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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 Long Does It Take to Become a Data Scientist?

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 15 Feb, 2024
5 min read

Introduction

Data science has become one of the most valuable skills in the tech market. Before the data science evolution, it would take up to 11-12 years to finish processing the data of millions of test cases. But now, it takes months, sometimes just weeks! So, how long does it take to become a data scientist? Amazingly, you can be a data scientist in just a year. It depends on your learning pace and consistency. Let’s look at the tentative time it takes to become a data scientist and why you should become one.

Table of Contents

Why Opt For a Data Science Career?

data science vector,how long does it take

Machine learning and AI are taking over the world, thanks to the continuously evolving technology world. The data science market revenue is estimated to reach $322.9 billion by the year 2026. The accelerating adoption of technology, big data, and ML algorithms in businesses has led to a fast growth of data science.

According to the BLS (Bureau of Labor Statistics), a data scientist earns about $1,00,000 on average. With numerous career opportunities available, you can become a data analyst, data scientist, etc., with a high pay scale that serves your skills.

How Long Does It Take to Become a Data Scientist?

The journey to becoming a data scientist can be different for every individual. You can learn data science in 12 months if we divide each month into specific topics. Anyone can master the art of data science within a year with consistent efforts and a zeal to learn.

However, the learning curve depends upon the consistency and the time you spare to learn data science. Some individuals can master data science in comparatively less time due to their prior knowledge of data science.

how long does it take to learn data science

Follow along and learn the basic and complex concepts of data science within 12 months. Let’s see how long it takes to learn data science with the content blueprint for every month.

Month 1: Data Science Toolkit

Let’s kickstart your journey of becoming a data scientist with basic data science tools. The basic understanding of data science is developed by learning common but essential data science tools, which include Python and its libraries like NumPy, Panda, Matplotlib, and Seaborn, which can set a strong foundation for the upcoming months.

Month 2: Data Visualization

After laying a strong foundation, we move on to the next stage of becoming a data scientist and mastering the art of data visualization. Get familiar with the data visualization tools, such as Tableau, and techniques to plot graphs, distribution maps, etc. It will also embark on the new beginning of learning SQL.

Data visualization tools for data science professionals

Month 3: Data Exploration

The third month focuses on data exploration with the help of hidden data. Data exploration refers to exhibiting informational data in the form of crucial insights. This month will teach you how to explore the data with EDA (Exploratory Data Analysis). You can expect to learn the basics of statistics required to become a data scientist.

Month 4: Basics of Machine Learning and the Art of Storytelling

This month will begin your adventure into the wonders of machine learning. You will learn the basics of machine learning and get familiar with the technical terms and techniques. You will uncover the art of storytelling with the help of structured thinking this month.

Data storytelling using Generative AI

Month 5: Advanced Machine Learning

Things will start to get serious from the fifth month as you will learn the advanced machine-learning algorithms to elevate your skill set. In this month, you can expect to learn feature engineering and how to work with text and images.

Month 6: Unsupervised Machine Learning

In this month, learn to work with unstructured and unlabeled data. Learn to handle the unstructured data with unsupervised machine learning algorithms, such as PCA, clustering, K-Means, anomaly detection, etc. You will finally set your foot in the world of projects.

Month 7: Recommendation Engines

Recommendation systems are the backbone behind the precise recommendations by Netflix, YouTube, Zomato, etc. In the seventh month, learn the logic behind different recommendation techniques and how to build recommendation engines. Also, unleash the exciting project further.

Month 8: Working with Time Series Data

Many organizations across the world depend on time series data to observe the repeated measurements of a dataset over time. In this month, learn how to handle the time series data, along with effective techniques to solve the problems on time series.

time series forecasting

Month 9: Introduction to Deep Learning and Computer Vision

Do you ever wonder how self-driving cars and mask-detection cameras work? Deep learning and computer vision are the pioneers behind revolutionary innovations like this in AI. This month will initiate your journey to learn the basic techniques of deep learning algorithms. You will be implementing this knowledge by solving various computer vision projects.

Month 10: Basics of Natural Language Processing

If you have ever used ChatGPT, it processes text based on the NLP algorithm. In this month, you will be paying attention to natural language processing. In this month, you will dive deep into the deep learning architectures and solve the projects based on NLP.

Month 11: Model Deployment

Well, you are finally at the step to deploy your own data science models and implement your knowledge to practical use. You will be focusing on various ways to deploy your model. It includes learning how to streamline the deployment process, Deployment of data science models using Flask, and using AWS to your advantage.

Month 12: Projects and Jobs

Hooray! You are completely ready to enter the sea of opportunities from this month. Start building a strong portfolio with various projects and apply for different internships and jobs in your dream company.

With an understanding of how long it takes to learn data science, it all boils down to your familiarity with some of the topics listed above. If you do possess a basic understanding of relevant concepts, you can start your glorious career to become a data scientist in just 6-8 months.

Conclusion

The world of AI and data science is intriguing because of how fast things are evolving. How long does it take to become a data scientist? After decoding this question, the next big challenge is finding the right resources that lead to mastering data science and AI.

Analytics Vidhya got you covered with their extensive AI and ML Blackbelt+ program. Consider it as a compilation of all the comprehensive courses offered by Analytics Vidhya that will set a detailed path for your AI journey.

careerComputer Visiondata explorationdata sciencedata science careerdata scientistdata visualizationdeep learninglearn data sciencelearning pathmachine learningNLPtechniquestimeTime Series
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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Here’s What You Need to Know to Become a Data Scientist!
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