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The Data Science learning path is a collective set of curated courses that comprise a learning plan for achieving the required skills for the data scientist role. While the time limit to complete the learning path to become a data scientist can expect 8-9 months to get through all Data Science courses. It is known that people from diverse backgrounds with zero experience turn out to be good data scientists in just a year through learning smart coding.
This article will cover what it takes to learn Data Science to become a Data scientist. Understand what Data Science is, why it is needed, what are the roles of a Data scientist, the job description of a data scientist, how long it take to become one, what to study, the skills required, the job scope, and much more. You can go for a Data Science course in India to further pump up your Data Science learning.
Many of us visualize data as a matrix of numbers and characters organized in tabular form running endlessly. That is one way of imagining it, but this is not what defines data completely. Understanding what defines data in the modern world is the first step toward the Data Science self-learning path.
There is a much broader spectrum of things out there which can be classified as data. For instance, sales of a company, medical records of a patient, stock market records, tweets, Netflix’s list of programs, audio files on Spotify, log files of a self-driven car, your food bill from Zomato, and your screen time on Instagram. In fact, you reading this blog is also being recorded as an instance of data in some digital storage.
In 2018, the world produced 33 Zettabytes (ZB) of data, which is equivalent to 33 trillion Gigabytes (GB). In 2020, this number grew to 59 ZB and was expected to reach a whopping 175 ZB in 2025. Can you imagine the data that big?
How can companies leverage the data that they have to solve problems or innovate with current ideas? How can they make the data tell the story that is going to help them find the solution? How would one know what to sell and to which customers, based on data?
This is where Data Science comes into the picture. Data Science is a field that uses scientific methods, algorithms, and processes to extract useful insights and knowledge from noisy data. Data Science is how the modern world leverages data to answer questions with the help of advanced computational systems and extensions of statistical methods. These systems and methods can be applied to massive amounts of data. To know more about the beginner's data science learning path, check out Data Science online courses.
Before you dive into the world of Data Science with all guns blazing, I would ask you to take a step back, breathe and think. Ask yourself why you want to learn Data Science. This is important because this will help you understand what areas to focus on while following the Data Science Learning Path.
To help you find the purpose, I suggest you think about what excites you the most. Is it the part where you turn raw data into useful ones, or it the part where you engineer new features out of the existing ones in order to help create suitable models? Is it Data Visualization where you present your findings in form of a report or a Dashboard, or is it Machine Learning where you build ML models and deploy them?
You should also think about what kind of Data is interesting for you. For some, it does not matter what the data is about. For some of us are more inclined towards a particular domain of data. For example, you might be interested more in healthcare, where you get to deal with medical or clinical data. You could be interested in Financial data which includes lots of numeric variables.
Data Science is an advanced skill, and it's important to know why you are learning it. You can enhance your resume well if you can target the skills you want to learn more than the others. Try to learn every skill but then master one of them to highlight it as your superpower so to speak. Learn the art of problem-solving using the tools you learn. You might know both SQL and Python for example. But you should focus more on sharpening one of them.
Most people will ask you to learn programming as the first step toward Data Science, but in my experience, it’s equally important to learn SQL. This is because it’s widely used across industries as a language to manipulate and analyze data. Many companies expect you to know SQL as the basic requirement for Data Science roles. This will also give you an idea of how to visualize tabular data and perform various functions to fetch the required information from the data.
Python and R are both great choices in data science. But I suggest that you get comfortable with Python, as it is widely used in the industry and comes with a lot of Data Science friendly packages. Another reason to learn Python is that it is beginner-friendly and quite easy to code in Python. To begin with, you can install Anaconda distribution, which simplifies the process of package installation. You can then start coding on Jupyter notebooks, a terrific way to code and store your projects with output. You will see what I mean when you will use Jupyter.
Now that you know how to code in Python start picking toy datasets to perform analysis using Python. Learn about Dataframes, Pandas, and Numpy to begin with. Learn how to import data, to visualize data using libraries like Matplotlib and Seaborn. Try to perform basic functions like changing column names, counting rows, checking counts of different values, transposing, grouping, adding new columns, deleting existing columns, etc.
Now that you have learned how to do data analysis and manipulation in Python, it's time for you to learn the most romanticized thing in the whole of Data Science - Machine Learning. Scikit-Learn is one of the most important Python libraries for building Machine Learning models. Start by understanding the basics of simple models like Linear Regression, K-Nearest Neighbors, etc. Learn about Supervised and Unsupervised learning methods. Learn about loss functions and hyperparameters. Learn about feature engineering and feature transformation.
It is one thing to know about Machine Learning algorithms and how to call their functions. But what gives you an edge over your peers is the in-depth knowledge of various Machine Learning algorithms. It’s important that you understand the underlying logic and mathematical reasoning behind these algorithms. Some of these algorithms are built over statistical methods and theories, which makes it imperative that you understand them before applying them to build models. Once you do that, you’ll be able to answer questions like - which model is the best for your dataset, how you can interpret the results of your model, how generalized your model is, and what are the top features.
This is the most crucial step. After attaining all the knowledge, you need to keep yourself updated with the latest trends. You also need to stay in practice if you want to be good at Data Science. Keep giving yourself challenges to do more and learn more. You can participate in various data science competitions and hackathons to keep yourself motivated and to learn from the community. Kaggle is one of the greatest platforms for budding Data Scientists to learn and grow.
It is the first skill to have if you want to succeed as a Data Scientist. You should be well versed in one of the programming languages; it’s better if it’s Python or R. All the processes, like data cleaning, analysis, and modeling, rely on your programming skills.
Structured Query Language or SQL is one of the key skills to have if you are willing to become a successful Data Scientist. Many companies, even today, rely on SQL for most of their data wrangling and analysis work.
It is one of the fundamental skills that many people ignore. Data Science is about data and numbers, and mathematics is at the very core of it. It is also important to know the underlying math to understand the various ML algorithms. Linear Algebra and Probability are two of the most important Math topics you should focus on. Basic Calculus can also come in handy if you work with advanced Machine Learning and Deep Learning methods.
Statistics is yet another important skill to have if you are willing to be a Data Scientist. You should know topics like central tendencies, probability, PDF, CDF, etc.
This refers to the process of data cleaning, also known as data munging, which means cleaning and transforming the data into a more readable format.
Visualizing data has become even more important for a better understanding of outputs and emerging patterns inside the data. It is one of the skills every Data Scientist should have so that she can tell the story through visuals that the data is trying to show.
It is expected from a Data Scientist she knows what Machine Learning is and how various algorithms and ML libraries can be used based on the kind of data and the problem to be solved.
Let's talk about some useful resources for Data Science that can come in handy on your journey to become a Data Scientist. Let's start with some preparation tips.
1. Practical Statistics for Data Science
This book will give you all the required knowledge you need on statistics to begin your Data Science journey.
2. Introduction to Machine Learning with Python
A guide for Data Scientists - It's a great beginner-friendly book that introduces you to the world of Machine Learning with easy explanations of various algorithms implemented in Python.
3. Python for Data Analysis
This book will come in handy if you want to learn Python programming for Data Analysis.
Now that you have seen how Data Science is needed to extract actionable insights from raw data, it Is important to know what is expected from you as a Data Scientist. So let us talk about some of the major roles and responsibilities of a Data Scientist.
A Data Scientist is responsible for providing data solutions to the business problem, and for that, she needs to define a strategy to help achieve that goal. It is the role of the Data Scientist to plan and design a system that can process the given data all the way up to the final stage.
A Data Scientist must perform data analysis to draw insights and patterns. It is up to the data scientist to choose the right methods based on the kind of data and the problem at hand. The Data Scientist will develop models for problems like regression, classification, projection, forecasting, clustering, etc.
A Data Scientist can also be expected to manage the whole project, as it is likely that the whole project revolves around her standpoint and strategies. She is responsible for constructing the base of the project keeping in mind the future aspects and the technical abilities that would be needed.
This is one of the key roles of any Data Scientist. A Data Scientist is not working alone; she needs to collaborate with other individuals and teams like Data Engineers to understand the data requirements, Senior Data Scientists to communicate high-level obstacles, and relevant stakeholders to keep aligned with the business needs and to also enhance decision-making.
Learning never ends for a Data Scientist. It is a prime responsibility of a Data Scientist to keep learning to stay updated with the latest trends and state-of-the-art technologies. Data Scientist is also expected to transfer the knowledge to other colleagues and junior Data Scientists in the team.
Data Scientists can earn from somewhere around $60,000 to $140,000 per year. The median Salary of a Data Scientist is $91,000, these are the numbers in the US market. In India, you can earn up to Rs.50 Lakhs working as a Data Scientist with an average pay of around Rs.11 Lakhs.
Various other job roles associated with Data Science are also equally exciting and rewarding. Let us look at some of them and their salaries:
|Machine Learning Engineer||$114,826|
|Machine Learning Scientist||$114,121|
|Business Intelligence Developer||$81,514|
Let us see the top industries hiring Data Scientists the most:
Data Science is widely used in this sector. Major applications of Data Science in BFSI include Fraud Detection, Risk assessment, Customer Segmentation, Credit Scoring, and Algorithmic Trading. Some of the top employers are JPMorgan Chase, Citi Group, HSBC, Barclays, etc.
Data Science has taken the healthcare industry to a whole new level simply by leveraging the power of healthcare and clinical data. Data Science is now used for applications like an easier diagnosis of disease, cancer detection, customized care, doctor-patient relationship enhancements, and a lot more. Some of the major employers in Healthcare are Sanofi, GSK, GE Healthcare, etc.
This industry has recently been growing impressively, and one of the key engines driving this sector is Data Science. Top applications of Data Science in Media and Entertainment include Customer Sentiment Analysis, Hyper-targeted advertisement, Smart Recommendations, Personalised Content Experiences, etc. Top recruiters in this industry are Netflix, Hotstar, Hindustan Media, NDTV, etc.
Another important industry that leverages Data Science for applications like analyzing customer behavior, creating recommendation systems for marketing, improving customer experience using predictive modeling, etc. Some of the top employers of Data Science in retail are Amazon, Flipkart, Walmart, etc.
Science is widely used in understanding the nature of malicious attacks, predicting them in advance, and to also prevent them from happening. Some of the top companies hiring in this industry are Accenture, IBM, Meta, Microsoft, Cisco, etc.
The automotive industry is using advanced Data Science these days to modernize and revolutionize the production and use of automobiles. From optimizing production lines to building self-driving cars, Data Science and Artificial Intelligence have become an integral part of the Automotive industry. Some of the top recruiters of Data Scientists in this industry are Volkswagen, General Motors, Ford, etc.
Yet another sector that is taking advantage of Data Science is telecommunication. Using data science, they can make personalized offers to customers, allocate network resources effectively, detect fraudulent activities, design location-based promotions, and optimize pricing. Top telecom companies hiring Data Scientists are Bharti Airtel Limited, Reliance Jio, BSNL, Vodafone, etc.
In a world where almost everyone is on social media, digital marketing has become one of the most important industries touching peoples’ lives every day. Digital marketing has modernized itself with the help of Data Science. Companies can now leverage big data to predict users’ behavior and accordingly make better business decisions, identify patterns and trends that aid in product innovation, and interact with users more effectively by segmenting the market. Top recruiters in this space are Meta, Amazon, Google, etc.
The list does not end here. There are many more industries out there that are looking for Data Scientists to use the power of data and innovate in terms of customer experience and profit maximization.
Data in itself is not useful if it can’t be converted into valuable information. Data Science enables organizations and companies to effectively understand big data from various sources and derive valuable and actionable insights to make smarter and better data-driven decisions. Data Science can be widely applied and used in various industries, including but not limited to marketing, healthcare, finance, banking, policy work, and more. This explains why Data Science is needed.
Here are some examples of how Data Science is used across various domains:
These examples are hardly the tip of the iceberg. There are a whole lot of domains and applications where Data Science is needed for businesses to thrive.
We hope you enjoyed reading this data science learning path blog and that it helped you feel more confident about Data Science and all the aspects needed in order to become a Data Scientist. There is a lot of data scientist job scopes out there for you to explore. All you need is a hunger for knowledge and a positive attitude. If you follow the above data science learning path steps sincerely, nothing can stop you from becoming a successful Data Scientist. How long does it take to become a Data Scientist? Well, that depends on you and your learning curve. But you can easily look into 6 months of preparation before you are job ready. You can also check KnowledgeHut’s Data Science course in India as an option to kickstart your journey.
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While Data Science is a field that focuses on how to process, analyze, and model data, computer science is a much broader field having a wide variety of applications to it. So comparing difficulties, Data Science will come out to be a much easier field than Computer Science.
You do not need to know complete Python in order to pursue Data Science. You should be familiar with the fundamentals of programming with Python and the relevant libraries that are used for various Data Science and Machine Learning functions. Some of the most important libraries to know are Pandas, Numpy, SciKit Learn, Matplotlib, Seaborn, etc.
It depends on your academic background. If you come from a non-technical background, it can be challenging for you to learn Data Science. But if you come from a technical background, it should not be much of a challenge to pick things up.
Yes, you can become a Data Scientist with no experience. All you need is a start and an attitude to keep learning.
Considering a Bachelor’s degree, a Computer Science or related degree can prove to be the best. If you are considering a Master’s, then a degree in Data Science or related fields can be helpful to boost your career in Data Science.