Data Science is a popular as well as vast field; till date, there are a lot of opportunities in this field, and most people, whether they are working professionals or students, everyone want a transition in data science because of its scope.
If you are a student or a fresher who wants to be a part of the data science domain but is a little bit confused about What to learn? How much to learn? So, this roadmap to learning data science will help you to resolve all your queries.
If you search “learning data science”, “a roadmap to learn data science”, or “how to become a data scientist as a fresher?” “Data science roadmap for fresher”, “Roadmap of Data Science”, “Roadmap for data science learning”, “Roadmap for Data Scientist”
So, you have come up with a lot of resources, blogs, and articles, and that talk about the mainframe technologies in data science, such as Python, SQL, Mathematics, Machine Learning, Deep Learning, excel, cloud technologies, and so many other things.
Each technology has its own depth as well as its usage, and if you give it a try, each and everything still has its depth. So, trust me, my friends, you end up losing so much time and energy.
You need to understand each and every technology with respect to their need in data science. You have to learn only those parts of technology that are useful in data science as well as help you land a job.
What to do next?
Don’t worry; you have landed at the right place; in this article, I will give you a crystal clear roadmap to learning data science. And this article is very focused on the important aspects of tools and technology which are useful in data science, and after reading this, you will have a good knowledge of things with respect to “What to learn and how much to learn in data science?”.
Prerequisite to follow this roadmap of data science
There are such prerequisites to follow this roadmap, your dedication of 2–3 hours per day till 6–8 months, where your first 4–6 months cover all your learning part and the last 2–3 months cover your end-to-end projects, resume building, networking, and applying for a job.
The very first thing to focus on in data science is Computer fundamentals, and in this section, you need to understand some basic Linux commands such as: create a directory, create the file, delete the file, open file, open directory, delete a directory, move from one directory to another, create an environment and for this, you need to understand the basic terminal usage in Linux or CMD in Windows.
So that later on, when you build some projects, these things will help you to smoothen your project-building process as well as help you to push your code on GitHub or some repository.
After the Linux or CMD commands, you can also read about API, like “What is API? and What role does it play?” Just basic knowledge of API.
Remember one thing you don’t have to master these things; basic knowledge is enough to go further.
This is one the most important part of the journey because this gives great exposure as compared to any pre-build tool, and with the help of programming, you can make your own tools.
In programming, You need to learn two types of language. One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases.
Python is a High-level, Procedural, and object-oriented language; it is also a vast language itself, and covering the whole of Python is one the worst mistakes we can make in the data science journey.
Python things like its Data Structures and their operations, Loops, Conditional Statements, Functional Programming, and Object Oriented Programming.
Note: Functional Programming and Object Oriented Programming will help you to build a clean & reusable code.
As I mentioned above, Python is a vast language which means it has rich community support, and you can find various types of libraries and modules as per your work.
In Data Science, we also use Python libraries such as Pandas, Numpy, Matplotlib, Seaborn, etc. After finishing basic Python, you need to focus on these libraries also.
Each library has its own functionality and depth so, learning these libraries with any data (data frame) makes your learning go in the right direction.
You can also do SQL scripting in Python itself with the help of some connector (basically a Python library that connects the Python Interpreter to MySQL)
In the previous section, I have mentioned two types of programming, one is scripting, and the other is Query language, and in this database fundamental segment, we will talk about the Query language.
There is one Query language known as SQL (Structured Query Language), which works for a type of database.
Database Fundamentals means you need to focus on the fundamental knowledge of databases like Relational Database (SQL) databases and Non-Relational Database (NoSQL); you don’t need to learn all SQL or NoSQL DB; instead, either one from both databases is enough to proceed further.
SQL Databases are MySQL, PostgreSQL, MariaDB, etc.
NoSQL Databases are MongoDB, Redis, Cassandra DB, etc.
For example, you can learn MySQL from SQL databases and MongoDB from NoSQL databases, because learning one database from each will give the familiarity of others also.
Because most of the DB’s have similar kinds of operations and flow, there are very few differences in similar databases. Just focusing on one DB from each will make your work easier.
Why do we need databases? We are not Database or Backend developers.
Yes, we are not Databases or Backend developers, but our domain is dependent on the data, and that is the reason we need to have knowledge of the databases so, we can at least manipulate or get the data from the databases.
You don’t need to go in-depth into these databases, Just CRUD operations are enough from SQL and NoSQL databases.
CRUD refers to Creating, Reading, Updating, and Deleting operations in DB’s, and in Data Science, our work is to manipulate the data and get the insights as per the use case we work on.
“HE is sitting between two MATs that have the qualification of ICS”, so this is the only simplest way to learn MATHEMATICS (spelling).
Most people are afraid after hearing the term Math, and in data science, predictive modeling is totally based on Math. So, having an understanding of mathematical concepts will make your journeys like bread and butter.
Mathematics for data science is not very complex, and don’t worry here you don’t need to solve any mathematical questions. Instead, having the conceptual knowledge of Linear Algebra, Single and Multivariate Calculus, Vector & Matrices, Statistics, and Probability is enough.
Statistics have two types one is Descriptive, and the other is Inferential. In Descriptive Statistics, you need to focus on topics like Mean, Median, Mode, and Standard Deviation.
In Inferential Statistics, you can learn P-Value, T-Value, Hypothesis Testing, and A/B Testing, which will help you to understand your data in the form of mathematics.
And don’t think that Statistics only has these topics; Statistics itself is a vast field, but whatever I write above is good enough to start with, and as you go further in data science, you’ll explore more and more.
In Probability, you can focus on Conditional Probability and Bayes Theorem, and for linear algebra and all conceptual knowledge is enough.
Note: Now, Start joining Data Science communities on social media platforms.
These communities will help you to be updated in the field, because there are some experienced data scientists posting the stuff, or you can talk with them so they will also guide you in your journey.
After learning math now, you are able to talk with your data. And if you combine Data Analysis and Math together, working on data as well as understanding the data is so smooth and easy.
Data Analysis also helps you to prepare your data for predictive modeling, and it is also a specific field in Data Science. There is a position called Data Analyst whose work is to analyze the historical data, and from that, they will derive some KPIs (Key Performance Indicators) for making any further calls.
For Data Analysis you can focus on such topics as Feature Engineering, Data Wrangling, and EDA which is also known as Exploratory Data Analysis.
Feature Engineering plays a major part in the process of model building. 70–80% of the work before the predictive model building has been taken by Feature Engineering itself.
First learn the basics of Feature Engineering, and EDA then take some different-different data sheets (data frames) and apply all the techniques you have learned to date. Because this is the only effective way to learn Data Analysis.
Note: Now write some articles or blogs on the things you have learned because this thing will help you to develop soft skills as well if you want to publish some research paper on AI/ML so this writing habit will help you there for sure.
This is also an important aspect in data science but not a necessary one, like if you have the basic knowledge of Web Scraping, so it’s good for you in some situations where you need to scrap the data from the internet for your work.
Where the knowledge of Web Scraping will work for you, and for doing web scraping, you can explore some Python libraries such as Beautiful Soup, Scrapy, and urllib.
With the help of web scraping, you can make your own data set to work on.
Machine learning is a type of artificial intelligence that allows software applications to learn from the data and become more accurate over time.
The following subheadings outline some of the fundamental concepts in machine learning
For more understanding, you can refer to this article: A-Z of Machine Learning
In Machine Learning, you should focus on such topics are:
In this type of learning, the algorithm is trained on a labeled dataset, where the correct output is provided.
The two most common types of supervised learning are classification, where the algorithm predicts a categorical label, and regression, where the algorithm predicts a numerical value.
In this type of learning, the algorithm is trained on an unlabeled dataset, where no correct output is provided.
The two most common types of unsupervised learning are clustering, where the algorithm groups similar data points together, and dimensionality reduction, where the algorithm reduces the number of features in the data.
These are used to evaluate the performance of a machine-learning algorithm. Some common metrics include RMS (Root Mean Squared Error), Confusion Matrix, AUC (Area Under the Curve), ROC (Receiver Operating Characteristic) curve, and Accuracy.
Hyperparameters are parameters that are set before the learning process begins. Tuning these hyperparameters can significantly improve the performance of a machine-learning model.
Common techniques for hyperparameter tuning include Grid Search, Random Search, and Bayesian optimization.
Machine learning tools and techniques are essential for data analysis and predictive modeling.
One powerful technique used in machine learning is the ensemble technique, which combines multiple models to improve accuracy and reduce overfitting.
Two common algorithms used in ensemble techniques are Random Forest and Boosting Algorithms.
Time Series analysis is also an important tool in machine learning for analyzing and forecasting data points over time.
Things to be learned: Ensemble Techniques such as Random Forest and Boosting Algorithms and you can also learn Time Series Analysis.
Deep Learning is a subfield of machine learning that focuses on training deep neural networks with multiple layers to improve performance on complex tasks.
Some popular libraries used for deep learning are Keras, PyTorch, and TensorFlow. These libraries provide pre-built functionality to train, test and deploy deep neural networks.
Deep neural networks can be further classified into Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). Generative Adversarial Networks (GANs) are deep neural networks used for unsupervised learning.
Transfer learning is another important technique in deep learning that involves reusing pre-trained models to improve the performance of a new model on a different task.
Things to be learned: Keras, PyTorch, TensorFlow, RNN, LSTM, CNN, GAN, and Transfer Learning.
Note: Start Reading Research Paper
The Next Section is not mandatory, it’s an optional one for freshers, but as we discussed earlier, Data Science is a very vast field. So, having a basic understanding of MLOps, Infrastructure such as Code, CI/CD, and Cloud are good enough for you.
But as a fresher, you can learn the things I have mentioned above and have a good grasp on those, as well as build some projects with the help of things you have learned so far.
Because building projects and getting your hands dirty in the Data field is the easiest way to learn Data Science.
MLOps, short for Machine Learning Operations, is a set of practices and tools used to automate and streamline the deployment, monitoring, and maintenance of machine learning models.
There are several platforms available for MLOps, including TensorFlow Extended, KuberFlow, and Amazon SageMaker.
TensorFlow Extended (TFX) is an open-source platform developed by Google that simplifies the process of deploying and maintaining machine learning models.
It provides end-to-end pipeline components for building scalable and reliable ML production systems.
KuberFlow is a cloud-native ML platform developed by Google that leverages Kubernetes and other cloud-native technologies to provide scalable and portable ML workflows.
It includes a suite of open-source tools and APIs that make it easy to build and deploy ML workflows on Kubernetes.
Amazon SageMaker is a managed service offered by Amazon Web Services (AWS) that provides a comprehensive platform for building, training, and deploying machine learning models at scale.
It includes a range of tools and features for data preparation, model training, and deployment, making it an ideal platform for large-scale ML projects.
Things to learn: TensorFlow Extended, KuberFlow, and Amazon SageMaker
Infrastructure as Code (IAC) is managing infrastructure through code, rather than manual processes.
Docker, a containerization platform, enables developers to package applications and dependencies in a portable container.
Kubernetes is a container orchestration platform that automates the deployment, scaling, and management of containerized applications.
Terraform is an open-source infrastructure as a code tool that allows developers to define and manage infrastructure using declarative configuration files, enabling efficient provisioning, change management, and collaboration.
By leveraging these tools, organizations can automate the deployment and management of infrastructure, improving scalability, security, and consistency.
Things to learn: Docker, Kubernetes, and Terraform
CI/CD, or Continuous Integration/Continuous Deployment, is a software engineering practice that aims to improve the quality of software development by automating the process of building, testing, and deploying applications.
Two popular tools used for CI/CD are GitHub Actions and Jenkins.
GitHub Actions is a flexible and powerful CI/CD tool that allows developers to automate their workflows in a single place. It offers pre-built actions and workflows, as well as the ability to create custom workflows using YAML files.
With GitHub Actions, developers can automate their testing, deployment, and other tasks directly from their GitHub repositories.
Jenkins, on the other hand, is an open-source automation server that provides hundreds of plugins to support building, deploying, and automating any project.
It is highly configurable and can integrate with other tools like Git, Docker, and AWS. Jenkins supports distributed builds and can scale horizontally, making it a popular choice for large-scale projects.
Things to learn: GitHub Actions, Jenkins
Cloud computing refers to the delivery of computing services over the Internet, providing scalable and flexible resources to businesses and individuals.
Three of the most popular cloud platforms are Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
AWS is a comprehensive cloud platform that offers a wide range of services, including computing, storage, and databases. It also provides tools for machine learning and data analytics.
GCP is another popular cloud platform that offers a variety of services, including computing, storage, and networking. It also provides tools for machine learning and data analytics, as well as specialized services for areas such as IoT and gaming.
Microsoft Azure is a cloud platform that offers a range of services, including computing, storage, and databases. It also provides tools for machine learning and data analytics, as well as specialized services for areas such as IoT and AI.
Things to learn: AWS, GCP, or Microsoft Azure anyone of them.
Data science is rapidly growing and has become an essential part of many industries. As a beginner or fresher, the roadmap to learning data science can be overwhelming due to the vast amount of information available.
By following the steps outlined in the article, you can learn data science in a structured and efficient manner. It is essential to have a strong foundation in mathematics, statistics, and programming, as well as hands-on experience with data analysis and machine learning algorithms.
Now, build some end-to-end projects, fine-tune your resume according to the projects you made as well make some connections for a successful and rewarding career in data science.