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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 Becoming a Data Scientist in 2025

Kunal Jain
Kunal Jain Last Updated : 24 Dec, 2024
5 min read

Introduction

So you’ve taken the plunge. You want to become a data scientist. But where to begin? There are far too many resources out there. How do you decide the starting point? Did you miss out on topics you should have studied? Which are the best resources to learn?

Don’t worry, we have you covered!

Analytics Vidhya’s learning path for 2016 saw 250,000+ views. In 2017, we went even further and saw an incredible 500,000+ views! So this year, we have made the learning path more interactive than ever before and we can’t wait for you to experience it yourself.

 

What changes have we made in the learning path?

This year, the learning path has been designed on a completely new LMS portal. This portal allows you to track your progress as your data science journey continues. We have designed questions and exercises after each module to test your understanding. You will also be able to access the related hackathons / practice problems from the same place.

We even have a discussion portal within the learning path where you can share your doubts and queries and even post the awesome projects you’re working on!

Take a sneak peek below of how the progress tracking looks like:

Here it is then – the ultimate learning path to becoming a Data Scientist in 2018!

 

Just a few things to note before you experience our new LMS portal:

  1. We have published the resources for January and February to get you started on your path. We will publish the remaining links in the next couple of weeks.
  2. The majority of this learning path is based on learning through Python. Why is this so – because we are increasingly getting convinced that Python is the way to go for complete beginners (at least for 2018).

Below is a summary of the learning path – an overview what you should follow throughout the year. Let’s get cracking

 

January

Getting Started with Data Science and Python

By the end of January, you’ll know what role data science plays in the industry. You’ll also be able to answer the burning question – why use Python and how is it useful?

 

February

Statistics, Data Exploration and Basic Data Visualization

Before this month is over, you should have a firm grasp over the basics of statistics. You should also be proficient at exploring the dataset given to you and know the role data visualization plays in this. The budding data scientist is slowly coming out!

 

March

Probability and Machine Learning Basics (Part I)

Time to get into machine learning!

By the end of the month, you should have a firm command on the basic machine learning topics like linear and logistic regression, among others. To test what you’ve learnt so far, we will provide you with two projects to apply your newly acquired data science skills!

 

April

Machine Learning Basics (Part II) and Feature Engineering

Continue learning the ML basics and by the end of April, you should know enough to take part in hackathons are secure a decent rank. Also, go in depth into feature engineering – one of the MOST important things in data science.

 

May

Build Your Data Science Persona

Building models is not enough. The real test of a data scientist comes in explaining the power of the model you’ve created to non-technical people. By the end of May, you should have structured your thinking and personality as a data scientist to be able to do this.

This is a very critical month in your progress. Attempt to get a high ranking in hackathons and competitions all the while learning how to make an impactful presentation of your work. Also, start looking for an internship; you should have enough knowledge by now to secure one.

 

June

Advanced Machine Learning and Time Series Modeling

Deep dive into advanced machine learning. With half the year behind you, you should be ready to tackle advanced ML algorithms and time series models.

 

July

Dealing with Unstructured Data

Unfortunately, most real-world data comes in an unstructured format. This month you should get a deeper understanding of how to deal with unstructured data in business scenarios including learning the Natural Language Processing field. At the end of the month, you will be given a few projects to apply your newly learned skills.

 

August

Introduction to Deep Learning

Here comes one of the hottest data science subjects around – Deep Learning! By the end of August, you should be able to deal with basic neural network problems. As usual, we will provide you with a couple of projects to test your mettle.

 

September – October

PRACTICE!

Practice is the name of the data science game. Keep checking your progress by taking part in competitions.

By the end of October, you should also be familiar with topics like recommendation methods, and reinforced learning. This is also when you should start taking up a language like SQL to interact with databases (a truly important skill for a data scientist).

 

November – December

Apply for jobs and further enhance your portfolio

If you have seriously followed this plan, you should be able to deal with interview questions. Continue to acquire new skills, delve into Big Data and make sure you stick to your plan!

 

A few things to keep in mind:

A few pointers to make this learning path (and 2018) super successful for you:

  • Follow and experience it on our new LMS. It will need a one time sign up, but once you are done (or you sign in with your existing Analytics Vidhya id), you can track your progress and assignment at a single place. It sounds like I am selling you our new LMS – but it is free and makes life super easy.
  • Learn, Engage, Compete and Get Hired! That is exactly what you need to do. Make sure you follow the activities mentioned. Data Science is all about learning through practice and showing curiosity through data. Follow these and you will be hired before the year finishes.
  • Remember – depth matters more than the breadth – So, whatever you do, do it well. While we have laid out things on a time line – you should be doing this on your own pace and time. If you need more time to grasp statistics – you take that. You are not missing a bus by grasping the fundamentals

 

Let’s get started

2018 – here we come! I hope this year our path gets 1,000,000+ visits! We have made sure that we put all our wisdom and experience in creating it. Having said that – Is there anything you feel we should have included?

Or if you had taken our learning path last year – what was your experience and how do you like the current changes? Let us know your thoughts in the comments section below!

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Anil
Anil

Tried to enroll LMS. But didn't get any activation email(s). Tried with a couple of accounts. Still the same issue.

123

Kunal Jain
Kunal Jain

Hi Anil - let me check with our mail servers

123
Eswar
Eswar

Thank you was waiting for this post from long time. Awesome, you guys rock !!

123

Kunal Jain
Kunal Jain

Do share feedback about the portal and learning path, once you have used it

123
George
George

One month is not enough for the topics listed. Even one year wouldn't be enough to master the topics.

123

Kunal Jain
Kunal Jain

George - feel free to pace things at your own pace and comfort. As I said, quality of understanding and depth in subject trumps quantity. But, if you have to become a data scientist in a year - I am afraid that is the pace to keep. Regards, Kunal

123
Kimberly
Kimberly

Facts.

123

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