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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

Crucial Data Engineer Skills for a Successful Career

M
Meetu_Arora Last Updated : 01 Sep, 2022
4 min read

Introduction

Data engineering and data science have been one of the hottest trends in the vocational market for quite some time. Given the prediction by industry pundits and researchers, the demand for these two roles will witness a massive surge in the coming time. To build a successful career in data engineering, the aspirants need to understand data engineering skills completely.

data engineer skills

Source: Springboard

Covering basics first, data engineers are information technology professionals with expertise in diverse applications and processes. Data engineering and data science are the two branches working in conjunction with each other and can extract the best of raw and unstructured data. Data engineers act as a rope bridge for data scientists as they efficiently build pipelines that transform raw data into consumable data formats. Data engineers also play a crucial role in optimizing the data recovery process, developing dashboards, and generating reports.

In this article, we will explore what data engineers do, the essential skills required to be a data engineer, and how to start a career in data engineering.

 

What Do Data Engineers Do?

Multiple data science projects deploy various architectures, and data engineers are responsible for developing and maintaining the such architecture. They ensure that data flow between servers and applications remains uninterrupted. The data engineering branch combines the best of data science and software engineering elements.

The primary functions of a data engineer include:

  • Developing data collection processes
  • Integrating the newest software trends and data management modalities with the existing system
  • Streamlining the existing processes for data collection and consumption

 

Technical Skills Required to be a Data Engineer

Data engineers must possess the following technical and professional skills to build a successful career in data engineering. These are some of the most in-demand data engineer skills sought by prospective employers while hiring. Let’s look at them:

 

Database Systems

While speaking of technical skills required to be a data engineer, SQL and NoSQL are among the most sought-after. To be a successful data engineer, you should know how to efficiently work on and manipulate diverse database management systems for storing and retrieving information.

 

Data Warehousing Solutions

It is a simple job for data engineers to work on massive piles of data. While this data is extracted and lifted from multiple and extensive sources, data engineers must be well-versed with varied data warehousing solutions like MarkLogic, Oracle, Amazon RedShift, and so on.

 

ETL Tools

ETL Tools, also known as Extract, transfer, and load tools, belong to a technology category. They are responsible for gathering, moving, and reading raw data from multiple sources into a database or a BI (business intelligence) platform. The whole process aims to collect and analyze the right data to solve a specific business problem. This is one of the must-have data engineer skills.

 

Machine Learning Algorithms

Machine Learning or ML Algorithms is a specific science that makes future predictions based on previous data sets. Since it is the primary job of data engineers to collaborate closely with data scientists, it is an imperative data engineer skill to have a basic understanding of ML algorithms.

data engineer skills

Source: Udacity

Programming Languages

A data engineer must have a fair to a deep understanding of the common programming languages. All employers want to hire data engineers to have an understanding of the following programming languages for statistical analysis and modeling:

  • Python
  • Golang
  • Ruby
  • Perl
  • Scala
  • Java
  • SAS
  • R
  • MatLab
  • C and C++

 

Amazon Web Services (AWS)

Data engineers constantly harness AWS for designing automated data flows while ensuring its dexterity, innovation, and scalability.

 

Big Data

Big data stands for large chunks of structured and unstructured data. Big data forms a vital tool for data scientists and AI experts and makes it necessary for data engineers to have an exhaustive understanding of working with big data. This includes the processes starting from learning to store, clean, process, and extract information from the data.

 

Automation & Scripting

Since it is a simple job for data scientists to deal with large chunks of data, it becomes crucial for them to possess scriptwriting skills to automate repetitive tasks and save time and effort.

 

Business Skills Required to be a Data Engineer

Apart from the technical skills discussed above, it is highly crucial and advantageous for data engineers to possess the following business skills. Combining these two skill sets is the key to a successful run in data engineering. Let’s have a look at some of these business skills:

 

Analytical Problem Solving Skills

A data engineer’s job involves addressing high-level challenges on an everyday basis. They must keep a clear eye on the important ones while deploying the right approach and methods to make the most of given time and resources.

 

Impeccable Communication Skills

As a data engineer, you may be faced with situations where you’ve to explain the core technical aspects to completely non-technical persons. To thrive in such situations effectively, it is crucial to possess effective communication skills. Data engineer has to share their findings and suggestions with peers from data science and architects regularly with the help of clear and astound communication skills.

Intellectual Inquisitiveness

Possessing intellectual curiosity and a certain level of inquisitiveness is a must-have data engineering skill. This helps data engineers explore new avenues and territories to get out of the league and find creative and unusual ways to solve complex data issues.

 

Deep Industry Knowledge

To be a successful data engineer, it is highly crucial to have an in-depth understanding of how your chosen industry functions. This helps immensely in making data engineers understand how data is collected, analyzed, and used.

 

How to Become a Successful Data Engineer?

If you aspire to become a data engineer, follow the simple steps explained below:

  • Get your Bachelor’s Degree (Information Technology/Computer Science/Computer Engineering/Software Engineering/Applied Math/Physics/Statistics)
  • Course in Coding/Database Management/Algorithms/Data Structures
  • Go for an internship to develop programming skills and languages
  • Pursue certifications by IBM/Google and more
  • Consider an entry-level job to gain experience and broaden your horizons
  • Consider a Master’s degree in Computer Science or Computer Engineering

 

Conclusion

Data engineering career continues to grow steadily and offers lucrative job opportunities in diverse industries and sectors. The above article is a brief guide to the road map of becoming a successful data engineer by understanding the must-have data engineering skills.

Big dataComputer Sciencedata engineerdata engineering careerdata science
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9 Must-Have Skills to Become a Data Engineer!
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