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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

DataHack Hour Revealed – the best way to learn data science through hands on problems!

Faizan Shaikh
Faizan Shaikh Last Updated : 21 Apr, 2017
6 min read

Introduction

As part of DataFest 2017, we launched a new initiative – DataHack Hour. DataHack Hour was inspired by numerous queries we get related to learning Data Science. Questions like “How to learn analytics?” or “How to become a data scientist?” are asked to us multiple time every day.

While we had written several articles on this subject on Analytics Vidhya – we needed something more definitive to answer these queries. In order to answer these queries, we decided to create an experience to show people how to learn Data Science. This version of DataHack Hour is our answer to the above questions or many more questions which come to us.

DataHack Hour is completely free to consume for Analytics Vidhya community and is created with an aim to help more and more people learn data science. This article will tell you the journey participants of DataHack Hour are undergoing. If you are one of the people struggling to learn Data Science – join DataHack Hour today and become part of this awesome experience.

 

Table of Contents

  • What is DataHack Hour?
  • Progress till date
  • Day 1: How to convert a business problem to analytics problem and hypothesis generation.
  • Day 2: Getting started with Jupyter Notebook and write your first program in Python.
  • Day 3: Data exploration and visualization methods.
  • Day 4: Detect and treat outlier & missing values.
  • Day 5: Build your first linear regression model.
  • Next steps and upcoming sessions
  • End Notes

 

What is DataHack Hour?

DataHack Hour is based on a very simple concept – “Daily small improvements or learning in small steps can make a huge difference over time.” It is the same principle which Jeff Olson describes in his book “The Slight Edge”. Let me explain this in a bit more detail.

Most of the queries which we receive on Analytics Vidhya about challenges in learning can fall in one of the following categories:

  • Not having access to Structured learning path and resources – this is a relatively simple problem to solve. We have Learning paths on Analytics Vidhya for precisely this. There are also other courses and specialisations which you can look at. But for some reason, the problem still persists.
  • Not having access to mentors and an ecosystem – This is comparatively more difficult to solve. A lot of people start their journey only to come across a problem. They do not have access to mentors or people who would have taken that journey before. So, if a newbie got stuck with installation of conflicting Python libraries – it might overwhelm him / her to solve the problem and move forward.
  • Not being able to learn things on a daily basis – This is probably the most difficult of all the problems to solve. You might have your work or family responsibilities which makes it difficult to take out dedicated time for learning on a daily basis.
  • Lack of motivation – this is hard to solve for!

We believe that DataHack Hour is the solution to the first 3 problems mentioned here. We believe that by going over one chapter at a time daily, with help of volunteers and mentors from community to help is the most powerful way to learn Data Science. You learn by solving hands on problem, the content has been curated by Analytics Vidhya team and there are mentors to help you out on a daily basis. Honestly, I can’t think of a better way to learn!

 

Launch of DataHack Hour

We launched DataHack Hour on 16th April 2017 as part of DataFest 2017. We got outstanding response from the community members and from people who want to really learn the subject.

We came across various users like EspyM, who said he does not have access to such resources in his country and in 5 days we have seen him devoting time to build first model and submit a solution to DataHack platform! I am pretty sure that by end of this DataHack Hour, we will have multiple people like EspyM who would enable learning in their own communities later on.

In order to raise awareness about DataHack Hour further, we are releasing the content of the first 5 days on our blog. The idea is to put the content out to a larger world and invite people who have missed out on 5 awesome days. You can still join today by learning the content below. You can register for DataHack here.

If you registered on DataHack Hour and missed out a particular day, you can go through the content below and come back on track.

 

Day 1 – Webinar

We kicked off Datahack Hour with this awesome session by Tavish Srivastava. The agenda of the webinar was “How to convert a business problem to analytics problem? and Importance of hypothesis generation”. This is the best place to start your journey about learning analytics. It also touches about the point which gets ignored in a lot of tool focussed courses today.

Here is the webinar recording from the session:

Hopefully you are all geared up for the hands on exercises to come!

 

Day 2 – Jupyter Notebook and Python Scripts

From Day 2 onwards we started our 1 hour challenges. The agenda for day 2 included the following:

  • Why learn Python for data analysis?
  • Python Data Structures
  • Conditional and iterative statements
  • Loading data
  • Understand pandas dataframes

Let us cover them one by one. You can download all Day 1 resources here after Logging in and Signing up. By end of the day you would have installed Anaconda, become comfortable with Jupyter notebook interface and would have written a few simple programs in Python and Pandas. We also cover different data structures in Python, iterative and conditional statement and ways to load and access the data.

Our mentor for the day was none other than me 🙂

Day 3 – Data Exploration and Visualization

This day was probably the most important session for a beginner after going through the first 2 days. On day 3, we got our hands dirty by doing data exploration and visualization. The topics covered in this session included the following:
  • Variable Identification
  • Univariate Analysis
  • Bi-variate Analysis
People started working on a dataset which included various types of variables – continuous, nominal and ordinal. People started plotting distributions and became equipped with the tools to understand the hidden insights in data. By end of the day, people plotted Box plots, histograms and were comfortable looking for correlations.
Mentor for Day 3 session was our community members – Ziron

 

Day 4 – Missing Values & Outliers

This session focussed on some of the practical challenges people face while doing exploratory analysis. Irrespective of how good is your data, you would come across missing values and Outliers. This session was aimed to help people deal with missing values and Outliers in the data. Again, you can access the content here after logging in and registering for DataHack Hour.

Topics covered in Missing Value

  • What is a missing value?
  • But why do missing values occur?
  • Impact of missing values
  • How to detect missing values?
  • Which are the methods to treat missing values?

Outlier detection

  • What is an Outlier?
  • Why do outliers occur?
  • Impact of Outliers
  • How to detect Outliers?
  • How to deal with Outliers?
By now, participants would have understood missing values, imputed it and would have handled the Outliers in the data. Let us progress to build our first Predictive model.
Mentor for the day was our volunteer Aman Kapoor

Day 5 – Building a Linear Regression model

On day 5, people will start build simple predictive models. The sessions starts with talking about what is a predictive model and enables you to build a simple and a multivariate regression model by end of this session. You can download the resources here.

  • What is Predictive Modeling?
  • Building the first model
  • How to find the best regression line?
  • Performance Evaluation Metrics in Regression
  • Multivariate Regression
  • Hands-on practice problem
  • A few points of caution when applying Linear Regression
Mentor for the day was our volunteer – Madhur Modi

Next Steps:

Here is the agenda for the coming days. If you think you got stuck in learning analytics and data science in past, come and join us in these DataHack Hour sessions. By end of this DataHack hour, you will be able to work on data science problems independently, would have 10+ mentors you wold have interacted with and a few hundreds of peers. All of this is available freely and can be absorbed as long as you are motivated!

Day 6: Feature Engineering and Transformation, helps to improve model performance

Day 7: Validating and measuring model performance

Day 8: Building logistic regression model

Day 9: Building naive bayes model

Day 10: Building a decision tree model

Day 11: Building a k-NN model

Day 12: Ensemble, Methods to combine model outcomes

Day 13: Apply your learnings on 6-hours hackathon

See you around in DataHack Hour.

End Notes:

Thanks a lot to our volunteers and mentors, who are helping out the community learn by the day. We are truly on our way to create thousands of real data scientists!
Faizan Shaikh
Faizan Shaikh

Faizan is a Data Science enthusiast and a Deep learning rookie. A recent Comp. Sc. undergrad, he aims to utilize his skills to push the boundaries of AI research.

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Responses From Readers

Clear

Jay
Jay

Are any of the slides/videos posted anywhere online for ppl who missed to see it ?

123

Faizan Shaikh
Faizan Shaikh

The material has not been released for now as DataHack Hour may recur at a later time.

123

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