How to Learn Data Science From Scratch

How to Learn Data Science From Scratch

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.

Why is data science important?

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:

  • visualize trends
  • reduce costs
  • launch new products and services
  • extend business to different demographics

Your Learning Plan

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.

1. Technical Skills

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

2. Theory

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.

 3. Math

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

  • Derivative of a function
  • Geometric definition
  • Nonlinear function

3.1.2. Chain Rule

  • Composite functions
  • Multiple functions
  • Derivatives of composite functions

3.1.3. Gradients

  • Directional derivatives
  • Integrals
  • Partial derivatives

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

  • Vectors
  • Linear dependence and independence
  • Linear combinations
  • The vector dot and cross product

3.2.2. Matrix transformations

  • Multiplication of a matrix
  • Transpose of a matrix
  • Linear transformations
  • Inverse function

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

  • Types of distribution
  • Central tendency
  • Summarization of data
  • Dependence measure

3.3.2. Experiment Design

  • Sampling
  • Randomness
  • Probability
  • Hypothesis testing
  • Significance Testing

3.2.3. Machine Learning

  • Regression
  • Classification
  • Inference about slope
4. Practical experience

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.

Data Science Lifecycle

Every data science project goes through a lifecycle. Here we describe each of the phases of the cycle in detail.

  1. Discovery: In this phase, you define the problem to be solved. You also make a report regarding the manpower, skills and technology available to you. This is the step where you can approve or reject a project.
  2. Data Preparation: Here you will need to prepare an analytical sandbox that will be used in the remaining part of the project. You also need to condition the data before modeling. First, you prepare the analytical sandbox, then prepare ETLT, then data conditioning and finally visualization.
  3. Model Planning: Here you will need to draw a relationship among the variables. You need to understand the data. These relationships will be the basis of the algorithm used in your project. You can use any of the following model planning tools: SAS/ACCESS, SQL or R.
  4. Model Building: Here you need to develop data sets to train your system. You have to make a choice between your existing tools or a new more robust environment. Various model-building tools available in the market are SAS Enterprise Manager, MATLAB, WEKA, Statistica, Alpine Miner, etc.
  5. Operationalize: In this step, you deliver a final report, code of the system and technical briefings. You also try to test the system in pilot mode to ascertain how it functions before deploying it in the real world.
  6. Communicate Results: Now your work is done. In this step, you communicate with the stakeholders, whether or not your system complies with all their requirements ascertained in step 1. If they accept the system, your project is a success, or else it is a failure.

Data Science Components

  • Data: Data is the basic building block of data science. Data is of two types: structured data (is basically in tabular form) and unstructured data (images, emails, videos, PDF files, etc.)
  • Programming: R and Python are the most widely used programming language in data science. Programming is the way to maintain, organize and analyze data.
  • Mathematics: In the field of mathematics, you don't need to know everything. Statistics and probability are mostly used in data science. Without the proper knowledge of mathematics and probability, you will most probably make incorrect decisions and misinterpret data.
  • Machine Learning: As a data scientist, you will be working with machine learning algorithms on a daily basis. Regression, classification, etc. are some of the well-known machine learning algorithms.
  • Big Data: In this era, raw data is compared with crude oil. Like we refine crude oil and use it to drive automobiles, similarly, the raw data must be refined and used to drive technology. Remember, raw data is of no use. It is the refined data that is used in all machine learning algorithms.

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.

About Author:

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

ITIL Expert certified

APMG, PEOPLECERT and EXIN Accredited Trainer for all modules of ITIL till Expert

Trained over 3000+ professionals across the globe

Currently authoring a book on ITIL "ITIL MADE EASY"

Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices

Name: Ram Tavva

Designation: Director of ExcelR Solutions

Location: Bangalore

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