, and RStudio
- Set up R, R-Studio, Github and other useful tools
- R programming including reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code
- Collecting, cleaning, and sharing data
- Exploratory data analysis
- Plotting systems in R and basic principles of constructing data graphics
- Reproducible research along with statistical analysis tools
Level : Beginner
Duration : 5 months, 8 hours per week
Rating : 4.6
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Mastering Software Development in R Specialization by Johns Hopkins University
This Specialization from Johns Hopkins University is all about R software development for building data science tools that are highly reusable, modular, and collaborative. It imparts rigorous training in the R language, including the skills for handling complex data, building R packages, and developing custom data visualizations. It also covers R libraries for data manipulation, like tidyverse, and data visualization and graphics, like ggplot2.
The specialization is structured as 5 following courses:
- The R Programming Environment – This course covers basic R concepts and language fundamentals, key concepts like tidy data and related “tidyverse” tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks.
- Advanced R Programming – This course covers functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions.
- Building R Packages – This course covers R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub.
- Building Data Visualization Tools – This course focuses on the ggplot2 framework and describes how to use and extend the system to suit the specific needs of your organization or team.
- Mastering Software Development in R Capstone – This includes an R programming capstone project
Level : Beginner
Duration : 6 months, 4 hours per week
Rating : 4.1
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Applied Data Science with Python Specialization by University of Michigan
This Data Science specialization offered by the University of Michigan is an all-in-one introduction to data science. It introduces learners to the basics of working with data, cleaning, and visualizing it all in Python. Learners acquire analysis skills and learn to apply data science methods and techniques (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.
This specialization is a series of 5 courses that focus on the applied side of data science. Each course covers the use of one or more free Python libraries and builds on the previous courses. The courses are as follows:
- Introduction to Data Science in Python – This course covers fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. It teaches how to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
- Applied Plotting, Charting & Data Representation in Python – This course teaches the information visualization basics, with a focus on reporting and charting using the matplotlib library.
- Applied Machine Learning in Python – This course introduces learners to applied machine learning, focusing more on the techniques and methods. It covers the Scikit learn toolkit as well.
- Applied Text Mining in Python – This course covers text mining and text manipulation basics and the nltk framework for manipulating text.
- Applied Social Network Analysis in Python – This course covers network analysis through tutorials using the NetworkX library.
Level : Intermediate
Duration : 5 months, 7 hours per week
Rating : 4.5
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Databases and SQL
Data Science is essentially the study and analysis of data, so without database, it is meaningless. SQL (structured query language) is used by Data Scientists to work with the data. It is in fact a standard for many database systems. Big Data systems like Hadoop and Spark also make use of SQL. So knowledge of SQL is an essential skill for Data Science.
SQL for Data Science by UC Davis
This Course offered by University of California, Davis is the most popular SQL course on Coursera. It provides an introduction to the fundamentals of SQL and working with data so that learners can begin analyzing it for data science purposes. It has been designed with beginners in mind and is an excellent choice for building skills in SQL.
The course encourages learners to ask the right questions and come up with good answers to deliver valuable insights for their organization. It starts with the basics and gradually moves to concepts of data governance and profiling. Structured as 4 weekly modules, the course covers the following:
- Selecting & Retrieving Data with SQL
- Different types of data like strings and numbers
- Creating new tables and moving data into them
- Filtering, Sorting, and Calculating Data with SQL
- Operators and aggregate functions
- Types of JOINs, including the Cartesian join, an inner join, left and right joins, full outer joins, and a self join
- Subqueries and when to use them
- Modifying and Analyzing Data with SQL
- Tips and tricks to apply SQL in a data science context
Level : Beginner
Duration : 14 hours
Rating : 4.6
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Statistics & Probability
Statistics is a fundamental tool of Data Scientists. It is useful to provide structure to data, analyse it and deliver deeper insights into data. Statistics and Machine Learning work in sync, it is therefore important that learners gain the knowledge of core concepts and basics of Probability & Statistics in order to pursue a career in data science.
Statistics with Python Specialization by University of Michigan
This specialization introduces learners to modern statistical thinking and teaches how to use Python to understand statistical studies and reports. Apart from teaching statistical concepts in a fun and engaging way, it also gives learners several opportunities to analyze real data using Python, and apply the concepts to real problems. It covers where data come from, what types of data can be collected, data design, data management, and how to effectively carry out data exploration and visualization.
There are three courses in this specialization that cover the following:
- Basic conceptual knowledge of study design
- Data Management
- Descriptive Statistical Analysis
- Data Visualization
- Differences between probability and non-probability sampling from larger populations
- Utilize data for estimation and assessing theories
- Confidence Interval construction
- Inferential Results interpretation
- Hypothesis Testing
- Various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models
- Bayesian Inference techniques
- Analysis of clustered or longitudinal data
Along with top-notch instruction, there is a wide variety of examples of real statistical analysis using Python that make this specialization program very effective.
Level : Beginner
Duration : 3 months, 4 hours per week
Rating : 4.6
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Statistics with R Specialization by Duke University
This Specialization offered by Duke University is designed to be a starting point for learning to think critically about data and serves as an introduction to the fundamental concepts in Frequentist and Bayesian statistics. You’ll learn how to make use of data, collect data, analyse data, make inferences and draw conclusions about real world phenomenon. You’ll wrangle and visualize data with R packages for data analysis.
The Specialization comprises of 5 courses that cover the following topics:
- Basic probability theory and Bayes’ rule
- Exploratory data analysis techniques
- Statistical inference methods for numerical and categorical data
- Simple and multiple linear regression models
- Simulation based inference
- End-to-end Bayesian analyses
- Bayesian regression and inference using multiple models
- Bayesian prediction
- Communicate statistical results correctly
- Evaluate data-based claims and decisions
During the course of this specialization learners will complete several data analysis projects involving statistical data analysis skills from exploratory analysis to inference to modelling.
Level : Beginner
Duration : 7 months, 3 hours per week
Rating : 4.5
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Data Science: Statistics and Machine Learning Specialization by Johns Hopkins University
This specialization continues and develops on the material from the Data Science: Foundations using R specialization. It teaches learners to build models, make inferences, and deliver interactive data products. There are five courses in the specialization each of which includes a hands-on, peer-graded assignment. There is also a capstone project where learners build a data product using real-world data.
Following topics are covered in the five courses:
- Statistical inference
- Broad theories of inference like frequentists, Bayesian, likelihood, design based
- Regression analysis, least squares and inference using Regression models
- Special cases of the regression model, ANOVA and ANCOVA
- Uses of regression models including scatterplot smoothing
- Practical Machine learning
- Process of building prediction functions including data collection, feature creation, algorithms, and evaluation
- Machine learning methods including regression, classification trees, Naive Bayes, and random forests
- Development of data products using Shiny, R packages, and interactive graphics
Level : Intermediate
Duration : 6 months, 6 hours per week
Rating : 4.5
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Data Visualization
Data Visualization is a necessary skill for Data Analysts for converting data into actionable visual representations. It is widely used in exploratory data analysis and data mining. Visual representations of data help to sort through, comprehend, project trends, and explain the massive amount of data in a way that it makes sense to the business owners and stakeholders; and is hence key to the decision making process in any organization.
Though multiple specializations mentioned above cover data visualization as well, there is one program by University of California Davis that needs a mention here.
Data Visualization with Tableau Specialization by UC Davis
This Specialization is offered by University of California, Davis in collaboration with Tableau. It teaches how to visualize business data with tableau and create powerful business intelligence reports. Students learn how to leverage Tableau’s library of resources to create and design visualizations and dashboards and present their data story. Several examples from real world business cases and journalistic examples from leading media companies are included in the specialization.
The specialization is organized as a series of five courses that teach the following:
- Fundamental concepts of data visualization
- Examine, navigate, and explore the Tableau interface and features
- Essential design principles for Tableau
- Concepts of exploratory and explanatory analysis
- Tools that Tableau offers in the areas of charting, dates, table calculations and mapping
- Advanced functions within Tableau
- Creating dashboards and storytelling with Tableau
- Applying predicative analytics to improve business decision making
Level : Beginner
Duration : 6 months, 3 hours per week
Rating : 4.6
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Machine Learning & Deep Learning
Machine Learning involves complex algorithms and techniques like regression. There are basically two main categories of machine learning techniques – supervised learning (regression and classification) and unsupervised learning (clustering and dimension reduction). Popular Python tools for machine learning include Scikit-learn, Pytorch and TensorFlow.
Machine Learning Specialization by University of Washington
This Specialization has been created by two leading professors and researchers at the University of Washington. It is a series of four hands-on courses that help learners master the fundamentals of Machine Learning and build intelligent applications. The four courses cover all the major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. Following topics are included:
- Machine Learning concepts
- Data Clustering Algorithms
- Classification Algorithms
- Decision Tree
- Boosting
- Deep Learning
- Linear Regression
- Ridge Regression
- Regression Analysis
- Logistic Regression
- Lasso (Statistics)
- Recommender systems
- Analyzing the performance of the model
Throughout the specialization, learners work with real data sets. They learn to analyze large and complex datasets, and create systems that adapt and improve over time. There are multiple practical case studies and projects that help learners gain applied machine learning and Python programming experience.
Level : Intermediate
Duration : 7 months, 3 hours per week
Rating : 4.7
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Advanced Machine Learning Specialization by HSE
This is an advanced specialization for data scientists offered by National Research University – Higher School of Economics (HSE). It dives deep into the modern machine learning methods and AI techniques. Through a wide variety of projects and case studies, learners get a hands-on experience of solving real-world problems through ML techniques.
Because of the advanced nature of this specialization program, it assumes students to be familiar with basic concepts of machine learning, probability theory, linear algebra & calculus and Python programming. There are courses that are focussed on main machine learning fields and also courses that bridge the gap between theory and practice. Following topics are covered in the 7 courses of this program:
- Introduction to Deep Learning – modern neural networks and their applications
- How to solve predictive modelling competitions efficiently
- Bayesian methods
- Foundations of Reinforcement Learning
- Computer Vision
- Natural Language Processing – sentiment analysis, summarization, dialogue state tracking etc.
- Addressing Large Hadron Collider (LHC) challenges
Level : Advanced
Duration : 10 months, 6 hours per week
Rating : 4.5
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Reinforcement Learning Specialization by University of Alberta
This Specialization by University of Alberta provides a comprehensive introduction to key concepts and classic algorithms of Reinforcement Learning. It helps to understand how RL relates to and fits under the broader umbrella of machine learning, deep learning, supervised and unsupervised learning.
Through 4 courses of the specialization learners explore adaptive learning systems and artificial intelligence (AI) and learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end.
Following topics are covered in the specialization courses:
- Fundamentals of Reinforcement Learning
- Formulating tasks as RL problems
- Classic and modern algorithms in RL
- Sample based learning methods such as Monte Carlo methods, and temporal difference learning methods including Q-learning
- Prediction and control with function approximation
Level : Intermediate
Duration : 5 months, 5 hours per week
Rating : 4.8
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Big Data
Big Data is an application of Data Science which involves enormous data sets and specialized techniques, and tools to deal with capturing, storing, extracting, processing, and analyzing information from these huge data sets.
Big Data Specialization by UC San Diego
This Specialization by University of California, San Diego is a series of six courses that teach fundamental big data methods. It imparts an overview of how big data is organized, analyzed, and interpreted, so as to drive better business decisions. It covers basics of using Hadoop with MapReduce, Spark, Pig and Hive.
Following are the 6 courses included in the specialization:
- Introduction to Big Data
- Big Data Modeling and Management Systems
- Big Data Integration and Processing
- Machine Learning With Big Data
- Graph Analytics for Big Data
- Big Data – Capstone Project
Upon completing this specialization, learners are able to ask the right questions about data, do basic exploration of large, complex datasets and perform predictive modeling and leverage graph analytics to model problems. They also learn to apply insights to real-world problems and questions.
Level : Beginner
Duration : 8 months, 3 hours per week
Rating : 4.5
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