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ChatGPT
LLM
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Machine Learning
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Python
NLP
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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Learning Path to Become a Data Scientist in 2024

Yana Khare
Yana Khare Last Updated : 15 Oct, 2024
8 min read

Introduction

In the wake of a year teeming with breakthroughs and monumental technological advancements, 2023 is a testament to the incredible pace of data science and analytics innovation. As we enter 2024, this guide becomes not just a roadmap but a gateway to harnessing the momentum of progress witnessed in the past year. Join us on a transformative journey as we unveil a step-by-step blueprint to become a data scientist in 2024 by leveraging the cutting-edge insights and methodologies that define this thrilling era of technological evolution.

Skills Required to Become a Data Scientist in 2024

Table of contents

Overview of Data Scientist Learning Path 2024

At the core of this roadmap lies a simple equation: To become a Data Scientist, you need the right set of tools, a diverse range of techniques, and the skill to design impactful solutions. These skills complement each other multiplicatively, unlocking new possibilities. For instance, mastering Python, a tool, empowers you to delve into techniques like Exploratory Data Analysis (EDA).

In this comprehensive learning roadmap for aspiring Data Scientists in 2024, we offer a step-by-step framework, detailing the essential tools and techniques to master, coupled with the cultivation of the highly sought-after design skill.

Noteworthy in this year’s roadmap is its condensed format — a focused 9-month journey compared to the previous 12-month roadmap. This adjustment acknowledges the urgency to cover new ground swiftly in response to the accelerated pace of AI advancements. Assuming a commitment of 15-20 hours per week, this roadmap is designed to propel your learning journey effectively.

On this note, let the exploration begin!

Quarter 1: Beginning with the Basics

In the First three months, your goal would be to learn the foundational data analytics skills, like programming, basic statistics, EDA, software engineering skills, and finally, the basics of Cloud Computing.

With the knowledge of these skills, you can start applying for Data Analyst roles right after the 1st quarter. Now, here’s what you need to learn.

Start with Data Science Tools

Programming Skills

Start with Python, a versatile and easy-to-learn language with a wide range of applications. Then, delve into a domain-specific language like SQL for tasks such as querying databases and managing relational data.

Software Engineering Skills

Next, focus on tools like Git and Github. These web-based platforms help you manage your code efficiently. Additionally, familiarize yourself with basic Linux commands, which will be handy for navigating and processing data.

Cloud Computing Concepts

Explore the fundamentals of cloud computing with platforms like AWS, GCP, or Azure. Choose one that aligns with your goals and learn the basics, including setting up a machine, running Jupyter Notebooks, optimizing storage, and ensuring platform security.

Learn the Essential Data Science Techniques

After mastering the essential tools, shift your attention to key techniques crucial for data analytics:

Statistics for Machine Learning

Delve into fundamental statistical concepts indispensable for machine learning, encompassing descriptive statistics, probability, hypothesis testing, and regression analysis.

EDA

Now, let’s dive into Exploratory Data Analysis (EDA). EDA involves visually and statistically summarizing, interpreting, and understanding a dataset’s main characteristics. Techniques include Univariate/Bivariate Analysis. Learn to perform EDA using Python and relevant libraries such as pandas, matplotlib, seaborn, etc. Plus, with AI tools like ChatGPT and its Code Interpreter, EDA becomes simpler. Just provide your dataset and start asking questions, like checking for missing values or deciding how to impute them with mean or median.

Design

Now, let’s talk about design. Strengthen your problem formulation skills by questioning and defining how a problem can be answered. Throughout this first quarter, concentrate on enhancing analytical skills through practice in logical reasoning, data interpretation, and basic mathematics problems. Additionally, focus on improving your presentation (PPT) skills during this period.

Things to do After Quarter 1

After completing the first quarter, you’ll have a strong foundation in Machine Learning. Now, it’s time to kickstart your career by applying for Data Analyst roles. Create essential professional profiles like your Resume, Cover Letter, and LinkedIn account, effortlessly with ChatGPT in minutes. Additionally, stay informed about the latest developments in the Gen AI ecosystem as a valuable exercise during this quarter.

Quarter 2: Master Machine Learning

Moving into the second quarter, emphasize Essential Mathematics for Machine Learning and delve into advanced ML topics such as Deep Learning, covering NLP and Computer Vision. Progress to End-to-end Projects, including Model Deployment. By the quarter’s end, actively participate in Data Science competitions on Kaggle and Datahack Platform and initiate applications for entry-level Data Scientist positions. Now, let’s look into the specific areas you should focus on:

Essential Machine Learning Techniques

In our last quarter, we dived into essential tools. Now, let’s shift gears and focus on techniques. First up is mastering the Essential Mathematics required for ML. Get comfortable with concepts like linear algebra and gradient descent. Next, explore a range of supervised and unsupervised ML algorithms. Cap it off by learning various model evaluation metrics such as accuracy, precision, and recall. Let’s build that strong foundation together!

Machine Learning Projects

Practice solving real-world problems using machine learning methodologies by engaging in various projects. These hands-on endeavors provide invaluable practical experience and consolidate theoretical knowledge.

Mastering Advanced Machine Learning Concepts

With a few projects under your belt, it’s time to advance into more sophisticated ML techniques. Start with ensemble learning, then move to the fundamentals of deep learning, covering basics like neural networks, popular frameworks, and transfer learning.

As you progress, dive deeper into deep learning with a focus on natural language processing (NLP) and computer vision (CV). Computer vision is vital for data science enthusiasts, allowing analysis and insights from visual data for applications like image recognition.

Similarly, NLP is crucial for working with unstructured text data, enabling applications such as sentiment analysis.

MLOps

In MLOps, understanding tools is key, and applying them involves techniques like model deployment. In the first quarter, you’ve already explored cloud platforms, setting the stage for MLOps. This phase involves scaling ML models for production. Dive into tools by familiarizing yourself with containerization and app-building frameworks like Streamlit and Gradio. Building on your knowledge of MLOps platforms (Azure, AWS, GCP), shift focus to essential techniques for managing the complete Machine Learning Project Lifecycle: build, train, deploy, and maintain. L

End-to-End Projects

Finally, you have learnt all the skills you need. Next you must do end to end projects, along with – documenting them on GitHub. This way, you are solving real world problems, just like a Data Scientist. These are some of the projects you may do at this point.

Design

In Quarter 2, let’s shine a spotlight on Communication Skills. Elevate your communication by honing your storytelling abilities through blog writing or creating YouTube videos. Cultivate structured thinking with exercises like guesstimation, reading case studies, and practicing mind mapping.

Expand your skill set by delving into model implementation, which encompasses techniques like A/B testing—a methodology for comparing model versions to determine performance. Round off by mastering the art of monitoring these models

Things to do After Quarter 2

By the end of Quarter 2, you’ll possess a robust understanding of building and deploying basic and advanced ML models. Equipped with this proficiency, you can now pursue entry-level data scientist roles. Moreover, adeptness in feature engineering and training high-performance ML models further strengthens your candidacy in the field.

Quarter 3: Learning Design Skills and AI

As you enter the conclusive phase of this learning journey of being a Data Scientist in 2024, the ultimate goal is clear: securing a full-time position in Data Science. Tailored guidance awaits, whether you’re a fresh graduate exploring traditional Data Scientist roles or a seasoned professional seeking to apply newfound knowledge within your current workspace.

Domain Expertise and Professional Application

For working professionals transitioning to Data Science, building domain expertise in your current field is crucial. If, for instance, you’re a data analyst in insurance, leverage your newfound tools and techniques to develop algorithms addressing industry-specific challenges. This practical application is a key technique to implement your learning.

For freshers embarking on this journey, exploration is key. Dive into a variety of data science problems on platforms like Kaggle or DataHack. This hands-on approach helps you gain diverse experience and develop problem-solving skills from the ground up.

Generative AI

Elevate your learning by delving into advanced AI topics, focusing on Generative Models—an industry-defining competence. Consider two tracks: specialize in Natural Language Processing (NLP) or immerse yourself in Computer Vision.

Generative Models for NLP Track

Master the nuances of Generative Models for NLP with a structured approach:

  • Getting started with LLMs: Begin by understanding the fundamentals of Large Language Models (LLMs), exploring the different types available. Dive into Foundation Models to establish a strong base.
  • Prompt Engineering: Master Prompt Engineering, delving into its various techniques and tricks. This skill is essential for shaping the input prompts for optimal model performance.
  • RAG (Retrieval-augmented generation): Learn to build RAG applications using tools like Llama Index or LangChain. Understand the integration of retrieval-augmented generation for enhanced NLP outcomes.
  • Fine-tuning LLMs: Reach the next level by fine-tuning LLMs on domain-specific datasets. Utilize Parameter-Efficient Fine-Tuning (PEFT) to tailor models to specific niches. This advanced skill ensures optimal performance in specialized contexts.
Generative Models for Computer Vision Track

If you choose the generative models for Computer Vision track, here’s what you need to learn:

  • Getting started with Stable Diffusion Models: Begin with the fundamentals, grasping the essence of Diffusion Models and exploring their diverse types. Dive deeper into Stable Diffusion Models to establish a solid foundation.
  • Prompt Engineering: Master the art of Prompt Engineering, uncovering various tricks for optimal results in text-to-image models. Explore techniques like Midjourney and Dall-E 3 to enhance your prompting skills.
  • Fine-tuning of Stable Diffusion Models: Progress to fine-tuning Stable Diffusion Models on domain-specific datasets. Utilize Parameter-Efficient Fine-Tuning (PEFT) to tailor models for specific contexts, ensuring they align seamlessly with your project goals.
  • Personalizing Stable Diffusion Models: Advance your skills by learning to control stable diffusion models such as dreambooth and InstructPix2Pix. This personalized approach allows you to adapt these models for unique applications, enhancing their utility and versatility.

Design Thinking and Application

In this quarter, immerse yourself in Design Thinking—a non-linear, iterative process crucial for Data Scientists. This approach involves understanding end-users, challenging assumptions, redefining problems, and crafting innovative solutions. Embrace the five key phases: Empathize, Define, Ideate, Prototype, and Test. Mastering Design Thinking is an essential skill set that adds depth to your capabilities as a Data Scientist.

Post-Quarter 3

Post-Quarter 3, whether you’re a fresher or a working professional, you’ll be ready to apply for full-fledged Data Scientist roles. Take the next steps by updating your resume and gearing up for interviews. We’ve curated a collection of videos to guide you on the path to landing your dream job.

Leverage your newfound knowledge in NLP and Computer Vision to explore building your own AI applications. Dive into our quick tutorials where we’ve crafted various AI applications to inspire and guide you. Your journey towards a rewarding career in Data Science is on the horizon!

How can you Speed-up the Process of Becoming a Data Scientist in 2024?

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For those seeking a faster route to becoming a Data Scientist while maintaining their current job, the BlackBelt Plus program is best for you. Enroll now to access a full-stack Data Science curriculum featuring a personalized learning roadmap curated just for you.

You will also get access to 50+ hands-on industry projects, one-on-one mentorship, and dedicated interview preparation with placement support.

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Conclusion

Concluding our comprehensive guide to becoming a Data Scientist in 2024, this journey is not just a roadmap; it’s a gateway to embracing the forefront of technological evolution. In a year marked by remarkable technological strides, the landscape of data science and analytics has surged forward, demanding a spectrum of skills to navigate this dynamic field.

By dedicating yourself to diligently following this guide, you’re not just acquiring skills; you’re building a solid foundation. This empowers you to innovate, create, and contribute significantly to the exciting landscape of data science in 2024 and beyond.

Moreover, Join our Analytics Vidhya community platform for an immersive experience. Tailored Data Science and Generative AI community groups await your interests, providing opportunities to learn alongside your peers. Along with that, enjoy free access to live webinars and AMA sessions from industry experts.

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Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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Responses From Readers

Clear

Prabhakar Reddy
Prabhakar Reddy

Thank you so much, it looks promising path to become Data Scientist. I will look forward and follow this learning path. And make it as 2021 not 2020, a the end of below sentence "you’d be in a great position to start cracking data science interviews by the end of 2020."

123

Harpreet Singh
Harpreet Singh

Should I enroll for "Introduction to Python" before this course? Or is it included in this course.

123

Pulkit Sharma
Pulkit Sharma

Hi Harpreet, Python course is included in this learning path.

123
Inderpreet Kaur
Inderpreet Kaur

Thanks for writing this in depth post. You covered every angle. One word to say, I love it!

123

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Top 10 Data Analytics Projects with Source Codes
Top 10 Data Analytics Projects with Source Codes
How to Become a Data Scientist in 12 Months?
How to Become a Data Scientist in 12 Months?

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