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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

Common mistakes Data Engineers do in their Learning Path

S
Saurav Last Updated : 13 Apr, 2021
4 min read

It has been lately called that ‘data scientist’ is the sexiest job of the 21st century. However, now, data engineering jobs are poised to give data scientists tough competition. Data Engineering Jobs are getting more popular than Data Science jobs.

Common mistakes Data Engineers Meme

So once you’ve decided data engineering is the field for you, you need to understand that becoming a great data engineer is a journey and not a destination. Everyone talks about success stories and what to do for it, but nobody talks about the nuances of what not to do and where not to waste time.

It does not come easy. Industry experts keep complaining that there is a large gap between self-educated data engineer’s skills and real-world work in the field of data engineering.

In this article, I will discuss the common mistakes data engineers make in their learning path(I have made some of them myself). I have also provided tips wherever applicable with the aim of helping you avoid these pitfalls on your data engineering journey.

 

Table of Contents

  • Mistake #1: Not making data fundamentals strong

  • Mistake #2: Learning outdated/ legacy skills/technologies

  • Mistake #3: Missing the required depth/ breadth of topics

  • Mistake #4: Not doing ample hands-on practice

  • Mistake #5: Unable to visualize and understand the end to end picture

 

Mistake #1: Not making data fundamentals strong

Common mistakes Data Engineers 1

The first and foremost mistake data engineers make is not making their fundamentals base learning strong enough. A data engineer is expected to be reasonably good in coding/scripting and SQL as well. Without being able to work on simple programs if a data engineer directly jumps to write a complex data pipeline, it is definitely going to be a mess of a code.

Also, a data engineer should be conversant enough in the basics of databases and relational database management systems as well. Not understanding the difference between a primary key and a surrogate key is going to create problems even to define a simple data model.

 

Mistake #2: Learning outdated/ legacy skills/technologies

Common mistakes Data Engineers 2

The second common mistake data engineers do is to learn outdated technologies too much in-depth like learning too much in-depth Map Reduce OR Data warehousing concepts in Kimball /Inmon or some DWBI(Data Warehousing Business Intelligence ) tools which are not being used readily in the industry today. Time is a precious thing, learners can’t afford to miss focus on their learning priorities. It’s better to see the job descriptions and pick the most common skills like Spark, Kafka, NoSQL, Flink, etc rather than spending time and effort on outdated tools and techniques. But, do learn how to create Data models on NoSQL and Data lake systems.

 

Mistake #3: Missing the required depth/ breadth of topics

Mistake #3: Missing the required depth/ breadth of topics

I agree there are too many topics to be studied, there is Spark or Hive. Then, there are Kafka, NoSQL databases like Hbase or MongoDB. In-stream analytics, we have Spark streaming or Flink. On the cloud side, we have AWS, Azure, and GCP. So is it mandatory to be thorough in all of these tools and technologies? Absolutely not.

The need is to be proficient in the fundamental concepts in these data processing tools e.g how Spark internals work, how Kafka Pub-Sub mechanism works, and how NoSQL is different from SQL when to use which one. Preferably, we should go with any one of the options rather than focusing on everything.

Personal recommendation is to just learn one programming language: Scala/Python, Kafka, Spark, MongoDB/Hbase, and finally AWS for Cloud. Sometimes it is better to go with tools used in current projects when you don’t have an option.

 

Mistake #4: Not doing ample hands-on practice

Mistake #4: Not doing ample hands-on practice

This is something of paramount importance. Everyone just completes theory by reading documentations and some videos but no one really does the hard work of actually writing an end-to-end pipeline themselves. This not only leads to surprises and hiccups while working on actual projects but also shows the shallow knowledge when the interviewer starts to grill on the project hand on the part.

The recommendation is to start with a public dataset and a real-time API(e.g. Twitter etc). Ingest the dataset into Storage like HDFS and Kafka. Process it using Spark SQL/DS and Streaming(for real-time API data). Finally, presenting the insights in a visualized form like Tableau will add icing to the cake.

Performance optimization of the initial build of pipelines can further increase your chances of cracking the interviews.

 

Mistake #5: Unable to visualize and understand end to end picture

Unable to visualize and understand end to end picture

Finally, without knowing the end-to-end pipeline just focusing on ingestion or storage or processing will not make the data engineer understand what is going on with his/ her work. Apart from knowing business impact data engineers should also understand the technical architecture and system design of the data pipelines and supporting frameworks.

Things like DevOps, Platform Infrastructure, and Networking are completely ignored by data engineers. These are critical aspects and supporting frameworks that are definitely important to understand the end-to-end picture. A basic overview of these supporting frameworks is definitely important if not in-depth.

Hope you had a good time reading the 5 common mistakes data engineers make, do share your experiences and any questions on the above.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

blogathonCommon Mistakesdata engineerData Engineering Common Mistakes
S
Saurav

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13 Common Mistakes Amateur Data Scientists Make and How to Avoid Them?
13 Common Mistakes Amateur Data Scientists Make and How to Avoid Them?
Want to Become a Data Engineer? Here’s a Comprehensive List of Resources to get Started
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