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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
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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

Data Engineering – Concepts and Importance

VIKRAM
VIKRAM Last Updated : 01 Jul, 2021
5 min read

This article was published as a part of the Data Science Blogathon

Introduction

First of all, we are surrounded by data in day-to-day life. It shows us that software engineering wants an additional category to have data engineering, which is useful in many real-time platforms like data storage, transportation, etc.

Image Data engineering
Image Source: Unsplash

In this article, we will learn the concepts like

  • The role of Data Engineering
  •  Responsibilities of Data Engineers
  •  Data Engineering skills
  • Other fields related to Data Engineering

The Role Of Data Engineering:

Data Engineering is the field associated with analysis and tasks to get and store the data from other sources. Then, process those data and convert them into clean data used in further processes such as Data Visualisations, Business Analytics, Data Science solutions, etc.

Data Engineering converts Data Science more productive. If there is no such field, we have to spend more time preparing data analysis to solve complex business problems. So, Data Engineering requires a complete understanding of technologies, tools, faster execution of complex datasets with reliability.

The goal of Data Engineering is to provide organized, standard data flow to enable data-driven models such as ML models, data analysis. The above-mentioned data flow can get through several organizations and teams. To achieve the data flow, we use the method called data pipeline. It is the system that has independent programs that make several operations on stored data.

Data Engineering is responsible for the design, maintenance, extension, and construction support of data pipelines. Many data engineering teams are building data platforms. So many organizations cannot manage with just one pipeline to save data in an SQL database. Hence, they have many teams with several kinds of techniques to access data.

Responsibilities of Data Engineers:

Data Engineer is a technical person who is responsible for architecting, building, testing, and maintaining the data system. They are responsible to find recent trends in datasets and create efficient algorithms to make data more useful. They need required skills like programming, mathematics, and computer science, experience, and also soft skills to communicate the trends of data which help the business growth.

Some of the key responsibilities are:

  1. Get the required datasets for the problem statement
  2. Develop, construct, and maintain architectures
  3. Align the architecture with business requirements
  4. Develop the dataset process
  5. Usage of programming languages and tools to execute dataset
  6. Find the method to improve data reliability and efficiency
  7. Use large datasets to solve company issues
  8. Import machine learning and statistical methods
  9. Made the machine learning models such as predictive and prescriptive
  10. Use the required data to prepare tasks that will be automated
  11. Deliver the results to stakeholders based on the analysis which have been made

The different types of approaches made by data engineers are:

Data Flow:

We have to get input data in the form of XML data, batches of videos updated every hour, weekly batches of labeled images, and so on. Data Engineers consume data, design a model that can take those data from several sources, convert and store them.

Data Normalization and Modeling:

Data Normalization involves tasks that make those data more convenient to customers. It includes processes like clean the data, removing duplicates, and conforming data to a specific data model. Data Engineers store the normalized data in a relational database or data warehouse. Data normalization and modeling are part of the transform step of ETL(extract, transform, load) pipelines. Another way of transforming the method is data cleaning.

Data Cleaning:

Data cleaning is the process of fixing or removing the incorrect, corrupted, incorrectly formated, duplicate, or incomplete data within the dataset. If we combine many datasets, there are many problems like duplicating, mislabel, incorrect outcomes, unreliable outputs.

In this method, we remove the duplicates or irrelevant observations, fix the structural errors, filter the unwanted outliers, handle the missing data, and finally give us the effective dataset without any null values.

Data Accessibility:

It is one of the important responsibilities of the customer side data engineering team. Data Accessibility means the user’s ability to access or retrieve the data stored within a database or other repository.

Data Engineering Skills:

Data Engineering skills are mostly as same as skills needed for software engineering. In this section, we will see important skills like:

1. Programming languages

2. Databases

3. Cloud Engineering

Programming Languages:

Data Engineers should have a basic understanding of design concepts like data structures and algorithms, and object-oriented programming. The most popular programming language which was used for data engineering is Python. It is also widely used by machine learning and Artificial Intelligence teams. Scala is also a popular language like Python which is a functional language that runs on the Java Virtual Machine(JVM).

Databases:

If we have more data for usage, we need some databases which can store those data in a warehouse. Mostly used database technologies such as SQL and NoSQL. SQL databases are coming under the category of relational database management systems(RDBMS). NoSQL databases are databases that can store non-relational data such as document stores in MongoDB, graph databases stores in Neo4j, and so on.

Cloud Engineering:

In this technique, we use a method to have independent segments of a pipeline that is running on separate servers made by a message like Apache Kafka. These systems need many servers, and overall distributed teams need access to the data often. There are so many private cloud providers like AWS(Amazon Web Services), Microsoft Azure, and Google Cloud which are the most popular tools to build and develop distributed systems.

Other Fields Related To Data Engineering:

There are some of the fields that are closely related to data engineering as follows:

1) Data Science:

Data science is the subset field of data engineering that is data scientists derive insights from various datasets whereas data engineers create reusable programs using software engineering techniques. Data scientists use statistics, machine learning algorithms, Python or R language to explore efficient data which will be reusable, extensive.

2) Machine Learning Engineering:

Machine learning engineering is the field of using software engineering techniques and analytical data science knowledge and create a new efficient machine learning model which is useful for product users or consumers. For example, an ML engineer can develop a new recommendation algorithm for a company’s product, whereas a data engineer provides the data used to train and test the algorithm made by the ML engineer.

3) Business Intelligence:

Business intelligence is the process by which enterprises use strategies and technologies to analyze the data with the aim of improving decision-making and provide a competitive advantage. Data science is focused on making forecasting and future predictions, whereas business intelligence is focused on providing a view of the current state of the business. These teams based on data engineers to build some tools that made them analyze and inform relevant data.

Data Engineer Salary:

This professional career pays the biggest advantage for us. The average salary for data engineering roles between $65,000 and $135,000 and also depends on your educational qualifications, professional certifications, experience(in years) in the relevant field, additional skills, etc.

The annual salary for some of the top positions, according to the Bureau of Labor Statistics in 2019, such that:

1. Database Administrator – $93,750

2. Computer Network Architects – $112,690

3. Computer Research Scientists – $112,840

According to Glassdoor, the estimated base salary for Data Engineers in 2020 was $102,864 annually.

According to the reports from Indeed.com, Data Engineers can earn up to $129,415 annually with an additional possible bonus of $5,000.

As of April 2021, the average Data Engineer salary in the US falls anywhere between $90,000 and $126,133.

Conclusion:

Now, you can get an idea about some concepts and the importance of data engineering in real-world scenarios. This field is most suitable for those people who have an interest or educational background in computer science and technology fields. I hope that you are all excited about the blog. Does data engineering fascinate you? Let us know your thoughts in the comments!

Thanks for reading my article!

About the author:

Vikram Rajkumar – I am currently pursuing my Bachelor of Engineering (B.E.) in Electronics and Communication Engineering from Sri Krishna College of Engineering and Technology, Coimbatore. I have done projects and internships in the domain of data science and business analytics and also interested in data analysis, data visualizations.

LINKEDIN:  https://www.linkedin.com/in/vikram-rajkumar-3953a81b0/

GITHUB:  https://github.com/Viki183

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

VIKRAM
VIKRAM

I am a final year student pursuing Electronics and Communication Engineering at Sri Krishna College of Engineering and Technology, Coimbatore. I am always enthusiastic to learn new things and solve new challenges.
I am interested in Machine Learning and also did projects related to supervised learning. Also, I have done learning stuffs on Numpy, Pandas, Matplotlib, ML algos and so on.
I am skilled in C, Java, Data Structures, Algorithms, Python and Problem Solving.
I also wrote blogs on Machine Learning and Data Analysis which can see in "Medium.com".
I have started a YouTube Channel in August 2020, which has now more than 200K+ views and have more than 150+ videos. It is based on smartphones comparison and trolls.

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Yeah I am agree, it is the best

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Organizations must consciously invest in developing their data engineering capability in order to have a successful analytics program. At Nallas, we always believe data engineering and data analytics should work hand in hand.

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