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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

The Ultimate Learning Path to Become a Data Scientist and Master Machine Learning in 2024

Pranav Dar
Pranav Dar Last Updated : 28 Feb, 2024
4 min read

The Learning Path to Become a Data Scientist in 2020 is now live! Head over here to start your data science journey.

Introduction

Learning paths are immensely popular among our readers and with good reason! Learning paths take away the pain and confusion from the learning process. For those who don’t know what a learning path is – we take the pain of going through all the resources available on data science, machine learning and Artificial Intelligence, select the best ones, and arrange them in a logical sequence for you to follow.

If this sounds like a lot of work – it is. And it is done with the purpose of eliminating that huge amount of work you would otherwise have to do. We understand that when you are starting out in data science, the sheer amount of resources can be overwhelming. And hence we created learning paths. And our learning paths were an instant success with our community.

If learning paths were useful in 2018, this year they would be the most helpful tool for a person trying to become a data scientist. Why? Because the amount of content and information out there has increased multi-folds. And so has the confusion and the amount of knowledge expected out of a data scientist.

 

Access the complete learning path to become a data scientist in 2019 here. You will need to register yourself on our training portal – this will enable you to keep track of what you have covered.

 

Summary – Learning Path to become a Data Scientist in 2019

Broadly, the learning path to become a data scientist can be divided into the following steps:

  1. Getting Started: The biggest step of them all – beginning your data science journey. This stage is all about understanding what data science is and what a data scientist role entails. Additionally, this is where you should pick up the programming language and tool of your choice (our recommendation is Python). This will enable you to code through all that you learn in the coming months
  2. Learning Basic Maths and Statistics: What are the core concepts a data scientist must absolutely know? That would be statistics and mathematics. Where learning a tool will help you perform quick calculations and generate results, you can’t truly become a data scientist until you have a solid grasp on statistical methods (probability, descriptive and inferential stats) and mathematical fields (linear algebra to be precise). That’s why we emphasize on these two fields in this year’s learning path
  3. Learning Machine Learning concepts and applying them: This is where things start to hear up – you’re reading this article because you’ve been intrigued by what machine learning can do. And once you’re done with the above points (by March start if you follow the learning path), you will start learning the basics of machine learning. But this isn’t just limited to theoretical concepts. We firmly believe in learning by doing, hence we have provided some awesome projects so you can experience what a data scientist does!
  4. Some more applications of Machine Learning: Once you have a good grasp on these basic techniques, we move along in April to more advanced topics, like ensemble learning, random forest, boosting algorithms, and time series methods. But ML isn’t limited to just the algorithms, you need to know nifty tricks to improve your model, right? That’s where validation strategies and feature engineering will play a role. We also encourage you to keep your focus on industry applications, and have hence included a recommendation engine project in the learning path
  5. Introduction to Deep Learning: Now you know these machine learning concepts, what comes next? Deep learning of course! It’s becoming an essential part of any data scientist’s CV these days. July should see your data scientist path lean towards understanding neural networks and getting the hang of Keras
  6. Various deep learning architectures like RNN, CNN: Follow that up with a deep dive into advanced neural network frameworks, namely recurrent neural networks and convolutional neural networks. These are fairly heavy concepts, hence we recommend spending a few weeks on understanding them from scratch
  7. Computer Vision Applications: Computer vision is one of the hottest fields right now and hence we have focused a lot on this domain. We feel every data scientist should absolutely have this on their resume since this is where a lot of the jobs in the future will come up. We have included a really cool project to give you a practical understanding of how a computer vision model works
  8. Natural Language Processing (NLP): No data scientist learning path is fully complete without first going over NLP. You should focus on learning the basics at the very least, including text preprocessing and text classification. If you’re feeling adventurous, you can explore how deep learning works in NLP but that’s not a mandatory requirement

We have broken down all these steps month on month – so if you start following the learning path, you know exactly what you need to follow and what you need to cover every month starting today.

You can access the full learning path here and register yourself to start your journey today. Our training portal enables you to track your progress after each section thus helping you to stay on track throughout the year.

Here is an image laying out what should you do month on month to become a data scientist by the end of 2019. If you put in all the efforts as mentioned in the learning path – you will be well placed to get into a data scientist role before the end of the year.

We have one more gift to make your new year truly special. Join Analytics Vidhya’s CEO and Founder Kunal Jain on January 10th for an exclusive webinar where he will elaborate on how to get the most out of this learning path. He will discuss the roadmap to become a data scientist in 2019. Get your questions answered and your doubts clarified by one of the eminent personalities in this field!

We have done all the hard work to make sure you have all you need on 1st January to become a data scientist this year. Now, it is your turn. As always, if you have any questions – do let us know. See you more often on Analytics Vidhya in 2019!

learning pathlearning path 2019machine learningmachine learning path
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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Responses From Readers

Clear

YASH PAL
YASH PAL

Thanks for giving this information. Am looking for it..

123

Pranav Dar
Pranav Dar

You're welcome, Yash, and all the best for your journey!

123
Peter
Peter

Anything for Apple store?

123

Pranav Dar
Pranav Dar

Hi Peter, Thanks for reaching out. The entire learning path is available on our trainings platform to enable you to track your progress in each section: https://trainings.analyticsvidhya.com/courses/course-v1:AnalyticsVidhya+LPDS2019+LPDS2019_T1/about

123
Hitman2552
Hitman2552

What is the difference between this learning path and the learning path for Deep Learning, Are both learning paths applicable to freshers???

123

Pulkit Sharma
Pulkit Sharma

Hi Hitman, Yes! both the learning paths are for freshers. This one is for people who wants to become data scientists by the end of 2019 and the other one is for people who wants to become deep learning experts.

123

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Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
The Ultimate Learning Path to Becoming a Data Scientist in 2024
The Ultimate Learning Path to Becoming a Data Scientist in 2024
Learning Path to Become a Data Scientist in 2024
Learning Path to Become a Data Scientist in 2024

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