5 Steps to Learn Data Science From Scratch in 2019

Comprehensive and clear roadmap to build your career in Data Science and Machine Learning. Get complete guidance on best online courses to jump start your career.

Data Science is all about solving business problems and there is no formal education system available with clearly defined syllabus. Let us understand the basic terminologies and how to build your career in Data Science.

It involved cleaning data dumps, mapping with other available data sets to get meaningful insights for business. Now data from social and various online platforms have also added to the list.

While the explosion of data happened, we didn’t train too many people to analyse these huge data dumps. That’s why we have huge shortage of good data scientists and there is complete imbalance of demand and supply.

It is very important to understand the basic difference among the three branches as shown above. In this article we will be talking about the middle branch that is Data Science.

Before that I will give you a brief about the other two:

Big Data:

This is mostly to do with data management, as we have seen that data is getting generated from many different platforms. It could be search history, social media, CRM etc. All this data is humongous and in different formats. We use tools like Hadoop to manage this kind of big data.

In this field, you will learn data storage, using query like SQL, processing data etc. Along with this there are tools for data visualization as well, which you can learn.

Data Analysis:

This is very much like business intelligence. In this we are not trying to predict or model anything like in Data Science. We just prepare trends and insights from available data sets using tools like Excel, Power BI, Tableau etc.

This is most basic of the three branches within data science.

Now let us understand what exactly Data Science is and the steps to master it.

What should I learn? – Concepts or coding

This is the question that many of you might be having. Many online learning platforms are promoting coding with R or Python and become machine learning expert.

So, what happens, if we take up these courses and learn coding but we don’t understand the background. So, in the real world scenario you will be only able to do, what your supervisor tells you to do.

For example, you may learn how to use a clustering technique in Python, but you will not know when to use clustering and which technique will work best in a given scenario.

To solve a real world problem, we need to know all the different techniques and which one can be applied. In our models, we look for better accuracy in predicting. To do this, we may end up using multiple techniques and concepts. All this needs to be logical based on statistical or mathematical concepts.

Initially, you may be doing only coding work but if you want to grow then concepts are very important and my suggestion is to start with building sound base with concepts.

2nd step should be learning Python for coding work. Once you master these then Deep Learning comes into picture.

If you are new to this field then I would try to explain you which courses you can take up and how to move forward, I am keeping it simple with few top rated courses only.

Also keeping cost in mind, I will recommend the most cost effective way to gain knowledge. I am not recommending you to start searching YouTube and get lost in plethora of videos, some are good but others are just average and you will not understand where to start and how do they link to each other.

Overall we can divide the complete learning into three broad areas as shown below in the graphic.

Machine Learning Concepts

This is arguably the best course on Machine Learning concepts. This course is not for Python coding but to understand the underlying concepts and you will really enjoy learning from Andrew Ng, who is the instructor of this course.

Andrew Ng from Coursera is a dynamic instructor, he is the co founder of Coursera and founding lead of Google Brain. He inspires confidence and self belief, especially when sharing practical implementation tips.

Machine Learning courses by Andrew Ng are highly recommended by most of the industry experts.

You can simultaneously do this course and learn Python, check below options for Python courses.

Machine Learning (Stanford University-Coursera) by Andrew Ng

Duration : Approx 55 Hours

Price : Approx $ 48/month (It will take 2-3 months to complete, you can apply for financial aid option for free course)

Rating : 4.9 out of 5

You can Sign up Here

Note: You can enrol for a 7 day free trial

The next course is actually a specialization from Coursera and is offered from University of Washington. It is a combination of four courses:

a) Machine Learning concepts with case studies

b) Regression

c) Classification concepts

d) Clustering and Retrieval

You can also take these courses one by one as well and work at your own pace. Upon completion you will get the certificate from the University. You will also get to work on a hands on projects as part of specialization.

Machine Learning Specialization(Coursera) by University of Washington

Duration : Approx 200 Hours

Price : Approx $ 48/month (It will take 5-6 months to complete, you can apply for financial aid option for free course)

Rating : 4.7 out of 5

You can Sign up Here

Note: You can enrol for a 7 day free trial

The next one is a unique program offered by Edx, this is the MicroMasters program by Columbia university. Students may apply to the university offering credit for the MicroMasters program certificate and, if accepted, can pursue an accelerated and less expensive Master’s Degree. These MicroMaster certifications are well recognized by the industry.

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modelling and model selection.

Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

Micro Masters in Machine Learning(Edx) by Columbia University

Duration : Approx 120 Hours

Price : Approx $ 300 for certificate (one can browse through the course for free)

Rating : 4.7 out of 5

You can Sign up Here

Note: You can enrol for a 7 day free trial

Learn Python and R

Most of the modern day coding for Machine Learning is happening in Python and R. Python has replaced most of the legacy tools/languages like SAS.

It is always better to start learning Python and R before we move into Machine Learning concepts. This is specially important for someone having very less or no programming experience.

Recommended Courses (Pick one of them):

Machine Learning A-Z™: Hands-On Python & R In Data Science(Udemy) by Kirill Eremenko

Duration : Approx 41 Hours

Price : Lowest price

Rating : 4.5 out of 5

You can Sign up Here

Complete Python Bootcamp: Go from zero to hero in Python 3(Udemy) by Jose Portilla

Duration : Approx 24 Hours

Price : Lowest price

Rating : 4.5 out of 5

You can Sign up Here

Above two courses from Udemy are beginner level good courses and the next one is an intermediate level specialization which I would recommend for more detailed study.

This one is from University of Michigan and offered by Coursera. This specialization is a combination of following courses:

Introduction of Python

Applied Plotting and data representation

Machine Learning with Python

Text Mining using Python

You can always pick one of the above mentioned course, complete and then opt for the next one.

Applied Data Science with Python Specialization(Coursera) by University of Michigan

Duration : Approx 99 Hours

Price : Approx $ 48/month (It will take 3-4 months to complete, you can apply for financial aid option for free course)

Rating : 4.5 out of 5

You can Sign up Here

Deep Learning and AI

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods. This is the next step within machine learning and AI.

The first recommended course is a specialization from Andrew Ng and it broadly covers following topics:

a) Tensor flow

b) Convolutional Neural Network

c) Artificial Neural Network

d) Deep Learning

You can pick one of the courses to start with and later complete other ones to earn your specialization, This covers all the important aspects of deep learning.

Deep Learning Specialization by Deeplearning.ai(Coursera) Course by Andrew Ng

Duration : Approx 120 Hours

Price : Approx $ 48/month (It will take 4-5 months to complete, you can apply for financial aid option for free course)

Rating : 4.9 out of 5

You can Sign up Here

This is a good course but you need to have prior Python experience. You will learn to write Deep Learning algorithms using Python.

This course is recommended because it’s a good one and very cost effective.

Deep Learning A-Z™: Hands-On Artificial Neural Networks(Udemy) by Kirill Eremenko

Duration : Approx 22 Hours

Price : Lowest price

Rating : 4.5 out of 5

You can Sign up Here

Machine Learning and Deep Learning are the most sought after skill in today’s time as well as in the foreseeable future. This is the right time to start your learning and jump start your career.

Since this is a fast evolving industry, be ready to learn new techniques and target to learn a new thing every quarter.

I have done exhaustive research and came up with the Best Machine Learning Courses, Best Deep Learning Courses and Best AI Courses which cover various aspects, technologies and programming languages.

I have only included the best and though there are other good courses also available but I have tried to keep it simple.

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