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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 DataHour: Create Effective DS Notebooks and Communication

Sakshi Raheja
Sakshi Raheja Last Updated : 20 May, 2022
3 min read

Dear Readers,

We bring you yet another exciting DataHour session for you. This session will be conducted by Martin Henze who is a Data Scientist at YiptiData and is a Kaggle Grandmaster. He will conduct an enriching session on how to create effective Data Science Notebooks and Communication. Sounds exciting? Register now!

BOOK YOUR SEAT NOW! 🛋️

About the DataHour

Being able to clearly communicate your process and findings is an important, yet underrated skill for data scientists and machine learning practitioners. Engaging and accessible notebooks are the perfect way to combine your big picture presentations with a varying amount of code for more technical audiences. Built around well-designed data visuals, the ideal notebook captures the attention of its readers through masterful storytelling and important insights.

In this DataHour, Martin will share some tips and best practices for effective data science communication. Notebooks can play a powerful role at any step of the machine learning and data science workflow, and allow you to communicate and improve your work all the way from data ingestion to the final model or actionable insights. Moreover, these notebooks can serve as a reproducible documentation of your work and reduce technical debt. This DataHour will cover all those aspects, and give recommendations on how to design impactful notebooks according to your needs.


Prerequisites: Enthusiasm for learning Data Science!

Who is this DataHour for?

  • Students & Freshers who want to build a career in Data Science
  • Working professionals who want to transition to a data science career
  • Data science professionals who want to accelerate their career growth

Why is this Important for Data Scientist?

In the world of algorithms, it is important for the data scientists to make engaging and accessible notebooks. The technical audience must be engaged by notebooks that reflect elements of data visualisation. Also, with the pandemic, the world and its business, everything has become online. Thus, as a data scientist it is very important for you to improve your communication skills. Martin will give you tips with which you’ll be able to do effective data science communication.

 

Speaker

The DataHour

Martin Henze

Data Scientist at YipitData | Kaggle Grandmaster

Martin has a Ph.D. in Astrophysics from Technical University Munich and currently works as a Data Scientist at YipitData. He has extensive experience in data wrangling, EDA, and data visualization in Python & R; and is passionate about storytelling and communication.

He is one of the top members of the Kaggle community as the first ever Kernels Grandmaster, and former number 1 in the Notebooks ranking, with 23 gold medals to his name. His granular level documentation is well lauded within the community. In addition, he is a Discussions Grandmaster with 108 Gold Medals.

You can follow him on Linkedin, Twitter and Kaggle.

 

Conclusion

I hope you’re excited to attend this DataHour session with us. Martin is a prolific speaker who brings his enriching experience in Data Science communications and notebooks that you wouldn’t want to miss! So, Stay tuned. If you wish to read some articles on Analytics problems. Head on to our blog!

Grab this fantastic opportunity by registering here for the DataHour Webinar. If you’re attending this session and have some preliminary questions about this topic, please send them to us at [email protected], or you could ask directly to the speaker during the session.

If you missed our previously conducted ‘The DataHour’ series, head to our YouTube Channel and check out the recordings.

Connect

If you’re facing any difficulty registering or wish to conduct a session with us. Then, get in touch with us at [email protected].

BOOK YOUR SEAT NOW! 🛋️

 

data sciencedata science communicationdata science notebooksTheDataHour
Sakshi Raheja
Sakshi Raheja

I am a passionate writer and avid reader who finds joy in weaving stories through the lens of data analytics and visualization. With a knack for blending creativity with numbers, I transform complex datasets into compelling narratives. Whether it's writing insightful blogs or crafting visual stories from data, I navigate both worlds with ease and enthusiasm. 

A lover of both chai and coffee, I believe the right brew sparks creativity and sharpens focus—fueling my journey in the ever-evolving field of analytics. For me, every dataset holds a story, and I am always on a quest to uncover it.

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