An estimated 1.145 trillion MB of data is created globally every day. But what does one do with this data? This is where a data scientist comes into the picture. From IT and retail to banking, financial services, and insurance, every sector requires scientists to handle this data using technology and glean relevant information from it. According to a recent report, there are almost 95,000 vacancies for data scientists in India. In this post, we will look at the best online courses on data science by Coursera.
As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building a framework and solutions to store data.
Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science.
Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business.
Online Courses on Data Science and Job Market Mismatch
There’s a vast array of Data Science courses available, each with its own merits and each suitable for different types of courses. Many of them are also quite costly, so you’ll need to conduct thorough research before you make any kind of commitment. Additionally, do not expect that a 3-month or 6-month online data science certification course will help you to bag a data scientist job role straight away. There is a huge mismatch between the data science job market and online courses on data science.
Mushrooming of Online Courses on Data Science
There are many e-learning and online courses providers out there in the market. These companies advertise heavily, get many reviews (some of which are digital marketers and not always real users), and offer different permutations and combinations of basic courses on R, Python, SAS, Tableau, Excel-based curriculum.
These courses often start with some basic introduction to mathematics/statistics, followed by hands-on visualization moving onto aspects of prediction and artificial intelligence. A few courses also contain domain-specific analytics – like sales analytics, human resource analytics, marketing analytics, financial analytics, etc.
The mismatch of expectations or disconnect begins when all those learners taking these courses or studying data science would like to refer to themselves as data scientists. They think that they can apply for data scientist job openings and succeed. But, the course providers also need to be blamed.
For not-so-well-informed learners, advertisements on “become a data scientist in 12 weeks or 6 months” are misleading.
In a price-sensitive market like India, when the learners don’t end up with desired outcomes, the whole e-learning vertical faces backlash. This also hurts the industry as the learners feel discouraged to up-skill and/or re-skill.
What should Aspiring Data Scientists Know?
In short, as a data scientist, you should have
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Experience with common data science toolkits, such as R, Python, Weka, NumPy, MatLab, etc
- Experience with data visualization tools, such as D3.js, GGplot, etc
- Proficiency in using query languages such as SQL, Hive, Pig
- Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
- Excellent applied statistics skills, such as distributions, statistical testing, regression, etc.
As you can see, you can never learn or master everything from the above list in 3 – 6 months. It actually takes 5 – 10 years. The field is evolving at a fast pace. So, this list can also change in 1 – 2 years.
“Most of the programmers are still theoretical. Many employers complain that while there are many with data science certifications, they do not have the skills that we look for”
– Abhimanyu Saxena, cofounder of Scaler Academy (Quoted on MoneyControl)
Clear Demand for Data Science Professionals
There is an enormous and still growing demand for data scientists with finely tuned skills. Jobs exist in almost every imaginable industry.
In most cases, you can get into the field at a low cost of entry (i.e., with limited prerequisite knowledge and experience).
They make great money. Average salaries for data scientists range from $120,000 to $350,000 in the US on online job platforms and there are ample job opportunities. In India, salaries could be 7-digit as well.
So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.
Top Roles and Responsibilities of Data Scientists
Predictive Causal Analytics:
If you want a model that can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model that can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
If you want a model that has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to make decisions like when to turn, which path to take when to slow down or speed up.
Machine Learning for Making Predictions:
If you have transactional data from a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
Machine learning for Pattern Discovery:
If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.
If you are planning to start your career in Data Science and wish to know the skills related to it, now is the right time to dive in. Some popular websites offering the best Digital Marketing courses around the web are listed down below.
Best Online Courses on Data Science from Coursera
Google Data Analytics Professional Certificate
4.8 (14,555 ratings) || 250,000 students enrolled
Over 8 courses, gain in-demand skills that prepare you for an entry-level job. You’ll learn from Google employees whose foundations in data analytics served as launchpads for their own careers. At under 10 hours per week, you can complete the certificate in less than 6 months.
IBM Data Science Professional Certificate
4.6 (47,858 ratings) || 39,761 students enrolled
The program consists of 9 online courses that will provide you with the latest job-ready tools and skills, including open-source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.
Python for Everybody Specialization
4.8 (172,799 ratings) || 1,081,260 students enrolled
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.
4.9 (161,517 ratings) || 4,259,647 students enrolled
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
- (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Learn SQL Basics for Data Science Specialization
4.5 (4,399 ratings) || 85,771 students enrolled
This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake, and more.
These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets.
Deep Learning Specialization
4.9 (115,754 ratings) || 614,383 students enrolled
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
DeepLearning.AI TensorFlow Developer Professional Certificate
4.7 (16,565 ratings) || 123,279 students enrolled
In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate.
Natural Language Processing Specialization
4.6 (3,404 ratings) || 60,065 students enrolled
This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Data Visualization with Tableau Specialization
4.5 (5,378 ratings) || 75,626 students enrolled
This Specialization, in collaboration with Tableau, is intended for newcomers to data visualization with no prior experience using Tableau. We leverage Tableau’s library of resources to demonstrate best practices for data visualization and data storytelling. You will view examples from real-world business cases and journalistic examples from leading media companies.
Generative Adversarial Networks (GANs) Specialization
4.7 (1,295 ratings) || 19,688 students enrolled
The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.
Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.