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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

Data Science Immersive Bootcamp – Hands-on Internship with Job Guarantee!

Sneha Jain
Sneha Jain Last Updated : 03 Dec, 2019
4 min read

“I have applied for various data science roles but I always get rejected because of a lack of experience.”

This is easily the most common issue I’ve heard from aspiring data scientists. They put in the hard work to learn the theoretical aspect of data science but when it comes to applying it in the real world, not many organizations are willing to take them on.

No matter how well you do in the interview round – the hiring manager always finds the lack of data science experience as the main sticking point.

So what can you do about this? It is a seemingly unassailable obstacle in your quest to become a rockstar data scientist.

We at Analytics Vidhya understand this challenge and are thrilled to launch the Data Science Immersive Bootcamp to help you overcome it!

This is an unmissable opportunity where you will get to learn on the job from data science experts with decades of industry experience.

“In the Data Science Immersive Bootcamp, we are not only focusing on classroom training – we provide hands-on internship to enrich you with practical experience.”

So you get the best of both worlds – you learn data science AND get to work on real-world projects.

 

Let’s Gauge the Benefits of the Data Science Immersive Bootcamp

This Bootcamp has been created by keeping Data Science professionals at heart and industry requirements in mind. Let’s dive in to understand the benefits of Data Science Immersive Bootcamp:

  • Learn on the job from Day 1: This is a golden opportunity where you can learn data science and apply your learnings in various projects manned by you at Analytics Vidhya during the course of this Bootcamp
  • Work with experienced data scientists and experts: The best experts from different verticles will come together to teach and mentor you at the Bootcamp – it is bound to boost your experience and knowledge exponentially!
  • Work on real-world projects: Apply all that you learn on the go! Real challenges are faced when you dive in to solve a practical problem and cruising through that successfully will hone and fine-tune your blossoming data science portfolio
  • Peer Groups and Collaborative Learning: Best solutions are derived when you learn with the community! And this internship gives you an opportunity to be part of several focused teams working on different data projects
  • Build your data science profile: You will get to present your work in front of Analytics Vidhya’s thriving and burgeoning community with over 400,000 data science enthusiasts and practitioners. You are bound to shine like a star after getting such an exhaustive learning and hands-on experience
  • Mock interviews: Get the best hack to crack data science interviews

 

Unique Features of the Data Science Immersive Bootcamp

There are so many unique features that come with this Bootcamp. Here’s a quick summary of the highlights:

data science bootcamp

 

Curriculum of Data Science Immersive Bootcamp

Data Science Immersive Bootcamp is one of the most holistic and intensive programs in the data science space. Here’s a high-level overview of what we will cover during this Bootcamp:

  • Python for Data Science
  • Linear Algebra
  • SQL and other Databases
  • Statistics – Descriptive and Inferential
  • Data Visualization
  • Structured Thinking & Communications
  • Basics of Machine Learning
  • Advanced Machine Learning Algorithms
  • Deep Learning Basics
  • Recurrent Neural Networks (RNN)
  • Natural Language Processing (NLP)
  • Convolutional Neural Networks (CNN)
  • Computer Vision
  • Building Data Pipelines
  • Big Data Engineering
  • Big Data Machine Learning

Wholesome Data Science is what we call it – everything you need to learn is presented in a single platter!

 

How to apply for the Data Science Immersive Bootcamp?

Here are the steps for the admission process to the Data Science Immersive Bootcamp:

  • Online Application – Apply with a simple form
  • Take the Fit Test – No knowledge of data science expected
  • Interaction with Analytics Vidhya team (Gurgaon) – Interview round to screen the best candidates
  • Offer Rollouts – The chosen candidates will be sent the official offer to be a part of the Bootcamp!
  • Offer Acceptance
  • Welcome to AV’s Data Science Immersive Bootcamp!

 

Fee Structure and Duration for the Data Science Immersive Bootcamp

Admission Fees: INR 25,000/-

Note: It is non-refundable and will get adjusted with the entire upfront payment (INR 3,50,000) or with 1st Installment (in case of Installment Payment Plan).

Option 1:  (If paying all upfront)

  • INR 3,51,000/-

Option 2: (Installment Payment Plan)

  • 1st Installment – INR  99,000/-  (30 days before the start date)
  • 2nd Installment – INR 1,25,000/- (within 60 days of the start date)
  • 3rd Installment – INR 1,25,000/- (within 120 days of the start date)

Here are the details of the program:

Duration of Program: 9 months / 40 weeks

Internship Stipend (from month 1): Rs. 15,000/- per month

Number of Projects: 10+ real-world projects

No. of Seats in the Bootcamp: Maximum 30

 

And The Most Awaited Aspect – You Get A Job Guarantee!

As mentioned above, this Bootcamp will enrich you with knowledge and industry experience thus making you the perfect fit for any role in Data Science. Bridging the gap between education and what employers want – the ultimate jackpot Analytics Vidhya is providing!

  • Build your data science profile and network
  • Create your own brand
  • Learn how to ace data science interviews
  • Craft the perfect data science resume
  • Work on real-world projects – a goldmine for recruiters

Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century.

And do not forget to register with us TODAY! Only 30 candidates will get a chance to unravel the best of Data Science in this specialized Bootcamp.

data science bootcampData science immersive bootcampdata science internshipdata science trainingmachine learning internship
Sneha Jain
Sneha Jain

The heart of every marketing campaign is great content and I love churning just that! I am a Data Science content marketing enthusiast. Exploring the field of applied Artificial Intelligence and Machine Learning and consistently being involved in editing the content at Analytics Vidhya is how I spend my day. I have always been fueled by the passion to do something different. The core of me is always eager to explore and learn more and more each day not only in the field of Data Science but also in the field of Psychology.

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Responses From Readers

Clear

Leela Anand
Leela Anand

Any plans to start this bootcamp in Chennai , Bengaluru ?

123

Pranav Dar
Pranav Dar

Hi Leela, This is currently planned exclusively for Gurugram. We don't have any updates on Chennai or Bengaluru yet. Thanks, Pranav

123
Mohammad Shahnawaz
Mohammad Shahnawaz

Hello, Content is good.

123

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Top 10 Data Analytics Projects with Source Codes
Top 10 Data Analytics Projects with Source Codes
All India Data Science – Student Scholarship Test
All India Data Science – Student Scholarship Test

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