**Introduction**

Data science is among the fastest-growing domains in the IT industry. In recent years, data scientist jobs have emerged as the most sought after roles in the job market.

Due to the rapid generation of data, organizations are investing heavily in employing data science professionals to help them analyze the large-scale and complex data to attain business growth.

As per recent job trend analysis reports, **a minimum of 5 million data science jobs** is likely to be listed in the IT job industry. With advanced technologies such as big data gaining immense popularity, the need for deriving meaningful insights and co-existing and working in unison has become more important.

As a result, the role of data scientists has evolved into more well-defined roles, thereby creating endless opportunities to deep-dive into this future-proof profession.

Each year, the world of data science is evolving, which signifies newer technologies, tools, and theories that are being added for advanced analysis. Such scenarios call for professionals who are updated with new skill sets **to provide a more competitive advantage to the organizations**.

Therefore, it is crucial to opt for top data science courses that are a valuable addition to the career, instead of a mere certification. With this in mind, the article focuses on providing the best data science courses that are available on the online platform today.

**Data Science Courses**

**1. Data Science Specialization – Johns Hopkins University**

The course is a data science specialization course on the Coursera platform. The specialization covers **data science fundamentals** and the important tools that are required in the data science pipeline

There is also a capstone project at the end of the course. The purpose is to build a data product with real-world data and with the concepts covered in the entirety of the course. The outcome of the course for learners’ will be the know-how of R programming to clean, visualize and analyze the data, the knowledge of how to manage **data science projects using GitHub**, exploring the concepts of navigating the data science pipeline from the point of data acquisition to publication and the idea of performing regression analysis and regression models. The modules of the course are:

- The Data Scientist’s Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Inference
- Regression Model
- Practical Machine Learning
- Developing Data Products
- Data Science Capstone

**Level:** Beginner

**Duration:** 11 months

**2. IBM Data Science Professional Certificate – Coursera**

This data science online course from IBM is offered on Coursera. The data science course syllabus for the program **consists of 9 modules**. The certificate course has a strong emphasis on building practical skills of the learners and it includes a hands-on lab session for each module.

The course explores all the latest tools and skills along with important concepts on open-source tools, python and its libraries, SQL, data visualization, data analysis, machine learning algorithms, and predictive modeling. The tools such as **Jupyter, GitHub**,** RStudio**, and** Watson** studio will be introduced as well. Some of the important libraries like pandas, NumPy, Matplotlib, Seaborn, ScipPy, Scikit-learn will be covered during the hands-on sessions. The course modules are listed below.

- What is Data Science?
- Tools for Data Science
- Data Science Methodology
- Python for Data Science and AI
- Databases and SQL for Data Science
- Data Analysis with Python
- Data Visualization with Python
- Machine Learning with Python
- Applied Data Science Capstone

**Level:** Beginner

**Duration:** 10 months

**3. Professional Certificate in Data Science – Harvard University**

This is an online data science certification course by **Harvard University** on the edX platform. The learners will be able to understand **the concepts on R programming** fundamentals, statistical concepts of probability, inference, and modeling, data visualization, explore the essential tools such as Unix, Linux, git, GitHub and RStudio, machine learning algorithms, and real-world case studies of data science. The modules of the course include the following.

- Data Science: R Basics
- Data Visualization
- Probability
- Inference and Modeling
- Productivity Tools
- Data Wrangling
- Linear Regression
- Machine Learning
- Capstone

Related Reading: edX Review

**Duration:** 1 year 5 months (self-paced)

**R4. Data Science: Statistics and Machine Learning Specialization – Coursera**

The online certification course is a specialization offered by **Johns Hopkins University** on the Coursera platform. The goals of this course are to equip the learners with **regression analysis**, the development of public data products, building prediction functions, and mastering the concepts on scientific truths of the data. Each module consists of a hands-on assignment. At the end of the course, the learners are required to complete the final capstone project. The course contents are highlighted below:

- Statistical Inference
- Regression Models
- Practical Machine Learning
- Developing Data Products
- Data Science Capstone

**Level: **Intermediate

**Duration:** 6 months

Related Reading: Coursera Review

**5. MicroMasters Program in Statistics and Data Science – MIT**

The online course is offered by **MIT** which is one of the top-tier institutes in the world. The course is available on the edX platform. The primary aim of the course is to allow learners to understand **the foundations of data science**, statistics, and machine learning.

Some of the key concepts that will be covered in this course are the **big data analysis for data-driven predictions**, probabilistic modeling, statistical inference, identification and deployment of models, related methodologies, development of machine learning algorithms, the difference between structured and unstructured data, and the essentials of machine learning such as supervised and unsupervised methods and neural networks. The course curriculum is listed in the following section.

- Probability: The Science of Uncertainty and Data
- Fundamentals of Statistics
- Machine Learning with Python: From Linear Models to Deep Learning
- Capstone (Select one of the topics: Data Analysis in Social Science

**Duration:** 1 year 2 months

**6. Python for Data Science – UC San Diego**

The certification course is offered by UC San Diego and delivered on the edX platform. The learners will be introduced to the **open-source tools**, essentials of Python programming language, and its tools such as Pandas, Git, and Matplotlib, learn to manipulate, analyze, and visualize complex data. The course is a self-paced computer science course that lets you get an in-depth experience with the tools and learn to explore datasets to succeed as a data scientist.

**Level:** Advanced

**Duration:** 10 weeks

Related Reading: Udacity Review

**7. MicroMasters Program in Analytics: Essential Tools and Methods – Georgia Tech**

This certificate course is offered **by one of the most prestigious institutions** in the world. The course is designed by Georgia Tech and delivered on the edX platform. The course focuses on important tools for data analysis with the likes of R, Python, SQL, etc. Among the concepts that are covered in the course are the fundamental models and methods for performing analytics and usage.

The learners will also gain insight into building a data analysis pipeline and the key components associated with it like collection, storage, and visualization of data. The course will also enable the learners to use their **analytical skills in a business context**. The modules of the course are.

- Introduction to Analytics Modeling
- Computing for Data Analysis
- Data Analytics for Business

**Duration:** 1 year

**8. Python for Data Science and Machine Learning Bootcamp – Udemy**

The program is a beginners’ data science course that is offered on Udemy. It is offered in a **boot camp format**. This is a comprehensive course on data science and it has garnered a lot of positive reviews from learners.

The key takeaways from the course is a refined understanding and knowledge of using python for data science and machine learning, implementation of machine learning algorithms, use of libraries such as Pandas, SciKit learn, Seaborn for statistical plotting, random forests, logistic regression, spark for big data analysis, NumPy, dynamic visualization, K-Means clustering, linear regression, support vector machine and natural language processing and spam filters. The modules of the course are **divided into two components** that are as follows.

- Course Introduction
- Environmental Set-Up
- Jupyter Overview
- Python Crash Course
- Python for Data Analysis with NumPy, Pandas, Matplotlib, Seaborn, built-in visualization, cufflinks, geographical plotting
- Capstone Project
- Introduction to Machine Learning
- Linear Regression
- Cross-Validation and Bias Variance Trade-off
- Logistic Regression
- K Nearest Neighbors
- Decision Tree
- Random Forest
- Support Vector Machine
- K Means Clustering
- Principal Component Analysis
- Recommender Systems
- Natural Language Processing
- Neural Nets and Deep Learning
- Big Data and Spark with Python

**Duration:** 25 hours on-demand video (Self-Paced Program) and Full Lifetime Access

**9. Data Scientist Course – Udacity**

The course is among the several courses that are offered by the school of data science on the Udacity platform. The course lets learners gain **real-world data science experience** by working on projects that are designed by industry professionals.

Some of the key areas are running a data pipeline, design experiments, building a recommendation system, and its deployment to the cloud. There are a few prerequisites for the course such as familiarity with **machine learning concepts**, python programming fundamentals, and probability and statistics. The course modules are.

**Solving Data Science Problems**

The first module covers the data science process that is inclusive of effective data visualization techniques and how to communicate with various stakeholders.

**Software Engineering for Data Scientists**

The module focuses on developing the software skills that are essential for a data scientist to be successful such as the creation of unit tests and building of classes.

**Data Engineering for Data Scientists**

The learners can build a solid understanding of working with data within a data science process with the likes of running pipelines, transforming the data, building appropriate models, and deploying the solutions to the cloud server.

**Experiment Design and Recommendations**

This unique module will allow learners to design experiments and analyze them through A/B test results and explore various approaches that are available for building an effective recommendation system.

**Data Science Projects**

The final module expects students to build their data science project based on all the concepts that are covered in the entire program.

**Level:** Intermediate

**Duration:** 4 months

**10. Become a Data Engineer – Udacity**

This is among the best online courses in Data Science. It is offered on the Udacity platform. The course is focused on the aspects of **data engineering** and its key elements. The course introduces **the fundamentals of data engineering** and the concepts of how to build a production-ready data infrastructure.

Some of the concepts that will be covered are designing data models, data warehouse, data lakes, and automation of data pipelines. The learners will also be able to work with large datasets for more insights. At the end of the course, there is a capstone project that needs to be completed. The prerequisite of the course is **the knowledge of Python and SQL**. The course syllabus is as follows.

- Data Modeling
- Cloud Data Warehouse
- Spark and Data Lakes
- Data Pipeline with Airflow
- Capstone Project

**Level:** Intermediate

**Duration:** 5 months

**11. Machine Learning A-Z: Hands-On Python and R in Data Science – Udemy**

A comprehensive course on data science that is delivered on Udemy. The course covers the essential concepts of machine learning with Python and R. The learners will be able to understand **the process of making accurate predictions**, building machine learning models, dimensionality reduction, business value of the machine learning models, reinforcement learning, natural language processing and deep learning.

The course is designed by two professionals data scientists working in top organizations. The course comprises the right balance between complexity theory and practical aspects of data science. All the content of the course is continuously updated with new additions as per industry trends. The program comprises real-world examples and hands-on experience for each concept. The course curriculum involves the following contents.

- Course Introduction
- Data Preprocessing in Python
- Data Preprocessing in R
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Models Performance
- Regression Model Selection in Python
- Regression Model Selection in R
- Logistic Regression
- KNN
- SVM Classification
- Kernel SVM
- Naïve Bayes
- Decision Tree
- Classification Model Selection
- Evaluating Classification Model Performance
- Clustering concepts with K-Means and Hierarchical Clustering
- Apriori
- Eclat
- Reinforcement Learning
- Thomson Sampling
- Natural Language Processing
- Deep Learning
- ANN
- CNN
- PCA and LDA
- Kernel PCA
- Model Selection
- XGBoost

** Duration:** Self-Paced

**12. Post Graduate Program in Data Science – Purdue University**

The program in data science is offered by Purdue University and in collaboration with IBM. It is offered on Simplilearn. The course provides a broader exposure to the key components of data science today. The theoretical concepts and tools like Python, R, and machine learning are covered thoroughly. There are hands-on lab sessions and projects to work on within this program.

The learners will gain alumni association membership and certification from Purdue University, **3 domains and 25 projects** with the industry-relevant dataset, sessions from **Purdue University faculty and IBM professionals**, and hackathon access by IBM. The pre-requisites are a basic understanding of mathematics and programming concepts. The course curriculum is highlighted as follows.

- R programming for Data Science
- Python for Data Science
- Machine Learning
- Natural Language Processing
- Tableau Training
- Data Science Capstone
- Electives: Academy Master Class (Purdue University), Industry Master Class (Data Science)

**Level: **2 years of work experience preferred

**Duration:** 12 months

**13. Data Science Course Online in Collaboration with IBM – Intellipat**

The data science course introduces learners to the world of **data analytics**, and its related tools like R, statistical computing, and machine learning algorithms. The program involves hands-on experience in multiple domains such as banking, finance, etc. The course content is discussed below.

**Module 1: Introduction to Data Science with R**

Topics on data science, the significance of data science in the data-driven world, lifecycle, and its components. R programming and installation of R studio, implementation of mathematical operations, and R operators.

**Module 2: Data Exploration**

Introduction of data exploration, import, and export of data from an external source, data exploratory analysis, data frames, and its elements like vectors, factors and operators, in-built functions, and conditional statements. The hands-on exercise includes churning of data and the use of predefined functions in R.

**Module 3: Data Manipulation**

The need for data manipulation, packages, pipe operators, condition filters, etc. The hands-on exercise deals with performing operations for data manipulation.

**Module 4: Data Visualization**

Introduction to visualization, different types of graphs, multivariate analysis, univariate analysis, creating bar plots, frequency plots, scatter plots, co-ordinates and plotly and geographic visualization are some of the key concepts covered in the module. There is a data visualization exercise as well that needs the learners to understand the churn ratio and importing and analyzing data along with visualizing the examples.

**Module 5: Introduction to Statistics**

The need for statistics, categories of statistics and terminologies, correlation, covariation and normalization, hypothesis, **ANOVA**, chi-square testing. The practical example requires learners to build a statistical analysis model.

**Module 6: Machine Learning**

Key concepts of machine learning, linear regression, predictive modeling, p-value, logistic regression, confusion matrix, **F-statistics** are some of the concepts that are covered in this module. The hands-on experience assignment is to model the relationship within data using linear predictor functions and implementation of linear and logistic regression in R by building a model.

**Module 7: Logistic Regression**

Introduction to logistic regression, linear vs logistic regression, binomial model, true positives and false-positive rates, ROC plots, and cross-validation are some of the essentials in this module. The hands-on is targeted at implementing predictive analysis by data description, explaining the relationship between one dependent binary variable.

**Module 8: Decision Tree and Random Forest**

The module covers **classification concepts** and their techniques, introduction to decision trees, building a decision tree in R, confusion matrix, random forest in R, Naïve Bayes, concepts of impurity, entropy, gain. The implementation of the random forest for regression and classification, building a tree with pruning, and ROCR are needed for the hands-on assignment.

**Module 9: Unsupervised Learning**

Clustering and use cases, k-means clustering, hierarchical clustering, unsupervised learning, feature extraction, dendrograms, and PCA using R. The deployment of unsupervised learning with R and k-means clustering for visualization are covered for the hands-on work.

**Module 10: Association Rule Mining and Recommendation Engines**

The introduction to association rule mining, support, confidence, and apriori algorithm and its implementation in R. The introduction of recommendation engines, collaborative filtering, and recommendation engine use cases are covered in-depth. The hands-on assignment needs the learners to work on deploying association analysis and identification of strong rules for databases and the measure based on the discoveries.

Modules 11-15 are **self-paced content** and cover the related concepts of artificial intelligence and some of the algorithms associated with it, time series analysis, support vector machine, Naïve Bayes, and Text mining.

**Duration: **42 hours’ instructor-led training, 28 hours of self-paced videos, 56 hours of project work, and exercises (self-paced).

**Conclusion**

The world of data science is vast and it continues to grow each year. For all the aspirants and professionals looking for a career in data science, the individuals must have **a wide range of knowledge including** the theoretical and practical aspects of the field.

While the theory will allow us to understand important concepts and factors behind data, proficiency with coding and the tools will lead the career towards success.

Today, every company is looking for professionals that have hands-on experience. It is to invest lesser time on cost-factors and time spent on training an individual. Therefore, it is advisable to continue learning and keep upgrading the skill set with the current trends in the industry.

For all the aspiring data scientists, it is an opportune moment in the age of digitization to embark on a highly successful career in the field of data science.