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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

How to Become a Data Scientist in 12 Months?

Yana Khare
Yana Khare Last Updated : 12 Sep, 2024
6 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 how to master Data Scientist in 12 months blueprint to become a data scientist in 2024 by leveraging the cutting-edge insights and methodologies that define this thrilling era of technological evolution.

In this article, you will learn how to become a data scientist by following a simple data science roadmap. We will look at the data scientist roadmap 2024, which shows you the key skills you need to succeed. Whether you want to learn in a classroom or become a data scientist online, this guide will help you with a clear road map to become a data scientist. Get ready to start your journey to becoming a data scientist!

Table of contents

Skills Required How to Become Data Scientist in 2024

Skills Required to Become a Data Scientist in 2024

To become a Data Scientist in 2024, these are the skills you need to master:

Technical Skills:

  1. Programming Proficiency: Mastery in languages like Python, R, SQL, and often other tools like Scala or Java.
  2. Statistical Analysis: Sound knowledge of statistics and probability theory, hypothesis testing, regression, etc.
  3. Data Wrangling: Ability to clean, preprocess, and organize data from diverse sources using tools like Pandas, NumPy, etc.
  4. Data Visualization: Proficiency in tools like Matplotlib, Seaborn, and Tableau for creating meaningful visualizations.
  5. Machine Learning: Understanding and implementing ML algorithms for classification, regression, clustering, etc., using libraries like TensorFlow, scikit-learn, etc.
  6. Big Data Handling: Familiarity with handling large datasets using tools like Hadoop, Spark, etc.

AI Skills:

  1. Understanding of AI Concepts: Knowledge of artificial intelligence concepts like neural networks, deep learning architectures, and reinforcement learning.
  2. Natural Language Processing (NLP): Ability to work with text data, sentiment analysis, and language modeling using libraries like NLTK, SpaCy, etc.
  3. Computer Vision: Understanding image processing, object detection, and classification using frameworks like OpenCV, TensorFlow, or PyTorch.

Soft Skills:

  1. Problem-Solving: Strong analytical and problem-solving skills to approach complex data challenges.
  2. Communication: Ability to convey complex findings in a simple, understandable manner to stakeholders.
  3. Curiosity and Learning Agility: A thirst for continuous learning and staying updated with the latest trends and technologies in the field.
  4. Teamwork: Collaborative mindset to work effectively in interdisciplinary teams.
  5. Business Acumen: Understanding of the business context, translating data insights into actionable strategies.


Feeling a bit overwhelmed? No need to stress! We’ve designed a structured 12-month plan to help you gain these skills. To make it easier, we’ve split the roadmap into four quarters. This plan is based on dedicating a minimum of 4 hours daily, 5 days a week, to your studies.

If you follow this plan diligently, you should be able to:

Quarter 1: Beginning with the Basics

In the initial quarter of this roadmap, the primary aim is to master foundational data analytics skills. This involves learning key aspects such as Programming, Basic Statistics, Exploratory Data Analysis (EDA), and Data Visualization.

Month 1:

We will begin by delving into a programming language like Python. Python’s advantages lie in its wide-ranging applications, easy learning, and extreme versatility. Afterward, acquaint yourself with a domain-specific language such as SQL, which is pivotal for tasks like querying databases and managing stored data in relational databases.

Month 2:

This month will focus on fundamental statistics relevant to Machine Learning. This includes descriptive statistics, probability, hypothesis testing, and regression analysis. Simultaneously, grasp the essence of Exploratory Data Analysis (EDA), a process vital for understanding a dataset’s characteristics through techniques like univariate and bivariate analysis.

Furthermore, we will utilize AI tools like ChatGPT and its Code Interpreter for streamlined EDA. Provide your dataset and ask questions to perform tasks like checking missing values or imputing them using mean or median. Additionally, learned beginner-level Prompt Engineering Techniques to optimize outcomes from Large Language Models.

Month 3:

Following EDA, we will delve into the art of storytelling with data. Focus on visualization tools like Power BI, Tableau, or Qlik Sense. Learn to construct interactive dashboards, for instance, creating visuals on topics like a Covid Vaccination Dashboard or a Cricket World Cup Visualization Dashboard.

Things to do after Quarter 1

By the conclusion of the initial quarter, you’ll have established a robust foundation in Machine Learning. At this juncture, consider applying for Data Scientists roles. Craft your resume and cover letter, and set up a LinkedIn profile. Use tools like ChatGPT to streamline these tasks efficiently.

Quarter 2: Master Machine Learning

During the second quarter of this learning journey how to Master data scientist in 2024, the emphasis shifts to mastering Essential Mathematics for Machine Learning and delving deeper into Advanced ML topics, notably Deep Learning. By the quarter’s end, you’ll be well-prepared to engage in Data Science competitions on platforms like Kaggle and Datahack. Let’s explore the focus areas for each month:

Month 4:

Begin by grasping the foundational mathematics required for Machine Learning. This involves understanding concepts like Linear Algebra and Gradient Descent. Proceed to comprehend Supervised and unsupervised ML Algorithms and various Model Evaluation Metrics such as Accuracy, Precision, and Recall.

Month 5:

Practice solving real-world problems through a series of machine-learning projects. Engage with a variety of projects to gain hands-on experience.

Month 6:

With a repertoire of projects completed, advance into more complex ML techniques, including Ensemble Learning. Explore the fundamentals of Deep Learning encompassing Neural Networks basics, popular Deep Learning Frameworks, and hands-on experience with Pre-trained Models and Transfer Learning. Mastery of these advanced skills is crucial in today’s AI landscape.

Things to do after Quarter 2

By the end of this quarter, you should be adept at conducting feature engineering and training high-performance ML models. With these skills, you’re now prepared to participate in various data science competitions. Additionally, by the conclusion of the second quarter, you’ll have the proficiency to build advanced ML Models, paving the way for the deployment phase in the subsequent quarter.

Quarter 3: Learning to Deploy Machine Learning Models

During the third quarter of this roadmap for how to master data scientist in 2024, the primary focus is on comprehending ML Model Deployments while aiming to initiate applications for entry-level Data Science roles by the quarter’s end. Here’s a breakdown of the quarterly objectives and what to learn in each month:

Month 7:

This month will focus on sharpening your Software Engineering skills during this period, concentrating on platforms like Git and Github. These web-based tools facilitate the storage and management of code online. Additionally, delve into learning Linux commands, which are crucial for efficient data navigation, processing, and management.

Month 8:

You will acquire knowledge in cloud computing and opt for a platform among AWS, GCP, or Azure. This expertise becomes vital in overseeing the entire Machine Learning Project Lifecycle, encompassing phases like building, training, deploying, and maintaining models.

Month 9:

Explore MLOps, which involves scaling ML models for production. Focus on skills such as containerization and utilizing app-building frameworks like Streamlit and Gradio. Additionally, familiarize yourself with AIOps, a broader domain where AI automates workflows.

Post-Quarter 3

By the conclusion of the third quarter, you’ll possess a comprehensive understanding of constructing and deploying ML models. You become eligible to apply for entry-level data scientist positions at this stage. Update your resume and start preparing for interviews. Access resources like ins

Quarter 4: Learning Natural Language Processing and Computer Vision

As we arrive at the final stretch of this learning journey to become a data scientist in 2024, the ultimate goal in this quarter is to secure a full-time Data Science position. The focus will be on mastering advanced topics like Natural Language Processing (NLP) and Computer Vision (CV), culminating in end-to-end projects.

Month 10:

This month focuses on building upon your Deep Learning knowledge from Quarter 2 and delve into Computer Vision (CV). CV is pivotal for data science aspirants as it enables the analysis and interpretation of visual data, facilitating tasks like image recognition and object detection.

Month 11:

Following proficiency in CV, we will shift our focus to NLP, another significant aspect of Deep Learning. NLP empowers data scientists to work with unstructured text data, enabling tasks like sentiment analysis and language understanding.

Month 12:

With a comprehensive skill set, embark on end-to-end projects and document them on GitHub. These projects simulate real-world problem-solving scenarios, allowing you to apply your data scientist skills effectively.

Post-Quarter 4

Upon completing the fourth quarter, you’ll be well-equipped to apply for full-fledged Data Scientist roles. Leveraging your expertise in NLP and CV, consider exploring the creation of AI applications. Explore further tutorials on building applications like Chatbots such as ChatGPT or image generator applications to enhance your skills and creativity in this domain. These applications demonstrate your proficiency and can even spark new ideas for future projects or career directions.

Conclusion

As we conclude this comprehensive guide to becoming a data scientist in 2024, it’s clear that the journey is not merely a roadmap but 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. With dedication and diligently following this guide, aspirants will possess the requisite skills and the confidence to apply for and thrive in the data science domain. The skills acquired throughout this journey form a solid foundation, empowering individuals to innovate, create, and contribute significantly to the exciting landscape of data science in 2024 and beyond.

I hope you like the article! This is the appropriate spot to learn about becoming a data scientist. Get the skills you need by following this simple data science roadmap. You can follow the steps outlined in the Data Scientist Roadmap 2024. And if working online is more your style, you can even become a data scientist. To succeed as a data scientist, just follow to this roadmap, and you’ll have a fantastic career!

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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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