Six Steps to get into Data Science in 2022

Do you want to know how to learn data science? What are the steps to become a data scientist? If yes, this article is for YOU!

The popularity of data science has risen exponentially in the last decade. It was dubbed the sexiest job of the 21st century because of its inherent multidisciplinarity and wide scope. The problems that can be solved using the data science tools of today include everything from (seemingly mundane) maximizing revenue for a small manufacturing firm to building truly self-driving cars.

The low barrier to entry is referred to very often, where proponents explain how to get into data science without a degree. This is in stark contrast to other professions:

  • Dare imagine treating a patient without undergoing a formal medical course.
  • Building reliable and durable structures as an unaccredited civil engineer are unimaginable.
  • Flying with a “self-trained” pilot isn’t an encouraging proposition.

Data science, despite being a very vast domain, is easy to start with. The prospects are good and so is the remuneration. Given this fact, one is accustomed to believing that the job market would be replete with data scientists, data analysts and other data professionals. This isn’t the case though, as 35% of companies have difficulty finding workers with a data science skill set.

What explains this shortfall? The reason, in our opinion, is very obvious. A single generic google search with a query resembling “How to get into data science with no experience” will show how countless companies are promising to make a data scientist out of you. There are numerous advertisements; there is no dearth of free resources.

However, having so many options is bound to confuse someone who is just getting started with data science. We earnestly feel this need not be the case. Thus, we are going to describe the best way to learn data science, in six easy steps.

Six Steps to Become a Data Scientist in 2022 – Table of Contents

Step 0 – Before you start

While anyone can become proficient at data science, we advise you to take a step back and assess whether you want to get into the domain. To arrive at an answer, consider the following questions:

  • Do you like to solve complex problems?
  • Are you good at learning new tools?
  • Does the prospect of working with experts from diverse domains appeal to you?
  • Is elucidation and breaking down stuff for others a favourite task of yours?
  • Would you call yourself curious?

If the answer to these questions is affirmative, you do have the penchant for building a career in the domain. You can assess yourself further by looking at the detailed version of the above questionnaire. Having established that data science is indeed your internal calling, let’s have a look at the ‘how’.

Step 1 – Learn the basics of Python

Before we get to “why Python” or any other programming language, let’s spare a moment on understanding what makes data science rely on one. 

  • Data Science is all about making sense of large amounts of data. 
  • This requires high computational power.
  • It also requires us to have the ability to instruct the machine to do tasks for us.

Modern programming languages fulfil all of these requirements. While there are dozens of languages used in data science, Python and the R Programming Language sit right at the top of the list.

 

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What makes us prefer Python over R?

  • It is a lot easier to learn for complete beginners.
  • It has very wide acceptance in the industry.
  • A larger open-source community looks after and develops new packages and libraries.
  • It is a multipurpose programming language.

You can read more about Python v/s R. There is vehement disagreement and a raging discussion which you can look into. 

Coming back to what you need to do before proceeding further:

  • Understanding the philosophy of programming – especially if you have never written code before.
  • Installing an integrated development environment (the place to write and execute programs). We prefer Jupyter Notebooks in Anaconda.
  • Learning the basics like variables and operators.
  • Reading about and implementing conditionals, functions and loops.
  • Grasping Python’s in-built data structures.

We also have a detailed guide for learning Python for data science from scratch.

Step 2 – Love the Libraries

Pandas, Matplotlib and Numpy

Credit: NumPy, Pandas and Matplotlib

Python has various libraries for various tasks. Think of them as hundreds of lines of code that have been written by the Python community to empower you and to improve your workflow. As far as data science is concerned, it is important to have the ability of:

  • Handling data intuitively
  • Visualizing data in the form of charts

NumPy, Pandas and Matplotlib enable doing all of this.

NumPy

The ability to work upon thousands of rows of data can be achieved with a sleek use of linear algebra. This is what NumPy does, with an abundance of mathematical and logical operations on Python’s n-arrays and matrices. 

It performs calculations at a lightning pace, while still ensuring that the signature lucidity of Python is not compromised. Primarily, as a data science beginner, aim to learn and practice creating NumPy arrays, using the reshape function, slicing, broadcasting, using min( ), max( ), sum( ), exp( ) functions and performing scalar operations.

Pandas

While NumPy lets you handle data at scale, here are a few propositions:

  • What if it had been possible to see data the way you see it in Excel or SQL? 
  • What if you could import data into your notebook from different file formats?
  • How about having excellent data manipulation, aggregation and pivoting capabilities?

Pandas is the one single answer to all these questions. It addresses these requirements by leveraging dataframes, which are tabular representations of data in Python, with the look and feel of Microsoft Excel. 

In addition to accessing data from external files, you can use its large repository of functions for deriving insights at the drop of a hat. A few functionalities a beginner should aim to imbibe include slicing, grouping, aggregation, joining, concatenation, merging and pivoting data, among others.

To its credit, Pandas integrates seamlessly with other libraries like Matplotlib and NumPy, which make it a crucial piece of the data science puzzle.

Matplotlib

A picture is worth a thousand words. Using data science for solving a question necessitates its visualization as the first step, which allows inspection and gaining an understanding of the overall trends. While Python gives you multiple options for a visual representation of data, Matplotlib stands out for its simplicity and flexibility.

It helps you create charts, graphs and animations, which enable practitioners to efficiently perform preprocessing. It works very well with NumPy and Pandas, among various other libraries. Here’s a logistic chart we created using Matplotlib, wherein 1 and 0 denote whether an SAT score was sufficient for getting admitted to a given college:

If these reasons couldn’t convince you, what if we told you that:

Step 3 – Specialize in Statistics

Statistics is a mathematical field of study that concerns itself with the collection, analysis, exploration, interpretation and presentation of data. It forms the bedrock of data science, with many of the seemingly perplexing concepts derived directly from core statistics.

Before you start learning statistics, make sure that you are thorough with basic Probability and Combinatorics. Simply stated, these explain how an element of chance is inherent to occurrences in everyday life and that there are multiple ways in which an event can be said to occur.

In statistics, make it a point to learn:

  • Variables and their types – dependent and independent
  • Measures of Central Tendency – Mean, Median, Mode, etc.
  • Position, viz. rank, percentile, quartiles, etc.
  • Histograms and their usage in statistics

After you are done with the basics, proceed to absorb:

Ensure that you are able to make sense of both descriptive and inferential statistics while relating them to the libraries that you had learned before. Everything is connected in data science and this is one of the many examples where you see this at play.

Step 4 – Meet Machine Learning

Machine learning represents the ability of machines to perform complex tasks without explicit instructions explaining how to do so. From your favourite search engine to fraud detection systems, machine learning is a pervasive part of our day-to-day life in the current day and age.

Data Science has had immense potential, which was translated into performance with the aid of machine learning. The analysis of data underwent a revolution with the help of models, even as methods of its collection and presentation made incremental gains.

Think of a relation y = f(x). Here, x is the independent variable whereas y is the dependent one. A machine learning model aims to emulate the relation with the help of several (x, y) pairs. A real-life example would be y being a person’s salary while x is the number of years they had studied. 

Quite obviously, we can have multiple independent variables (also known as features). For instance, a person’s salary can be estimated more accurately by considering factors like their GPA in their college, their field of study, their institution’s QS World University Rankings, years of experience, marital status, etc.

An ML model makes use of a learning process, which is an algorithm that allows the model to emulate the data’s behaviour (the relation between the independent variable(s) and the dependent one). For this section, one needs to:

Step 5 – Dive deeper into Machine Learning’s types

Machine Learning is classified in numerous ways. One of the most popular ones is to divide it into the three subcategories:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Fresh beginners should aim to gain an understanding of the first two types of learning first. The detailed contents to be mastered are mentioned next.

Supervised Learning

This is the most instinctive kind of machine learning, where both features (independent variables) and targets (dependent variables) are available. In other words, we can check and compare our model’s output with the actual answer to measure performance.

The data used in supervised learning is thus known as labelled data. The most commonly used renditions of supervised learning include linear regression and logistic regression. There are a plethora of assumptions in regression but the power it places in your hands is unthinkable.

Other topics you might fancy looking at include random forests, with topics like classification trees, bagging and boosting being the main ones. Once you are done with this part, you can truly start applying your newfound knowledge to more and more complex problems. After all, the best way to learn data science is by doing it.

Unsupervised Learning

Envisage the possibility of not having an explicit target. Letting the algorithm loose on a targetless dataset may seem to be a bleak endeavour. However, many invaluable insights can be obtained this way. 

For instance, one of the most famous paradigms of this discipline is clustering, which groups data points on the basis of some underlying similarity. It would look something like this:

Learning clustering allows you to design recommendation systems, which are used:

  • By online marketplaces, to recommend products to you
  • For playlist generation by audio and video content providers like Spotify
  • To recommend content on social media feeds
  • For proposing suitable dates on online dating sites

Step 6 – Apply and Iterate

Data Science needs a lot of commitment and practice from your side. Implementing regression and clustering using the libraries you have learned is as crucial as learning them in the first place. 

You can practice from datasets available on Kaggle. Going forward, you can also start working on datasets from global organizations like World Bank and World Trade Organization.

You can proceed to dive deeper into the subject, by approaching niche areas like deep learning, natural language processing, image recognition, etc. However, throughout your career, this is a broad template you shall need to follow, with every new thing you learn.

Bonus

There are thousands of options out there for becoming proficient in data science, with each one of them claiming to make you a data scientist. However, there are several issues with these MOOCs:

  • Their courses do not cater to the unique requirements of individual students.
  • Assignments do not test the clarity of what you have learned.
  • There is a lack of guidance when it comes to building projects.
  • Post-course guidance is minimal, which leaves learners in jeopardy.
  • The depth of the subject matter covered may not be optimal.

This is where you can take advantage of our courses. We adequately address all the aforementioned pain points. In addition to these advantages, you also get:

  • Lifetime access to 45+ hours of lectures on-demand.
  • 90+ hours of total course material, including assignments and projects.
  • Live sessions for clearing doubts and provoking more exploration.
  • Mock interviews and resume preparation tips.

Check out our most popular course variant, which gives you all these advantages. You can head to the courses tab in the navigation bar and check out the various options available, which are built for:

You can sign up for the free data science course to see how it feels and take it from there. Wishing you the very best!

FAQs

How to get into data science with no experience?

Data Science demands dedication from you, not experience. You can start from scratch and still become a good data scientist, at any point in your fledgling career. All you need to do is learn with a thought-out strategy, which is where mentors can help.

How to get into data science without a degree?

Many courses online demand a degree in B.Tech or B.Sc. However, data science doesn’t need a degree from you. Not having a degree won’t be a deterrent, if you set your heart to it. Tesla’s data scientist studied Petroleum Engineering in his undergrad, for example.

What qualifications do you need to be a data scientist?

A good foundation in Probability and Statistics, with sound knowledge of Python and its libraries for implementing data science, are basic requirements. Beyond this, you may need domain knowledge, soft skills and further expertise. Read more to understand in detail.

How do I start a career in data science 2022?

Follow these steps:

  • Be good in probability and statistics before you begin.
  • Learn the basics of Python programming language for data science.
  • Go for Python’s most used libraries for data science – NumPy, Pandas and Matplotlib.
  • Learn Machine learning basics.
  • Master linear and logistic regression, followed by techniques like clustering.
  • Implement these skills and keep learning new ones you desire.

Is Python necessary for data science?

Python isn’t the only language data scientists use; however, it is the most popular and the most widely-used one. It gives you various advantages which other programming languages cannot, with its large community of open-source developers. Read this guide for learning Python as an absolute beginner.

What is a data scientist salary?

According to Ambitionbox, the average data scientist earns 11 LPA (₹11,00,000 per annum), with a take-home/in-hand component of about ₹80,000 per month. The salary climbs up quickly with more experience. Glassdoor places the average annual salary in a similar range.

How do I land my first data science job?

After learning all the prerequisites, you should build some projects. Make them public on GitHub and have a portfolio online to reflect your progress. These can now be listed on your resume while you apply for jobs on websites like LinkedIn and other job portals. Keep applying, sit for a score of interviews and you’ll soon land your first job.

Can I become data scientist without coding?

You do not need to be a specialist programmer to get into data science. However, to have a lasting data scientist career, you should aspire to learn to code. It isn’t very difficult if you set your heart to it. However, as AutoML and no-code solutions keep gaining more prevalence, this might change in the future.

Can I teach myself data science?

Yes, you can! With the right guidance and topics’ learning sequence, you can learn and master data science all by yourself, with a PC and an internet connection. We have referred to autodidactism as being important often.

Is it too late to become a data scientist?

Probabilistically, you are not! People have been known to switch into data science very late in their careers. On the other hand, young collegegoers (and even high schoolers) often opt for data science despite not pursuing a degree in the same. This shows that it is never too late.

Is data science still in demand?

Yes! LinkedIn talks of 40,000+ jobs in data science in India, at the time of writing this article. Glassdoor has a high number of listings too. About 92% of hiring managers have complained of the existence of a demand-supply gap while recruiting for data science roles.

Can I start a career in data science with no experience?

Yes, you can! There’s always a day one in any career you opt for, and that holds true for data science as well. The best way to learn data science is by doing it and this article tells you what exactly you need to do in order to get into data science without having any experience.

Can I become a data scientist without a degree?

Yes, you can! Most data scientists do not have a degree in even computer science, leave alone data science. This shows that learning data science without having a degree in it is possible and entirely feasible, with the right kind of learning and practice.

Ayush Pareek

I am a programmer, who loves all things code. I have been writing about data science and other allied disciplines like machine learning and artificial intelligence ever since June 2021. You can check out my articles at pickl.ai/blog/author/ayushpareek/

I have been doing my undergrad in engineering at Jadavpur University since 2019. When not debugging issues, I can be found reading articles online that concern history, languages, and economics, among other topics. I can be reached on LinkedIn and via my email.

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