Data science is the branch of science that deals with the collection and analysis of data to extract useful information from it. The data can be in any form, be it text, numbers, images, videos, etc. The results from this data can be used to train a machine to perform tasks on its own, or it can be used to forecast future outcomes.
We are living in a world of data. More and more companies are turning towards data science, artificial intelligence and machine learning to get their job done. Learning data science can equip you for the future. This article will discuss how to learn data science from scratch.
You are always surrounded by zettabytes and yottabytes of data. Data can be structured or unstructured. It is important for businesses to use this data. This data can be used to:
Here we provide you with a solid learning plan. No matter if you are a beginner, intermediate or advanced in data science, this plan caters to the needs of each of you. If you use this plan, you can learn data science within a year. We have divided your learning into small chunks. Each level of completion will provide you with immense satisfaction and a boost to start the next level.
We will start with technical skills. Understanding technical skills will help you understand the algorithms with mathematics better. Python is the most widely used language in data science. There is a whole bunch of developers working hard to develop libraries in Python to make your data science experience smooth and easy. However, you should also polish your skills in R programming.
1.1. Python Fundamentals
Before using Python to solve data science problems, you must be able to understand its fundamentals. There are lots of free courses available online to learn Python. You can also use YouTube to learn Python for free. You can refer to the book Python for Dummies for more help.
1.2. Data Analysis using Python
Now we can move towards using Python in data analysis. I would suggest dataquest.io as the starting point. It is free, crisp and easy to understand. If you want a more in-depth knowledge of the topic, you can always buy the premium subscription. The price is somewhere between $24 and $49 depending on the type of package you opt for. It is always useful to spend some money for your future.
1.3. Machine Learning using Python
The premium package for dataquest.io already equips you with the fundamentals of ML. However, there are a plethora of free resources online to acquire skills in ML. Make sure whichever course you follow, it deals with scikit-learn. Scikit-learn is the most widely used Python library for data science and machine learning. At this stage, you can also start attending workshops and seminars. They will help you gain practical knowledge on this subject.
1.4. SQL
In data science, you always deal with data. This is where SQL comes into the picture. SQL helps you organize and access data. You can use an online learning platform like Codeacademy or YouTube to learn SQL for free.
1.5. R Programming
It is always a good idea to diversify your skills. You don't need to depend on Python alone. You can use Codeacademy or YouTube to learn the basics of R. It is a free course. If you can spend extra money, then I would say opt for the pro package for Codeacademy. It may cost you somewhere around $31 to $15
While you are learning about the technical aspects, you will encounter theory too. Don't make the mistake of ignoring the theory. Learn the theory alongside technicalities. Suppose you have learned an algorithm. It's fine. Now is the time to learn more about it by diving deep into its theory. The Khan Academy has all the theory you will need throughout this course.
Maths is an important part of data science.
3.1. Calculus
Calculus is an integral part of this curriculum. Every machine learning algorithm makes use of calculus. So, it becomes inevitable to have a good grip on this topic. The topics you need to study under calculus are:
3.1.1. Derivatives
3.1.2. Chain Rule
3.1.3. Gradients
3.2. Linear Algebra
Linear algebra is another important topic you need to master to understand data science. Linear algebra is used across all three domains – machine learning, artificial intelligence as well as data science.
The topics you need to study under linear algebra are:
3.2.1. Vectors and spaces
3.2.2. Matrix transformations
3.3. Statistics
Statistics are needed to sort and use the data. Proper organization and maintenance of data need the use of statistics. Here are the important topics under this umbrella:
3.3.1. Descriptive Statistics
3.3.2. Experiment Design
3.2.3. Machine Learning
Now you are ready to try your hands in some real-world data science problem. Enroll in an internship or contribute in some open-source project. This step will help you enrich your skills.
Every data science project goes through a lifecycle. Here we describe each of the phases of the cycle in detail.
Now you know everything about data science. Now you have a clear road map on how to master data science. Remember this will not be an easy career. Data science is a very young market. Breakthrough developments are taking place almost every day. It is your job to keep yourself acquainted with all the happenings in the market. A little effort and a bright future await you.
Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkata) with over 25 years of professional experience
Specialized in Data Science, Artificial Intelligence, and Machine Learning.
PMP Certified
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