How To Learn Data Science From Scratch [2023 Guide]

Sakshi GuptaSakshi Gupta | 10 minute read | March 31, 2022
How To Learn Data Science From Scratch [2022 Guide]

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With a hot job market, lucrative salaries, and promising career opportunities, it’s a great time to become a data scientist. But what if you’re starting from scratch? Luckily, there is a myriad of different learning paths. You can learn skills in the field in many different ways—from getting a college degree to attending bootcamps to teaching yourself. Not sure where to start? In this article, we’ll show you how to go from being a novice to being job-ready in the field of data science. 

Why Data Science?

Data science has risen to the forefront of the software industry because companies have begun to understand the importance of data. Sourcing and processing data effectively is a must for growing organizations today. Companies leverage data scientists to generate insights that can help them outmaneuver the competition and multiply profits. 

Because of this, the field of data science is seeing an abundance of opportunities. The American Bureau of Labor Statistics has projected that the field will grow by almost 30% through 2026. That’s partially why US News has listed “Data Scientist” as one of the top three technology jobs.

With companies competing for the best talent, salaries are rising. The University of San Francisco reports that the graduates of its MS in Data Science program earn a median salary of $125,000. More than 90% of graduates have landed a full-time role within three months of completing the program. 

Before you dive headfirst into the world of data science, you may be wondering: what does a data scientist actually do? Let’s find out. 

What Does a Data Scientist Do?

how to learn data science: What Does a Data Scientist Do?

A data scientist turns data into meaningful insights. These insights guide upper management when making business decisions. 

Data science starts with the collecting and cleaning of data. The latter is necessary because data, when it’s first sourced, does not come in a form that’s easy to analyze. There are usually missing entries, corrupted volumes, etc. So data scientists use statistical methods and engineering skills to clean that data. 

Then, they conduct an exploratory data analysis, in which they look for patterns in the data. Data scientists do this by writing algorithms and creating models which can be used to run experiments on datasets and uncover useful insights. 

Data scientists then communicate their insights to other teams and management. This often requires data visualization and presentation skills. 

To summarize, here are some of the tasks assigned to data scientists: 

  • Identify opportunities where data can be used to solve problems. 
  • Source data that can be valuable in solving the problem. 
  • Clean the data and ensure that it meets the organization’s standards for data accuracy. 
  • Employ algorithmic approaches and build models to generate insights. 
  • Use data visualization and storytelling to convey findings to various stakeholders. 

Now that we know what a data scientist does, let’s look at how to learn data science if you’re just starting out in the field. 

Steps To Learn Data Science

how to learn data science: Steps to Learn Data Science
  1. Build a Strong Foundation in Statistics and Math

  2. Learn Programming With Python and R

  3. Get Familiar With Databases

  4. Learn Data Analysis Methods

  5. Learn, Love, Practice, and Repeat

  6. Learn How To Use Data Science Tools

  7. Work on Data Science Projects

  8. Become a Data Storyteller

  9. Network

  10. Always Be Learning

Here are the steps to learn data science from scratch. 

Build a Strong Foundation in Statistics and Math

Like many other science disciplines, math is foundational to working in data science, and will give you a strong theoretical foundation in the field. 

When working in data science, statistics and probability are the most important areas to grasp. Most of the algorithms and models that data scientists build are just programmatic versions of statistical problem-solving approaches. 

If you’re a beginner with statistics and probability, you can start with a 101 course. Use this as an opportunity to learn basic concepts like variance, correlations, conditional probabilities, and Bayes’ theorem. Doing this will put you in a good position to understand how those concepts translate to the work that you will do as a data scientist. 

Here’s a video that covers a few of the mathematical concepts that you need to learn as a beginner in data science. 

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Learn Programming With Python and R

Once you’re familiar with the mathematical concepts you’ll need, it’s time to learn some programming skills, so that you can turn all that math know-how into scalable computer programs. Python and R are the two most popular programming languages used in data science, so that’s a good place to start. 

Python and R are good starting points for a few reasons. They’re both open-source and free, which means that anyone can learn to program in these languages. You can program in both languages across Linux, Windows, and macOS. Most importantly, these languages are beginner-friendly, with syntax and libraries that are easy to use. 

You can accomplish almost any data science task using Python and R together, but they do have their individual strengths in certain areas. Python tends to work better when you’re wrangling massive volumes of data. It is superior to R when it comes to deep learning tasks, web scraping, and workflow automation. 

R is a language that’s best for translating statistical approaches to computer models. It has a wealth of statistical packages that you can apply to datasets quickly and easily. That makes building statistical models easier in R as compared to Python. 

Ultimately, the choice between Python and R comes down to your career goals. Python is a better starting point if you want to work in areas of data science like deep learning and artificial intelligence. Start with R if you’re more inclined towards pure statistical approaches and model building. And remember, you can always learn the other one down the line. 

Get Familiar With Databases

how to learn data science: Learn Programming With Python and R

Data scientists need to know how to work with databases so they can retrieve the data they’re working with and store it after processing. 

Structured Query Language (SQL) is one of the most popular database query languages. It allows you to store new data, modify records, and create tables and views. Big data tools like Hadoop have extensions that allow you to make queries using SQL, which is an added advantage. Here is a post with 7 resources to help you learn big data easily.

As a data scientist, you don’t need a deep understanding of database technologies. Leave that to the database administrators. As a data scientist, you just need to understand how relational databases work and learn the specific query commands to retrieve and store data. 

Learn Data Analysis Methods

There are various methods that you can use to analyze a dataset. The specific approach that you employ depends on the problem that you’re looking to solve and the nature of the data that you’re using. As a data scientist, your job is to have the foresight required to know which method will work best for a particular problem. 

A few data analysis techniques are commonly used in the industry. That includes cluster analysis, regression, time series analysis, and cohort analysis. This post covers the details of all the popular data analysis techniques. 

You don’t need to know every data analysis method out there. It’s more important that you understand the uses of a particular approach. The best data analysts are the ones who can quickly pair problems with data analysis techniques.

Get To Know Other Data Science Students

Jasmine Kyung

Jasmine Kyung

Senior Operations Engineer at Raytheon Technologies

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Jonas Cuadrado

Jonas Cuadrado

Senior Data Scientist at Feedzai

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Jonathan Orr

Jonathan Orr

Data Scientist at Carlisle & Company

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Learn, Love, Practice, and Repeat

Once you’ve learned data analysis methods, you can start working on beginner projects. 

But remember, it’s more important to have a strong functional understanding of everything you’ve learned so far, rather than having a surface-level understanding of a wide range of topics. Practice what you study to make sure that you understand it. 

For example, let’s say you’re learning about the concept of a weighted mean. Don’t just stop at learning the definition. Try to implement a program in Python that calculates the weighted mean of a dataset. Learning by doing helps you gain a deep understanding of the concepts that you learn. 

Learn How To Use Data Science Tools

how to learn data science: Learn How To Use Data Science Tools

Data science tools streamline the work. For example, Apache Spark handles batch processing jobs while D3.js creates data visualizations for browsers. This post contains information on some of the other popular data science tools.

At this stage, you don’t need to master one particular tool. You can do that when you actually start a job and know which tools your company requires. At this point, it’s enough to pick one that seems interesting and play around with it. The goal is to get a basic idea of the tools and what you can achieve with them. 

If you have a particular company that you want to work at, then you can look at the job descriptions they publish. They’ll usually mention tools like Hadoop and Tensor Flow. You can familiarize yourself with those tools if you want to work at that particular organization. 

Work on Data Science Projects

Now it’s time to tie everything together by building personal projects. Let’s take a look at a couple of examples of what these projects could look like. 

Sentiment Analysis

Sentiment analysis is the process of inferring the sentiments expressed in a particular text. You might try to use a binary (positive or negative sentiment) or go with a more granular approach and label texts on a variety of emotions such as happy, excited, or curious. 

You can perform a sentiment analysis on any text on the internet. Social media feeds are often a good source for this kind of data and you could analyze a particular hashtag for your sentiment analysis project. 

Recommendation System

Let’s say you’re building a movie recommendation system. The MovieLens datasets can serve as a source for your data. You can then build your recommendation system based on considerations such as genre, actors, runtime, etc. 

These are just a couple of examples. Do something that you feel passionately about and see how you can unearth some insights using data. 

Become a Data Storyteller

Become a Data Storyteller

Data scientists need to communicate their findings in a way that their colleagues can understand. This is where the power of storytelling comes into play. Here are three main components of the data storytelling practice: 

Data

The data you corral from your analytical process will serve as the starting point for your story. 

Narrative

A narrative is a story and context that you want to communicate to your audience. 

Visualizations

These are graphic depictions of data. You can use graphs, charts, videos, and diagrams to support your narrative in a way that’s easy for your audience to understand. 

Network

If you’re ready to start looking for a data science job, it’s also important to network with people in the industry, in addition to working on personal projects and crafting your resume.

There are many ways that networking can help when you’re just starting your data science journey. Talking to data scientists can help you understand the state of the industry and what it’s like to work in. Talking to recruiters can give you insights into their interview process and possibly help you land a job. You can also gain a lot by talking to people who understand different industries and how they’re using data to make decisions. 

For all those reasons, it’s important to network as a young data scientist.

Always Be Learning

Your learning journey doesn’t end after you build a few projects or land a job. Data science is constantly evolving and you need to keep evolving too. 

You should be well-informed of progress in the industry. If you don’t know what’s changing, you won’t know what you need to learn. Follow influencers in the field and read industry newsletters. 

There are various certifications to upskill yourself as a data scientist. We’ve compiled a list of the best ones here

Related Read: How To Become a Data Scientist

Can You Learn Data Science on Your Own?

You can learn data science on your own with online courses or even YouTube videos. There is no dearth of learning materials on the Internet if you’re working towards a career in this field. 

That said, self-learning lacks structure, and you might not know what important elements you’re missing. Data science courses and bootcamps are a happy medium for those looking for independence and support, as they provide an experienced teacher and cohort setting to offer feedback.

Data Science FAQs

How Long Does It Take To Learn Data Science?

It depends on how you pace yourself, but it is recommended that you give yourself at least six months before you consider yourself a beginner data scientist. This will give you the opportunity to learn the requisite skills and implement them in the form of personal projects. 

Who Can Work in Data Science?

There really aren’t any limitations on who can work in data science. It is possible to work in the field even without a college degree. As long as you have the right theoretical foundations and projects that you can show recruiters, anyone can land a job in the industry. 

Related Read: How To Get Into Data Science (Without a Data Science Degree)

Is Data Science Hard To Learn?

Data science is not hard to learn if you choose the right learning methodologies and materials. Think about how you learn and find resources to accommodate that. For example, some may choose to teach themselves using videos, while others prefer mentor-led bootcamps. Don’t be afraid to experiment with a few different learning methodologies and commit to one only after you have evidence that it works for you. 

Is Data Science a Stressful Job?

According to US News, data science is a job with an average level of stress. You can make your job easier by better managing your tasks and communicating with your manager if you’re overwhelmed. Data science is more flexible than other jobs, so you could try working remotely or as a freelancer if more conventional working modes stress you out. 

Since you’re here…Are you a future data scientist? Investigate with our free step-by-step guide to getting started in the industry. When you’re ready to build a CV that will make hiring managers melt, join our 4-week Data Science Prep Course or our Data Science Bootcamp—you’ll get a job in data science or we’ll refund your tuition.

Sakshi Gupta

About Sakshi Gupta

Sakshi is a Senior Associate Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer and has experience working in the Indian and US markets.