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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

All India Data Science – Student Scholarship Test

Abhiraj Suresh
Abhiraj Suresh Last Updated : 20 May, 2021
3 min read

Introduction

Analytics Vidhya’s commitment to creating a diverse and democratized data science ecosystem is still intact and helps us stand apart from the crowd. Our latest offering, Data Science Immersive Bootcamp, is another initiative to build this ecosystem through one-on-one teaching and peer-to-peer learning, thus, making candidates industry ready to tackle data problems.

But, with the pandemic wrecking our lives, we have observed that a lot of us are not able to continue learning for many reasons. And as a relief to all data science aspirants, Analytics Vidhya is introducing the All India Data Science – Student Scholarship Test for all the students interested in being part of the Data Science Immersive Bootcamp.

But before we dive into more details, here is a quick introduction to Data Science Immersive Bootcamp.

What is Data Science Immersive Bootcamp?

bootcamp

Data Science Immersive Bootcamp is our flagship Job Guarantee program that runs for 6 months, where you learn and do an internship with us from Day 1.

Over a period of 6 months, you are going to go through-

  • 500+Hours of Live online classes on Data Science, Data Engineering & Cloud Computing
  • 500+Hours of Internship
  • 20+Projects
  • 12+Tools
  • 50+Hours of Interview Preparation

unique features

You can explore more about it here.

 

About the All India Data Science – Student Scholarship Test

Scholarship test

All India Data Science – Student Scholarship Test is an initiative of Analytics Vidhya to recognize and support meritorious students to showcase their skills and talent and get rewarded in the form of Scholarships.

Under this All India Data Science – Student Scholarship Test, top students will receive a “Certificate of Merit” based on their Data Science Aptitude and get a chance to earn up to 100% Scholarship in our flagship Job Guaranteed Program: Data Science Immersive Bootcamp.

Note- This test is just the first round of the Data Science Immersive Bootcamp selection process. The applicability of scholarship depends on whether you clear the subsequent rounds of interviews with the faculty or not

Eligibility

College Student: Students Enrolled in Full-time Graduate/Post-Graduate Programs from University/College/Institute recognized by AICTE and who have passed/will pass on or after 31st March 2021.

Please note that this scholarship test is only for students and not working professionals

Topics –

The following topics will be tested in the All India Data Science – Student Scholarship Test-

  1. Probability
  2. Arithmetic Aptitude
  3. Permutations & Combinations
  4. Data Interpretation
  5. Puzzles & Logical Reasoning

 

Important Dates

The following dates need to be kept in mind-

Applications Open: 10-May-2021

Deadline to Apply: 23-May-2021

Scholarship Test Date: 23-May-2021

Test duration: 1 Hour

The test can be taken on 23rd May 2021 at any time. Once the student starts the test, the student will get 60 minutes to complete the test.

Benefits

Here are the benefits of the All India Data Science – Student Scholarship Test–

  1. “Certificate of Merit” for All India Top 100 students on the leaderboard
  2. Up to 100% Scholarship in Data Science Immersive Bootcamp (with Job Guarantee*).
  3. Total Scholarships more than INR 50 Lacs up for grabs.
  4. Rank 1: 100% Scholarship of INR 3,25,000
  5. Rank 2-5: Scholarship of INR 2,00,000
  6. Rank 6-10: Scholarship of INR 1,50,000
  7. Rank 11-20: Scholarship of INR 1,25,000
  8. Rank 21-50 : Scholarship upto INR 1,00,000

How can you apply?

  • Click on the Register button on the top right corner
  • Fill in the required details
  • Upload relevant documents.
  • Accept the terms and conditions
  • Click on the Submit button
Results will be available on the Registration page on 24th May-2021. Students will be notified about the results over email.

 

Register Now!

So what are you waiting for? Put on your thinking cap and register with us today here.

All the best!

Abhiraj Suresh
Abhiraj Suresh

My name is Abhiraj. I am currently a manager for the Instruction Design team at Analytics Vidhya. My interests include badminton, voracious reading, and meeting new people. On a daily basis I love learning new things and spreading my knowledge.

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