**Summary: **Unleash your data skills! Learn the key steps to become a Data Scientist in 2024. Build a strong foundation in programming, statistics, and Machine Learning. Gain practical experience with projects and internships. Showcase your expertise with a Data Science portfolio.

**Introduction**

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:

Imagine treating a patient without undergoing a formal medical course.

Building reliable and durable structures as an unaccredited civil engineer is 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.

**Steps to Become a Data Scientist**

There are several online Data Science education platforms claiming to offer the best course. 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.

**Step 0 – Before you start**** **

While anyone can become proficient in 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, are you keen to build a career in the domain. You can assess yourself further by looking at the detailed version of the above questionnaire. Having established that Data Sciences is indeed your internal calling, let’s 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 to understand 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. At the same time, there are dozens of languages used in Data Science, such as Python and R programming language, which sit right at the top of the list. What makes us prefer Python over R

It is a lot easier for complete beginners to learn.

Python is very widely accepted in the industry.

A larger open-source community.

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**

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. 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.

**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 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:**

Types of distributions – with prime emphasis on the normal distribution and the Student’s t-distribution

Central Limit Theorem, and its usefulness as an underlying assumption

The concept of confidence intervals and confidence levels in making a prediction

How hypothesis testing works and how it mirrors the scientific method

Sampling from a population and estimating population parameters using sample statistics

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.

Data Science has immense potential, which has been 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 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).

**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 three subcategories:

**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 requires 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 using datasets available on Kaggle. Going forward, you can also start working on datasets from global organizations like the World Bank and the 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, each one of which claims to make you a Data Scientist. 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 lectures on-demand.

Live sessions are needed to clear doubts and provoke more exploration.

Mock interviews and resume preparation tips.

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

**Conclusion**

While this guide outlined a roadmap for 2022, the core concepts remain relevant for aspiring Data Scientists in 2024. The field is constantly evolving, so staying updated with the latest tools and techniques is crucial.

Remember, Data Science is a blend of technical skills and domain knowledge. Tailor your learning path to a specific industry if you have a strong interest. Most importantly, cultivate a passion for exploration and problem-solving. With dedication and continuous learning, you can unlock rewarding opportunities in this dynamic field.

**Frequently Asked Questions **

**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.

**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.** **

**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.