Data science has emerged in recent years as a rapidly growing field with the potential to revolutionise the way we live and work. Data science has become an essential component of many industries, ranging from healthcare and finance to marketing and social media. It’s an exciting time to be a data scientist or to start a career in this field as we enter 2023. However, with so many tools, technologies, and programming languages to learn, deciding where to start can be difficult. We’ll provide a comprehensive data science roadmap for 2023 in this blog post, which will walk you through the essential skills, tools, and techniques you’ll need to master to become a successful data scientist.

So, whether you’re a beginner or a seasoned pro, this roadmap will help you navigate the ever-changing landscape of data science and advance your career.

Contents

*Note: Before taking any of the courses, I strongly advise you to read this article on Data Science Career Paths & Tips to Choose the Right One. Also make sure you have thoroughly read the entire Roadmap. This is because some of the later courses may cover all the topics that are mentioned earlier in this Roadmap. By reviewing the entire Roadmap, you can select the courses that best suit your individual needs.

* Also note that there is no order for using the resources mentioned in this roadmap UNLESS SPECIFIED OTHERWISE. Always remember that this is just a Guide and you may use the resources as per your liking and needs. Please read the contents of the resources carefully before choosing any. I can assure you that all the resources mentioned in this roadmap are carefully selected after research. Although we are not responsible for any loss of money you may encounter by buying courses or books suggested in this roadmap.

1. Learn about the Basics of Programming

Anyone interested in a career in data science should learn the fundamentals of programming. Understanding programming is essential for working with large datasets and implementing machine learning algorithms in data science. Data scientists must be familiar with programming languages such as Python(Preffered over R if you are intrested in Machine Learning more than Data Analysis), R, and SQL, as well as fundamental concepts such as variables, loops, and conditional statements.

Data scientists can build efficient and scalable programs, perform data analysis, and solve complex business problems by learning programming basics. Furthermore, a solid programming foundation will make it easier to learn advanced topics like machine learning and deep learning.

Important Topics

1. Data Structures
2. Control Structures
3. Functions
4. Object-oriented Programming (OOP)
5. Error Handling
6. Libraries and Packages
7. Debugging

Now let’s see some of the best resources you can use to learn about these.

Courses

Introduction to Computer Science – Harvard’s CS50

CS50 is an introductory course to computer science and programming from Harvard University, suitable for those with or without prior programming experience. The course emphasizes problem-solving, correctness, design, and style. Topics covered include computational thinking, abstraction, algorithms, data structures, and computer science in general.

The course begins with the fundamental language of C, followed by Python, SQL, HTML, CSS, and JavaScript. Problem sets are inspired by various fields, such as the arts, humanities, social sciences, and sciences. The course aims to teach students how to program fundamentally and how to learn new languages.

What you’ll learn

• Scratch
• C
• Arrays
• Algorithms
• Memory
• Data Structures
• Python
• SQL
• HTML, CSS, JavaScript
• Flask
• Emoji

The course videos of an older version of the same course (2018) is available on YouTube for FREE.

CS50’s Introduction to Programming with Python

This is a self-paced free course that will introduce you to the world of programming using the most popular language used for Data Science, Python! It is designed for both beginners and experienced programmers who want to learn Python. It covers topics such as variables, conditionals, loops, functions, debugging, unit tests, and file input/output. Exercises are inspired by real-world programming problems.

No software is required except for a web browser or a personal computer. This course is part of the CS50x curriculum, which focuses on computer science and programming with various languages including C, Python, SQL, and JavaScript. However, CS50P focuses exclusively on Python and can be taken before, during, or after CS50x.

What you’ll learn

• Functions, Variables
• Conditionals
• Loops
• Exceptions
• Libraries
• Unit Tests
• File I/O
• Regular Expressions
• Object-Oriented Programming

All the videos for this course are available on YouTube and all the Slides, source code, and etc. used for the course are available at https://cs50.harvard.edu/python.

Books

Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming

The book begins with teaching fundamental programming concepts like variables, lists, classes, and loops through engaging exercises. It then progresses to building interactive programs and learning best practices for code testing. The later chapters focus on applying this knowledge to three exciting projects: a 2D arcade game similar to Space Invaders, a series of responsive data visualizations using Python libraries such as Pygame, Matplotlib, Plotly, and Django, and a personalized web application that can be deployed online.

This is one of the best books to read if you want to learn Python programming and get started on your Data Science journey.

Buy it online from here

That’s all the resources you would need to get started with Python for Data Science. Now let’ move on to the next skill required for Data Science

2. Learn Math’s & Statistics for Data Science

Data science is built on math and statistics, which are fundamental to the field. It offers the tools required to accurately analyze and interpret data, enabling reasoned decision-making. For data scientists to create models, derive insights, and make predictions from data, they must have a solid understanding of statistical concepts like probability, regression, and hypothesis testing. Mathematics, especially linear algebra and calculus, is necessary to understand and implement algorithms such as gradient descent and singular value decomposition that underpin machine learning. Without a strong understanding of these concepts, data scientists may struggle to derive meaningful insights from data, leading to incorrect or biased conclusions.

Therefore, a solid grasp of math and statistics is essential for success in data science. So let’s see some of the best resources you can use to learn about these.

Important Topics

1. Probability theory
2. Linear algebra
3. Calculus
4. Multivariate statistics
5. Optimization
6. Bayesian statistics
7. Nonparametric statistics
8. Statistical learning theory

Now let’s see the resources you can use to learn these topics.

Courses

Statistics for Data Science and Business Analysis

The course is designed for individuals who wish to pursue a career in data science, business intelligence, or become a marketing, business, or data analyst. The course aims to provide a comprehensive and practical understanding of statistical analysis, covering major topics and skills necessary to succeed in the industry.

The course distinguishes itself from others by emphasizing critical thinking abilities over software automation. It offers a structured program that explains the reasoning behind commonly used statistical tests, and students will learn how to visualize data and analyze it using scientific language. The course features high-quality animations, handouts, quizzes, case studies, and glossaries, as well as excellent support, including a one-day response time.

The course offers an unconditional 30-day money-back guarantee, and students will learn from a knowledgeable instructor, adept mathematician, and statistician who has competed at an international level. Overall, the course offers valuable skills that can lead to career growth, higher income, job security, and a dynamic and challenging work environment.

What you’ll learn

• Understand the fundamentals of statistics
• Learn how to work with different types of data
• How to plot different types of data
• Calculate the measures of central tendency, asymmetry, and variability
• Calculate correlation and covariance
• Distinguish and work with different types of distributions
• Estimate confidence intervals
• Perform hypothesis testing
• Make data driven decisions
• Understand the mechanics of regression analysis
• Carry out regression analysis
• Use and understand dummy variables
• Understand the concepts needed for data science even with Python and R!

Mathematics for Machine Learning Specialization

This specialization consist of 3 courses that aims to bridge the gap between mathematical concepts and their application in Machine Learning and Data Science. The first course focuses on Linear Algebra, followed by Multivariate Calculus, and Dimensionality Reduction with Principal Component Analysis. The courses require knowledge of Python and numpy and culminate in mini-projects that apply the learned concepts to real-world problems.

By the end of the specialization, students will have gained the necessary mathematical knowledge to advance in their machine learning studies.

What you’ll learn

• Implement mathematical concepts using real-world data
• Derive PCA from a projection perspective
• Understand how orthogonal projections work
• Master PCA

Even though you can enroll in this specialization for free, you have to pay an amount to receive the certificate. However if you wish to receive the certificate for FREE! Check out this article below:

*See how you can get this course for FREE

YouTube Channels & Websites

Khan Academy

Khan Academy is a free website with a focus on mathematics that provides comprehensive courses, interactive lessons, and adaptive practice tools, making it a great resource for anyone looking to learn math for data science. The site offers clear and concise step-by-step explanations, examples, and video tutorials that cover foundational math concepts from basic arithmetic to calculus and linear algebra. Khan Academy is accessible in multiple languages, making it an excellent resource for learners on a budget worldwide.

Overall, Khan Academy offers an engaging and effective way to build the foundational math skills necessary for success in data science.

StatQuest With Josh Starmer – YouTube

StatQuest With Josh Starmer is a YouTube channel that features entertaining and educational videos about statistics and data analysis. The channel, which was founded by Josh Starmer, a statistician and data scientist, offers a wide range of videos on topics such as probability, hypothesis testing, regression analysis, machine learning, and more. The videos are presented in a straightforward manner, with simple language, clear visual aids, and practical examples.

The goal of the channel is to make statistics available to everyone, regardless of background or level of expertise. StatQuest With Josh Starmer is an excellent resource for anyone looking to learn or refresh their statistical knowledge, with over 300 videos.

Books

Advanced Engineering Mathematics – by Erwin Kreyszig

“Advanced Engineering Mathematics” by Erwin Kreyszig is a comprehensive textbook that covers a wide range of mathematical topics relevant to engineering, physics, computer science, and other technical fields. The book is structured in a way that allows readers to easily navigate through the different topics, and it includes numerous examples, exercises, and applications. The textbook covers topics such as linear algebra, differential equations, partial differential equations, Fourier analysis, vector calculus, complex analysis, numerical analysis, optimization, and probability and statistics. Kreyszig’s approach to presenting mathematical concepts is known for its clarity and accessibility, making the book a valuable resource for both students and professionals who need to apply mathematical concepts in their work.

“Advanced Engineering Mathematics” is widely used in universities around the world as a reference book and a course textbook. Overall, the book offers an excellent introduction to advanced mathematical concepts for those studying or working in technical fields.

Practical Statistics for Data Scientists

“Practical Statistics for Data Scientists” is a practical guide to statistical analysis for data scientists. The book covers topics such as data exploration, regression analysis, hypothesis testing, and machine learning, with an emphasis on practical applications. The authors provide clear explanations of statistical concepts and use real-world examples to demonstrate their applications. The book also includes code examples in R. It is a valuable resource for data scientists, analysts, and researchers who want to improve their statistical analysis skills and apply them to practical problems.

That’s all the resources you would need to learn Math’s & Statistics for Data Science. Now let’ move on to the next most important skill for a Data Scientist.

3. Learn Excel for Data Science

Excel is an essential tool for data scientists because it provides a variety of functions for data manipulation, visualisation, collaboration, data cleaning, and predictive modelling. Data scientists can use Excel to efficiently organise, analyse, and visualise data using charts and graphs. Collaboration with colleagues is also simplified because many people are already familiar with the software.

Excel also includes tools for cleaning and formatting data, which is essential when analysing data. Finally, it includes predictive modelling functions that allow users to forecast future trends and scenarios. As a result, learning Excel is critical for data scientists who want to improve their skills, work more efficiently, and produce accurate analyses.

Important Topics

1. Basic Excel functions (SUM, AVERAGE, MAX, MIN, COUNT, etc.)
2. Data cleaning
3. Data analysis (PivotTables, PivotCharts, and other Excel tools)
4. Data visualization
5. Advanced Excel functions (VLOOKUP, INDEX-MATCH, IF-THEN, etc.)
6. Macros and automation
7. Statistical analysis
8. Regression analysis
9. Forecasting
10. Solver
11. Excel add-ins
12. Power Query
13. Power Pivot
14. Data mining

Let’s see some of the resources you can use to learn excel efficiently.

YouTube Videos & Websites

Microsoft Excel Tutorial for Beginners – Full Course | freeCodeCamp | YouTube

The freeCodeCamp Microsoft Excel Tutorial for Beginners is an in-depth course that covers everything from basic to advanced Excel functions. The course, which is delivered in video format on YouTube, provides clear explanations and demonstrations of each concept, beginning with an overview of the Excel interface and basic functions such as formatting, formulas, and charts. After that, the tutorial moves on to more advanced topics like data validation, macros, and working with large datasets.

The course emphasises best practises and shortcuts for working more efficiently with Excel, as well as practise exercises to reinforce learning. Ultimately, the tutorial is an excellent resource for anyone interested in learning how to use Excel, from novices to more experienced users looking to improve their skills.

Microsoft Excel Tutorial | Intellipaat | YouTube

The Excel Tutorial by Intellipaat is a comprehensive training course on YouTube that covers all the essential concepts of Microsoft Excel. The tutorial is presented in a video format and includes an overview of the Excel interface, basic functions such as formatting and cell referencing, and more advanced functions such as data analysis and macros.

The course also covers advanced functions such as VLOOKUP and HLOOKUP, creating and using templates, and collaborating with others using Excel. The instructor emphasizes best practices and time-saving shortcuts to make working with Excel more efficient.

The course includes practice exercises and quizzes to reinforce learning, making it an excellent resource for anyone looking to master Microsoft Excel, from beginners to experienced users. The clear explanations, comprehensive coverage, and practical examples make this tutorial a valuable learning tool for anyone looking to improve their Excel skills.

Microsoft’s Excel Training Center

Microsoft’s Excel Training Center includes free tutorials, videos, and guides for Windows, Mac OS, Android, iOS, and Windows Phone. The resources cover both the latest version of Excel and older versions, and are divided into three levels of Excel ability: beginner, intermediate, and advanced.

Beginner resources cover basic math and creating charts, intermediate resources cover sorting and filtering data, conditional formatting, and VLOOKUPs, and advanced resources cover pivot tables, advanced IF functions, and password-protecting worksheets and workbooks. This makes it an excellent resource for anyone looking to learn or improve their Excel skills, regardless of their level of experience or which platform they use.

The Spreadsheet Page

John Walkenbach’s website is a valuable resource for those who want to enhance their Excel skills. As an expert in the field, he has authored more than 60 Excel books and hundreds of articles and reviews for various publications. The Excel Tips tab on his website contains a plethora of useful tips on formatting, formulas, charts, graphics, and printing. The tips are geared towards both basic and advanced users and cover a wide range of topics, such as working with fractions, pivot tables, and spreadsheet protection.

Additionally, Walkenbach’s Downloads tab provides free Excel workbooks and add-ins that demonstrate helpful techniques that can be applied to users’ work. Lastly, the website includes links to all of Walkenbach’s Excel books, which cover each version of Excel from 2016 to the earliest iterations.

In this section we have seen all the resources necessary to learn Excel for Data Science. Let’s move on to the next one.

4. Learn SQL for Data Science

SQL is a programming language that is essential for data management and analysis in the field of data science. It enables data scientists to extract valuable insights and trends from large data sets stored in databases, allowing them to make data-driven decisions with significant implications for businesses and organisations. SQL is used for a variety of data manipulation tasks, including filtering, sorting, aggregating, and joining data tables. It is also integrated with popular data science tools such as Python, R, and SAS, allowing for more efficient and effective data workflows. SQL knowledge is a highly valued skill in the data science job market, and it is frequently required for entry-level positions.

As a result, learning SQL is critical for anyone interested in a career in data science. It allows data scientists to work with relational databases, which are an important part of many data analysis projects. So, a solid understanding of SQL is a must-have skill for anyone pursuing a career in data science.

Important Topics

1. SQL basics
2. Database design
3. Data manipulation
4. Data querying
5. Aggregate functions
6. Subqueries
7. Views
8. Stored procedures and functions
9. Indexing
10. Transactions
11. Data modeling
12. Normalization
13. Database security
14. NoSQL

Now let’s take a look at some of the best resources that you can use to learn SQL

YouTube Videos

Database Design Course – Learn how to design and plan a database for beginners | YouTube

The “Database Design Course” on YouTube is a beginner-level course that teaches the fundamental concepts and skills required for designing and planning a database. The course is presented in a step-by-step approach and covers topics such as entity relationship diagrams, data normalization, primary and foreign keys, and database constraints.

The instructor uses a practical example of designing a database for a school to illustrate the concepts and guide learners through the process. The course is easy to follow, and the instructor explains each concept in detail and provides examples to help learners understand the topics. The course is ideal for beginners who want to learn how to design and plan a database and for those who want to improve their database design skills.

SQL Tutorial – Full Database Course for Beginners | freeCodeCamp | YouTube

This course from freeCodeCamp on YouTube is a comprehensive SQL tutorial that covers database design, querying, and management for beginners. The tutorial consists of multiple videos, each covering a different aspect of SQL. The first few videos cover the basics of databases, including relational databases and their structure. The following videos then introduce SQL and its syntax, demonstrating how to use SQL to create tables, insert data, and manipulate data.

Later videos cover more advanced topics, such as joining tables, subqueries, and using SQL with Python. The tutorial also provides exercises for viewers to practice their skills. Overall, the tutorial is well-organized and provides a step-by-step approach to learning SQL, making it accessible to beginners who have no prior experience with databases or SQL.

Complete MongoDB Tutorial – The Net Ninja

This series teaches you MongoDB(NoSQL Database) from scratch and how to integrate it into a Node.js API. You’ll learn the basics of MongoDB, data modeling, and indexing for performance optimization. You’ll also learn how to perform CRUD operations and use the powerful aggregation framework. Additionally, you’ll use Mongoose ORM and Express.js to build a simple API, handle HTTP requests and responses, and validate data. Finally, you’ll use Postman to test your API endpoints and debug errors. By the end, you’ll have a solid understanding of MongoDB and practical experience building a simple API.

Let’s move on to the next skill

5. Learn Data Analysis

Learning data analysis is essential for anyone interested in data science. It involves using statistical and computational methods to uncover patterns, relationships, and trends in data. Data analysis skills are transferable across industries and sectors, making them a valuable asset for pursuing a career in data science. It improves critical thinking skills and decision-making abilities and provides individuals with the skills and knowledge needed to make informed decisions and pursue a successful career in data science. In today’s data-driven world, data analysis is a critical step in the data-driven decision-making process.

Important Topics

1. Statistical and computational methods
2. Data visualization techniques
3. Data cleaning and transformation
4. Exploratory data analysis
5. Hypothesis testing
6. Regression analysis and time series analysis
7. Statistical modeling and optimization

Let’s see some of the resources we can use to learn these.

Courses

The best and the only resource you need to learn the basics of Data Analysis is the Google Data Analytics Professional Certificate.

Google Data Analytics Professional Certificate

The Google Data Analytics Professional Certificate program on Coursera offers a comprehensive set of skills to its learners, including spreadsheet analysis, data cleansing, data analysis, data visualization, SQL, questioning, decision-making, problem-solving, metadata, data collection, data ethics, and sample size determination. These skills are essential for data analysts, business analysts, and other related job roles. By acquiring these skills, learners will be able to work with data, extract valuable insights, and make data-driven decisions to solve complex business problems.

The course is designed to build skills for the industry’s most common job titles, and 75% of Google certificate grads report career improvement after taking the course. The program also offers connections with over 150 U.S. employers post-completion.

*See how you can get this course for FREE

YouTube Videos

Data Analytics with Python – IIT Roorkee July 2018

The “Data Analytics with Python” course offered by IIT Roorkee in July 2018 was taught by Ramesh Anbanandam, an Assistant Professor in the Department of Computer Science and Engineering at IIT Roorkee.

The course covered a wide range of topics related to data analysis using Python, including data manipulation, data visualization, and statistical analysis. The lectures were recorded and are available on YouTube as a video series.

Let’s move on to the next skill

6. Setup a GitHub & LinkedIn Profile

Now that you have gained enough skills to showcase to recruiters as a junior data analyst, setting up both a GitHub and LinkedIn account is crucial.

Having a GitHub and a LinkedIn account can greatly increase your chances of landing a job in the technology industry. GitHub allows you to showcase your coding projects to potential employers, demonstrating your technical skills and experience. LinkedIn is a professional networking site that allows you to connect with recruiters, human resource professionals, and potential employers. Combining the two allows you to create a strong profile that highlights your technical skills and professional experience while also expanding your professional network.

Employers frequently search for potential candidates on LinkedIn, and having an active GitHub account with a strong portfolio of work can increase your chances of getting hired. When combined, these platforms can be extremely useful for job seekers in the technology industry. They each have their own set of advantages, but when combined, they can help you stand out and land your dream job.

Let’s see some YouTube Videos that can help you setup a GitHub profile.

YouTube Videos

Complete Git and GitHub Tutorial Kunal Kushwaha

In this Git and GitHub Tutorial by Kunal Kushwaha, he explains the basics of the Git version control system and the GitHub web-based hosting service. The video shows how to create a new repository, add files, commit changes, and work with a remote repository on GitHub, as well as how to push changes and merge changes from multiple branches.

Kunal also provides practical examples and tips for effectively using Git and GitHub, such as best practises for commit messages, branch management, and conflict resolution. The tutorial is intended for complete beginners and covers all of the fundamental concepts and features of Git and GitHub.

How to Make a Great Linkedin Profile – TIPS + EXAMPLES – Expert Academy

This “How to Make a Great LinkedIn Profile – TIPS + EXAMPLES – Expert Academy” video provides tips and examples to help create a professional LinkedIn profile. The video covers various sections of a LinkedIn profile, emphasizing the importance of having a strong headline and summary.

It provides tips on how to optimize experience and education sections and suggests adding multimedia elements to showcase skills. The video also covers best practices for networking, including connecting with relevant professionals and engaging with industry groups and communities. Overall, the video provides practical tips to create a professional LinkedIn profile that stands out to attract potential employers or clients.

After creating these accounts you should start posting about your projects, blogs, courses you have completed or anything you have done related to Data Science & Programming on LinkedIn. Also make sure to upload the code related to the projects on GitHub.

Now let’s move on to one of the most important skills for a Data Scientist.

7. Learn Machine Learning

Machine learning (ML) is a subset of AI that focuses on developing algorithms and models that can learn from data without being explicitly programmed. This field enables computers to improve their performance on a specific task automatically by continuously learning from data, without the need for human intervention.

Machine learning is critical in data science for extracting insights and making predictions from large and complex datasets. Data collected from various sources, such as social media, sensors, and other digital devices, can be analysed using machine learning algorithms to uncover patterns and trends that would be difficult to detect manually.

Important Topics

1.Supervised Learning

• Regression
• Classification
• Decision Trees
• Random Forests
• Support Vector Machines
• Naive Bayes

2. Unsupervised Learning

• Clustering
• Dimensionality reduction
• Principal Component Analysis
• K-means clustering
• Hierarchical clustering
• Anomaly detection

3. Reinforcement Learning

• Markov Decision Processes
• Q-learning
• Deep Reinforcement Learning
• Exploration vs Exploitation
• Value iteration

4.Model Selection and Evaluation

• Overfitting and underfitting
• Cross-validation
• Evaluation metrics
• Model selection techniques
• Bias-variance tradeoff

Let’s see some of the best resources that can help you learn Machine Learning efficiently.

Courses

Machine Learning Specialization – Coursera

The Machine Learning Specialization is an online program created by DeepLearning.AI and Stanford Online. It is taught by Andrew Ng and provides a broad introduction to modern machine learning, including supervised and unsupervised learning, best practices used in Silicon Valley for AI and machine learning innovation, and an applied learning project.

The course teaches the fundamentals of machine learning and how to build real-world AI applications, including building and training machine learning models in Python using popular libraries, such as NumPy and scikit-learn, building and training supervised machine learning models, building and training a neural network with TensorFlow, using decision trees and tree ensemble methods, unsupervised learning techniques, building recommender systems, and building a deep reinforcement learning model.

By the end of the course, students will have mastered key concepts and gained practical experience to apply machine learning to real-world problems, making it a great starting point for anyone looking to break into AI or build a career in machine learning.

*See how you can get this course for FREE

Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023] – Udemy

This course is designed to teach complex theory, algorithms, and coding libraries in a simple way. It covers various topics in machine learning, including data preprocessing, regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, deep learning, dimensionality reduction, and model selection.

The course is suitable for beginners with at least high school knowledge in math, as well as intermediate level people who want to explore different fields of machine learning. It includes both Python and R code templates and practical exercises based on real-life case studies.

The course is intended for anyone interested in machine learning, data analysts who want to level up in machine learning, and people who want to create added value to their business by using powerful machine learning tools.

YouTube Channels

sentdex

CampusX

codebasics

Krish Naik

Books

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

This book, “Hands-On Machine Learning with Scikit-Learn and TensorFlow,” introduces readers to the concepts and tools for building intelligent systems using machine learning. The author, Aurélien Géron, uses concrete examples and minimal theory to help readers gain an intuitive understanding of the subject matter. The book covers a range of techniques, from simple linear regression to deep neural networks, and includes exercises to help readers apply what they’ve learned.

The book also covers the machine learning landscape, explores training models, and provides practical code examples for building and scaling deep neural nets using TensorFlow. Overall, this book is a practical guide for programmers who want to implement machine learning programs capable of learning from data.

*Note: Before reading the Deep Learning part of this book I would suggest you to look at the Deep Learning section of the Roadmap.

Now let’s move on to the next important skill for a Data Scientist.

8. Learn Deep Learning

Deep learning is a subfield of machine learning that processes large and complex datasets using algorithms inspired by the structure and function of the human brain, known as artificial neural networks. It entails feeding massive amounts of data into these networks in order to identify patterns and relationships that will allow them to make accurate predictions or decisions on new data.

Because of its ability to handle unstructured data such as images, speech, and text, deep learning is becoming increasingly important in the field of data science. It has been successfully applied in a variety of industries, including finance, healthcare, and transportation, to solve complex problems like fraud detection, disease diagnosis, and autonomous driving.

Furthermore, the availability of large amounts of data and advances in computing power have enabled deep learning models to be trained more efficiently and accurately than ever before. As a result, deep learning is now an essential component of many data science projects, assisting businesses in making better decisions and gaining a competitive advantage in their respective industries.

Now let’s see some of the most important topics in Deep Learning you should focus on.

Important Topics

1. Artificial Neural Networks (ANNs)
2. Convolutional Neural Networks (CNNs)
3. Recurrent Neural Networks (RNNs)
4. Long Short-Term Memory (LSTM)
5. Autoencoders
6. Generative Adversarial Networks (GANs)
7. Reinforcement Learning
8. Transfer Learning
9. Hyperparameter Tuning
10. Backpropagation Algorithm
11. Optimization Techniques (SGD, Adam, Adagrad, etc.)
12. Regularization Techniques (L1, L2, Dropout, etc.)
13. Object Detection
14. Image Segmentation
15. Speech Recognition
16. Uncertainty Quantification.

Let’s see some of the best resources that can help you learn Machine Learning efficiently.

Courses

Deep Learning Specialization – Coursera

The Deep Learning Specialization is an online program that teaches students about deep learning’s capabilities, challenges, and applications. Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and Dropout, BatchNorm, and Xavier/He initialization are among the topics covered in the program. Students will learn the theory and practice of Python and TensorFlow while working on real-world projects such as speech recognition, music synthesis, and machine translation.

Career advice from industry experts is also included in the program. Students will be able to build and train deep neural networks, reduce errors in machine learning systems, work with visual and language data, and apply end-to-end, transfer, and multi-task learning by the end of the program.

*See how you can get this course for FREE

A deep understanding of deep learning (with Python intro)

This is a comprehensive course on deep learning that covers theory, math, implementation in Python (using PyTorch) . It includes clear explanations, visualizations, exercises, and an active Q&A forum. The course is suitable for students in a deep learning course, machine learning enthusiasts, data scientists, aspiring data scientists, and scientists/researchers interested in deep learning. The course also includes an 8+ hour Python tutorial, making it accessible to those new to Python. Overall, the course aims to provide a deep understanding of deep learning with lasting expertise.

TensorFlow Developer Certificate in 2023: Zero to Mastery

The course is aimed at beginners and covers topics such as neural network regression and classification, computer vision, transfer learning, NLP, time series forecasting, and more. The course includes hands-on projects and is taught by a TensorFlow certified expert. Passing the exam at the end of the course can lead to becoming part of Google’s TensorFlow Developer Network and potentially earning a high salary as a TensorFlow developer.

YouTube Videos & Channels

Applied Machine Learning – Andreas Mueller

MIT Introduction to Deep Learning | 6.S191

For YouTube channels to learn Deep Learning, you can go through the YouTube Channels mentioned in the Machine Learning Section

Books

Deep Learning An MIT Press book by Ian Goodfellow and Yoshua Bengio and Aaron Courville

The book “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville offers an introduction to a wide range of topics in deep learning. It covers the mathematical and conceptual background, including relevant concepts in linear algebra, probability theory, and information theory, numerical computation, and machine learning. The book describes deep learning techniques used by practitioners in industry and surveys their applications in natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

It also offers research perspectives on theoretical topics such as linear factor models, autoencoders, Monte Carlo methods, and deep generative models. The book is suitable for undergraduate or graduate students planning careers in industry or research, and for software engineers who want to begin using deep learning in their products or platforms.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”

—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Dive into Deep Learning

Adopted at 400 universities from 60 countries, D2L.ai, also known as Dive into Deep Learning, is an online textbook written by a team of authors led by Professor Aston Zhang from the University of California, Berkeley. The book covers a wide range of topics in deep learning and provides both theoretical and practical knowledge. It includes interactive Jupyter notebooks, allowing readers to experiment with code and visualize results. The book is well-structured and provides clear explanations of complex concepts. Additionally, it is regularly updated to reflect the latest developments in the field. Overall, D2L.ai is an excellent resource for anyone looking to learn about deep learning, from beginners to experienced practitioners.

” In less than a decade, the AI revolution has swept from research labs to broad industries to every corner of our daily life. Dive into Deep Learning is an excellent text on deep learning and deserves attention from anyone who wants to learn why deep learning has ignited the AI revolution: the most powerful technology force of our time. “

– Jensen Huang, Founder and CEO, NVIDIA

Now let’s move on to the next important skill for a Data Scientist.

9. Advanced Machine Learning Topics

Computer vision (CV), natural language processing (NLP), and generative adversarial networks (GANs) are some of the examples for advanced machine learning techniques.

Computer vision involves using machine learning algorithms to interpret and analyze digital images or videos. CV techniques can be used for a variety of applications, including image recognition, object detection, face recognition, and autonomous vehicles.

Natural language processing involves using machine learning algorithms to analyze and understand human language. NLP techniques can be used for a variety of applications, including text classification, sentiment analysis, machine translation, and chatbots.

Generative adversarial networks involve using two neural networks (a generator and a discriminator) to generate new data that is similar to a given dataset. GANs can be used for a variety of applications, including image generation, video generation, and text generation.

All three of these techniques are considered advanced machine learning techniques because they require more sophisticated algorithms and approaches than traditional machine learning techniques. They are also highly applicable to a wide range of real-world problems and are being increasingly used in many industries, such as healthcare, finance, and entertainment.

Let’s see some resources we can use to learn these

Resources to Learn Computer Vision

*Take these resources in the prescribed order to get the best outcome.

Stanford CS231n – Computer Vision

Stanford CS231n is a popular computer vision course offered by Stanford University. The course covers the fundamentals of computer vision, including image classification, object detection, segmentation, and visual question answering. It is taught by three experienced professors: Fei-Fei Li, Justin Johnson, and Serena Yeung, and includes lectures, notes, slides, and programming assignments. The course covers a wide range of topics in computer vision and is designed for students who have some prior knowledge of machine learning and deep learning. The knowledge gained from the course can be invaluable for a career in machine learning, computer vision, or related fields.

Checkout the course website for slides and other resources.

Computer Vision – Kaggle

This 4 hour Kaggle course and the tasks that follows would help you gain insights into the real world problems that can be solved using Computer Vision.

Advanced Computer Vision with TensorFlow

The Advanced Computer Vision with TensorFlow course focuses on exploring image classification, segmentation, object localization, and detection using TensorFlow’s advanced features. The course enables learners to apply transfer learning to object localization and detection, implement image segmentation using fully convolutional networks, and identify which parts of an image are used by the model to make predictions. The course is designed for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who want to expand their knowledge and skillset by learning advanced TensorFlow features to build powerful models.

*See how you can get this course for FREE

After taking these courses go through the contents and do some projects using the YouTube channel below.

Murtaza’s Workshop – Robotics and AI

Resources to Learn NLP

Stanford CS224N: NLP with Deep Learning

Stanford CS224N is a comprehensive course on Natural Language Processing (NLP) with Deep Learning. It covers a wide range of NLP topics, including word vectors, neural network models for NLP, syntax and parsing, machine translation, and sentiment analysis. Students are introduced to various deep learning techniques and models, such as RNNs, CNNs, and sequence-to-sequence models. The course is designed for students with a background in machine learning and programming, and provides lectures, readings, assignments, and projects. By the end of the course, students are equipped with the necessary skills to tackle various NLP challenges using deep learning techniques and popular NLP applications and tools.

Natural Language Processing Specialization

The Natural Language Processing Specialization on Coursera is designed to teach learners how to build cutting-edge NLP systems using machine learning basics and state-of-the-art deep learning techniques. The course is taught by two NLP experts, Younes Bensouda Mourri and Łukasz Kaiser, who cover topics ranging from sentiment analysis and named entity recognition to machine translation and chatbot building. Learners will gain hands-on experience using techniques such as logistic regression, dynamic programming, and dense and recurrent neural networks, and will be introduced to advanced models like T5, BERT, and transformers. As the demand for NLP professionals grows in the AI-powered future, this Specialization prepares learners to design and implement NLP applications with real-world impact.

Krish Naik NLP Playlist

CampusX NLP Playlist

Resources to Learn GANs

Generative Adversarial Networks (GANs) Specialization

The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization is a comprehensive course for software engineers, students, and researchers who want to learn about GANs and their applications in image generation. The course covers fundamental concepts of GANs, building basic and advanced GANs using PyTorch, controlling GANs and building conditional GANs, evaluating GANs using the Fréchet Inception Distance method, detecting and addressing bias in GANs, and implementing the state-of-the-art StyleGAN. The course also covers applications of GANs in data augmentation and privacy preservation and includes hands-on experience building Pix2Pix and CycleGAN for image translation. The course is accessible for learners with varying levels of familiarity with advanced math and machine learning research.

Image Generation using GANs | Deep Learning with PyTorch: Zero to GANs

Now let’s move on to the next important step in your Data Science Journey.

10. Participating in Competitions

Participating in data science competitions offers a range of benefits for individuals in the field. Competitions allow data scientists to hone their problem-solving skills by tackling real-world problems that necessitate creative solutions. Participants can also learn from their peers, stay up to date on the latest techniques, build a portfolio, and gain recognition in the field. Winning or placing highly in a competition can raise the profile of a data scientist’s work and lead to new job opportunities. Participating in competitions can help anyone improve their skills and advance their career, whether they are a beginner or an experienced data scientist. Overall, competitions provide a valuable platform for data scientists to demonstrate their skills and learn from their peers.

Now let’s see some of the online platforms where you can participate in Data Science Competitions.

Websites

• Machine Hack
• Kaggle
• DataHack
• Codelab
• TopCoder
• Driven Data
• Bitgrit
• Alcrowd
• Tianchi Big Data Science Competition
• IronViz

Learn more about these competitions in this article

Now let’s move on to the next important step in your Data Science Journey.

11. Resume & Portfolio Building

Now that you have Gained more than enough Knowledge about Data Science and have participated in some competitions, its time to showcase them all to the world. You can do this through your GitHub and LinkedIn pages that we created in the earlier section. But two other important places where you should showcase your skills are portfolio & resume. The Reason is that 90% of the time, these might be the first pieces of evidence for your skillset that a recruiter will be seeing. So making these as good as possible is as important as learning all these skills.

A resume is a brief document that summarises a candidate’s relevant educational background, work experience, and skills. It is usually one or two pages long, with bullet points highlighting specific accomplishments and responsibilities. A resume is intended to provide a concise overview of a candidate’s qualifications and experience, and it is frequently the first point of contact between a candidate and a potential employer.

A portfolio, on the other hand, is a collection of work samples that demonstrate a candidate’s skills and experience in greater detail. Examples of data analysis projects, visualisations, reports, or other relevant work may be included. A portfolio may also include written descriptions of each project and the role the candidate played in its completion. A portfolio is intended to provide a more comprehensive view of a candidate’s abilities and can be used to demonstrate how their skills have been applied to solve real-world problems.

While a resume and portfolio serve different purposes, they are both essential components of a successful data science job search. A well-crafted resume that is ATS Friendly can help a candidate stand out from the crowd by emphasising relevant qualifications, whereas a portfolio can provide a more detailed view of their abilities as well as demonstrate their passion and commitment to the field.

Let’s see some Resources that can help you out in Creating a Portfolio and Writing an ATS Friendly Resume.

Resources

How To Build A Kickass Data Science Portfolio | Portfolio For Data Science – CampusX | YouTube

How to Make A Data Science Portfolio Website with Github Pages – Ken Jee | YouTube

This resume got me offers from Google, Microsoft, and Amazon! – Pirate King | YouTube

Best Resume Format | Tips for writing an AWESOME Resume | ATS Resume Format – techTFQ | YouTube

Now let’s move on to the next important step in your Data Science Journey.

12. Interview Preparation

Preparing for a data science interview can be a daunting task, but with the right approach and resources, it can be made less difficult. Here are some pointers on how to prepare for a data science interview, as well as some useful resources:

Research the company: Research the company and the specific job requirements to determine what the interviewer is looking for. This will allow you to tailor your responses to the role and the organisation.

Review the job description: Carefully read the job description to understand the required skills and experience. This will assist you in preparing for technical questions while also emphasizing your relevant experience and accomplishments.

Practice answering common questions: Practice answering common interview questions, including technical questions about data science. This will make you feel more confident and in command of the situation during the interview.

Brush up on technical skills: Refresh your technical knowledge and become acquainted with relevant software and tools. This will help you perform better during the interview’s technical portion.

Prepare your questions in advance: Prepare questions about the company and the role to ask the interviewer. This demonstrates your interest in the position and allows you to better understand the company’s culture and values.

Let’s see some resources that can help you brush up your technical skills

Websites

Machine Hack

To prepare effectively for a data science interview, it is essential to evaluate your current standing by testing yourself. Machine Hack provides a mock interview platform that comprises various short and long mock data science interviews. This platform caters to specific companies, including AWS, Microsoft, and Google, and different data science positions, such as associate data scientist, data scientist, and deep learning engineer. By utilizing Machine Hack’s mock interview platform, you can evaluate your skills and knowledge, understand your strengths and weaknesses, and identify areas that need improvement to perform better during actual interviews.

Glassdoor 

Glassdoor is an excellent resource for preparing for data science interviews, with over 4,000 interview questions asked at specific companies. It covers various data science roles, such as front end engineer, iOS developer, lead data scientist, and software engineer. What sets Glassdoor apart is that these are real questions that people have been asked during data science interviews and cover a broad range of topics related to the subject. By utilizing Glassdoor’s extensive database of interview questions, you can gain insight into the types of questions that may be asked during your data science interview and be better prepared to respond effectively.

LeetCode 

LeetCode is a platform that helps individuals improve their skills and land their dream job by providing resources to enhance data science skills and prepare for interviews. It was originally developed for computer scientists and software developers to prepare for their interviews and includes a database section to practice SQL. The platform offers practice questions for coding skills and consists of a vast question library of over 20,000 algorithm and data structure problems. In addition, LeetCode’s premium section provides study packs, interview simulations, and select questions by companies to further prepare users for data science interviews.

StrataScratch 

StrataScratch is a platform that aims to help data scientists prepare for interview questions, with a focus on coding questions from over 500 big companies such as Amazon, Google, and Microsoft. Users can select coding questions based on SQL or Python, or opt for coding and non-coding questions. The platform also covers various other topics, including probability, business cases, product sense, modelling questions, statistics, miscellaneous technical questions, and system design. Its question library consists of over 1,000 questions, with some problems featuring video explanations. StrataScratch provides a comprehensive resource for data scientists to prepare for interviews and improve their skills.

AlgoExpert

AlgoBay is a coding interview resource created by Clement Mihailescu, an Ex-Google and Ex-Facebook Software Engineer. It is said to be the ultimate resource for coding interviews, providing organised structure, detailed solutions, video explanations, and quick crash courses on data structures. Although the question library is smaller than some platforms, with only 160 questions, it covers important topics across fifteen categories, such as string, binary trees, dynamic programming, sorting, algorithms, and more. The platform has received positive feedback from engineers working in big tech companies such as Google, Facebook, and Microsoft, making it a valuable resource for data science job seekers.

YouTube Videos

Data Science interview questions – Krish Naik

100 Most Common Interview Questions on Machine Learning | With Solutions – CampusX

Data Science Interview Preparation | Types of Questions Asked – CampusX

Now that we have come to the end of this Data Science Roadmap let’s see some of the useful resources that would ease your Data Science Journey.

13. Helper Resources

Blogs

• Data Science Central
• insideBIGDATA 
• BAIR blog
• Datafloq
• KDnuggets
• Towards Data Science
• Machine Learning Mastery

Datasets

• Amazon Datasets
• Kaggle Datasets
• Google Dataset Search
• Data.Gov
• Datahub.io
• UCI Machine Learning Repository
• Earth Data
• CERN Open Data Portal
• Global Health Observatory Data Repository
• BFI film industry statistics
• NYC Taxi Trip Data
• FBI Crime Data Explorer
• indiaai.gov.in/datasets
• data.gov.in

Reserach

• Google Scholar
• ResearchGate
• Papers With Code – This is an excellent resource that offers free access to research papers related to topics such as data science, machine learning, and big data.

GitHub Repos

• FREECODECAMP
• TENSORFLOW
• THE ALGORITHMS
• AWESOME MACHINE LEARNING
• DATA SCIENCE I-PYTHON NOTEBOOKS
• HOMEMADE MACHINE LEARNING
• AWESOME DATA SCIENCE
• DEEP LEARNING DRIZZLE 
• 500 AI-ML PROJECTS 
• INTERACTIVE TOOLS

Reddit Subreddits

• r/datascience
• r/MachineLearning
• r/analytics
• r/BusinessIntelligence
• r/DataScienceJobs
• r/datasets
• r/visualization
• r/dataisbeautiful
• r/learnpython
• r/rstats

Instagram pages

• datasciencebrain
• amankharwal.official
• sundaskhalidd
• storytellingbydata
• dataanalystduo
• mlwithrish

Twitter accounts

• Hilary Mason
• Lillian Pierson
• Kira Radinsky
• Richard Socher
• Christopher Surdak
• Sebastian Thrun
• John Myles White
• Dean Abbott
• Kenneth Cukier
• Nando de Freitas

discord channels

• Open Source Hub
• Data Nation
• Data Science
• The Data Share
• Data Analytics
• TensorFlow
• Data Science/ML/AI
• Data Science World
• Dose of Data

podcasts

• Gradient Dissent Podcast
• Late Nights
• Women in Data Science
• 99% Invisible
• Data Engineering Podcast
• TWIML (This Week In Machine Learning) AI Podcast
• Software Engineering Daily
• MLOps.community
• Lex Fridman Podcast

Conclusion

In conclusion, the Ultimate Data Science Roadmap is a comprehensive guide for anyone interested in pursuing a career in data science. This roadmap covers everything you need to know to become a successful data scientist, from programming basics to advanced machine learning topics. It also offers courses, books, YouTube channels, websites, and social media platforms like GitHub, LinkedIn, Reddit, Instagram, Twitter, and Discord to help you learn and connect with the community. This guide also covers competitions, building your resume and portfolio, and preparing for interviews. You can acquire the skills and knowledge required to excel in the field of data science by following this roadmap and utilising the resources provided.

For those who follow this Ultimate Data Science Roadmap, the journey may seem daunting at times, but remember that every step you take will bring you one step closer to your goal. With each new topic you learn, your knowledge and skill set will grow, making you a more valuable asset in the data science field. You will be able to showcase your abilities and stand out from the competition by developing your portfolio and participating in competitions. With each interview you prepare for, you’ll gain confidence in your abilities and become better prepared to land the job of your dreams.

Remember, data science is a constantly evolving field, and there is always more to learn. Accept the challenges, stay motivated, and continue to push yourself to learn and grow. You can achieve your goals and become a successful data scientist with determination and hard work.

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