How to Break Into Data Engineering #dataengineeringessentials #dataengineering

DeepLearningAI · Beginner ·📐 ML Fundamentals ·1y ago

Skills: ML Pipelines80%Data Literacy60%

Key Takeaways

Breaking into data engineering as a junior can be challenging due to the limited availability of junior roles, with many companies preferring experienced data engineers to handle pipelines, and alternative entry points such as software engineering or data analyst roles are often considered

Full Transcript

data engineering is a weird field cuz like if you're trying to get that first job trying to find those like Junior roles like there isn't that many Junior roles generally speaking for data engineering just because a lot of companies they need they need one data engineer and they they don't want the one data engineer to be a junior data engineer that's because like they want that person to to handle the pipelines so like that's a big thing that can happen so like finding that entry foothold role can be hard so there's a couple different ways to go some people go like with an adjacent role where they like start as like a software engineer or they start as like uh a data analyst so for me in my career my first role was actually a software engineering role

Original Description

Data engineering can be tough to break into—most companies don’t hire junior roles. Many start as software engineers or data analysts. Zach Wilson began his journey in a software engineering role himself! Learn more about his path in the Data Engineering professional certificate: https://bit.ly/3Y4Qb70

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Forward and Backward Propagation (C1W4L06)

Forward and Backward Propagation (C1W4L06)

deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin

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deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow

deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow

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deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy

Using an Appropriate Scale (C2W3L02)

Using an Appropriate Scale (C2W3L02)

Gradient Checking (C2W1L13)

Gradient Checking (C2W1L13)

Gradient Checking Implementation Notes (C2W1L14)

Gradient Checking Implementation Notes (C2W1L14)

Learning Rate Decay (C2W2L09)

Learning Rate Decay (C2W2L09)

Understanding Mini-Batch Gradient Dexcent (C2W2L02)

Understanding Mini-Batch Gradient Dexcent (C2W2L02)

Mini Batch Gradient Descent (C2W2L01)

Mini Batch Gradient Descent (C2W2L01)

The Problem of Local Optima (C2W3L10)

The Problem of Local Optima (C2W3L10)

Exponentially Weighted Averages (C2W2L03)

Exponentially Weighted Averages (C2W2L03)

Tuning Process (C2W3L01)

Tuning Process (C2W3L01)

Understanding Exponentially Weighted Averages (C2W2L04)

Understanding Exponentially Weighted Averages (C2W2L04)

Bias Correction of Exponentially Weighted Averages (C2W2L05)

Bias Correction of Exponentially Weighted Averages (C2W2L05)

Gradient Descent With Momentum (C2W2L06)

Gradient Descent With Momentum (C2W2L06)

Normalizing Activations in a Network (C2W3L04)

Normalizing Activations in a Network (C2W3L04)

Hyperparameter Tuning in Practice (C2W3L03)

Hyperparameter Tuning in Practice (C2W3L03)

Adam Optimization Algorithm (C2W2L08)

Adam Optimization Algorithm (C2W2L08)

RMSProp (C2W2L07)

RMSProp (C2W2L07)

Fitting Batch Norm Into Neural Networks (C2W3L05)

Fitting Batch Norm Into Neural Networks (C2W3L05)

Why Does Batch Norm Work? (C2W3L06)

Why Does Batch Norm Work? (C2W3L06)

Batch Norm At Test Time (C2W3L07)

Batch Norm At Test Time (C2W3L07)

Softmax Regression (C2W3L08)

Softmax Regression (C2W3L08)

Deep Learning Frameworks (C2W3L10)

Deep Learning Frameworks (C2W3L10)

Neural Network Overview (C1W3L01)

Neural Network Overview (C1W3L01)

Training Softmax Classifier (C2W3L09)

Training Softmax Classifier (C2W3L09)

Why Deep Representations? (C1W4L04)

Why Deep Representations? (C1W4L04)

Gradient Descent For Neural Networks (C1W3L09)

Gradient Descent For Neural Networks (C1W3L09)

Neural Network Representations (C1W3L02)

Neural Network Representations (C1W3L02)

TensorFlow (C2W3L11)

TensorFlow (C2W3L11)

Activation Functions (C1W3L06)

Activation Functions (C1W3L06)

Explanation For Vectorized Implementation (C1W3L05)

Explanation For Vectorized Implementation (C1W3L05)

Getting Matrix Dimensions Right (C1W4L03)

Getting Matrix Dimensions Right (C1W4L03)

Understanding Dropout (C2W1L07)

Understanding Dropout (C2W1L07)

Building Blocks of a Deep Neural Network (C1W4L05)

Building Blocks of a Deep Neural Network (C1W4L05)

Why Non-linear Activation Functions (C1W3L07)

Why Non-linear Activation Functions (C1W3L07)

Computing Neural Network Output (C1W3L03)

Computing Neural Network Output (C1W3L03)

Backpropagation Intuition (C1W3L10)

Backpropagation Intuition (C1W3L10)

Train/Dev/Test Sets (C2W1L01)

Train/Dev/Test Sets (C2W1L01)

Deep L-Layer Neural Network (C1W4L01)

Deep L-Layer Neural Network (C1W4L01)

Random Initialization (C1W3L11)

Random Initialization (C1W3L11)

Other Regularization Methods (C2W1L08)

Other Regularization Methods (C2W1L08)

Normalizing Inputs (C2W1L09)

Normalizing Inputs (C2W1L09)

Derivatives Of Activation Functions (C1W3L08)

Derivatives Of Activation Functions (C1W3L08)

Parameters vs Hyperparameters (C1W4L07)

Parameters vs Hyperparameters (C1W4L07)

Vectorizing Across Multiple Examples (C1W3L04)

Vectorizing Across Multiple Examples (C1W3L04)

What does this have to do with the brain? (C1W4L08)

What does this have to do with the brain? (C1W4L08)

Dropout Regularization (C2W1L06)

Dropout Regularization (C2W1L06)

Vanishing/Exploding Gradients (C2W1L10)

Vanishing/Exploding Gradients (C2W1L10)

Basic Recipe for Machine Learning (C2W1L03)

Basic Recipe for Machine Learning (C2W1L03)

Bias/Variance (C2W1L02)

Bias/Variance (C2W1L02)

Forward Propagation in a Deep Network (C1W4L02)

Forward Propagation in a Deep Network (C1W4L02)

Weight Initialization in a Deep Network (C2W1L11)

Weight Initialization in a Deep Network (C2W1L11)

Numerical Approximations of Gradients (C2W1L12)

Numerical Approximations of Gradients (C2W1L12)

Regularization (C2W1L04)

Regularization (C2W1L04)

Why Regularization Reduces Overfitting (C2W1L05)

Why Regularization Reduces Overfitting (C2W1L05)

Breaking into data engineering can be tough, but starting as a software engineer or data analyst can provide a foothold, and learning about data pipelines and data engineering essentials can help, the Data Engineering professional certificate can provide a comprehensive learning path

Key Takeaways

Research data engineering roles and responsibilities
Explore adjacent roles such as software engineering or data analyst
Learn about data pipelines and data processing systems
Develop skills in data analysis and visualization
Consider pursuing a Data Engineering professional certificate

💡 Many companies prefer experienced data engineers, so finding a junior role can be challenging, but starting in an adjacent role can provide a path to data engineering

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