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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: Learning Path to Master Data Engineering in 2022

Sakshi Raheja
Sakshi Raheja Last Updated : 14 Apr, 2022
3 min read

Dear Readers,

We are enthralled to bring to a new episode on ‘The DataHour’. In this session, Shashank Mishra, Ex- Amazon Data Engineer, will take you on a learning ride, ‘To Master Data Engineering in 2022‘ as we all know, how data engineering is shaping and plays a pivotal role when it comes to managing the data. I feel it is an excellent opportunity for freshers keen to build a career in Data Engineering.

Also, for professionals already in the field and aspire to work with big brands like Amazon. Shashank will be happy to share his journey with you all. It feels like working with one of the biggest brands that everyone aspires to. Also, what is all it takes to reach such a stature? I think the most critical aspect is to have your fundamentals right. Along with this, upskill yourself with the latest trends in the industry, every now and then.

The DataHour | Shashank Mishra

BOOK YOUR SEAT NOW! 🛋️

 

About The DataHour

Data Engineering has gained so much popularity starting in 2019 and is one of the best career profiles for aspiring Data Professionals in recent times. BigData is a very vast field with multiple job profiles, and that’s why there is a lot of confusion about different job profiles, their role & responsibilities, tech stack and how to decide which one is relevant for you?

In this DataHour, Shashank will share his 5 years of Data Engineering experience and will provide the best of the best roadmap to become a sound Data Engineer in 2022.

Prerequisites: Enthusiasm for learning Data Engineering!


Who is this Webinar for?

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


Speaker on The DataHour

The DataHour | Shashank Mishra

Shashank Mishra

Ex-Amazon Data Engineer

Shashank is an experienced Data Engineer with a demonstrated history of working in service and product companies such as Amazon, Paytm and McKinsey. He has solved various data mysteries for different domains like Aviation, Pharmaceutical, FinTech, Telecom and Employee Services. He also has good experience in designing scalable & optimized data pipelines to handle PetaBytes of data, with Batch & Real Time-frequency.

Currently, Shashank is an active contributor to the Data Science and Data Engineering community through his incredible podcasts and Youtube Channel (E-Learning Bridge)

Conclusion

I hope you’re excited to attend this DataHour session with us. Shashank is a prolific speaker who is bringing his enriching experience in Data Engineering which you wouldn’t want to miss! So, Stay tuned. If you wish to read some articles on Data Engineering. 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].

Data Engineeringdata engineering careerlearning pathMaster Data EngineeringThe DataHour
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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Nikhil Mishra’s Journey to Becoming a Kaggle Grandmaster
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