Data Science Engineer

Description

A Data Science Engineer is a key role in a team that bridges data engineering and data science. The role involves working with large datasets, creating data pipelines, ensuring data availability, and supporting machine learning models for analysis and decision-making. Below is a comprehensive Data Science Engineer syllabus that can guide your preparation for such a role.

Duration : 300Hrs

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1. Mathematics and Statistics

• Linear Algebra

  • Vectors, Matrices, Eigenvalues, and Eigenvectors

  • Matrix decomposition (LU, SVD)

  • Principal Component Analysis (PCA)

• Probability and Statistics

  • Probability Distributions (Normal, Binomial, Poisson)

  • Hypothesis Testing, p-values, Confidence Intervals

  • Bayes’ Theorem, Maximum Likelihood Estimation

  • Sampling methods, Central Limit Theorem

  • Descriptive Statistics (mean, median, variance, skewness, kurtosis)

• Optimization Techniques

  • Gradient Descent (Batch, Stochastic, Mini-batch)

  • Convex Optimization

  • Cost functions, Loss functions

  • Regularization (L1, L2, ElasticNet)

2. Programming and Tools

• Python

  • Core Python (Functions, OOP, Data Structures, Error Handling)

  • Python Libraries: numpy, pandas, matplotlib, seaborn, scikit-learn, statsmodels

  • Data Preprocessing: Data Cleaning, Feature Engineering, Handling Missing Data

  • Libraries for Web Scraping: beautifulsoup, requests

  • Writing Unit Tests in Python: unittest, pytest

• SQL & Databases

  • Advanced SQL queries (JOINS, Subqueries, Aggregations, Window functions)

  • Database design: normalization, indexing, foreign keys

  • Working with SQL databases (MySQL, PostgreSQL, etc.)

  • NoSQL Databases: MongoDB, Cassandra, DynamoDB

  • Data Warehousing concepts (ETL, OLAP, OLTP)

• Big Data Technologies

  • Apache Hadoop, Spark, and Hive

  • Data Processing frameworks: MapReduce, SparkSQL

  • Streaming data processing: Apache Kafka, Apache Flink

  • Distributed computing concepts

3. Data Engineering

• Data Pipelines

  • ETL (Extract, Transform, Load) pipelines

  • Workflow orchestration tools: Apache Airflow, Prefect, Luigi

  • Automation of data processes and batch processing

• Data Storage and Management

  • Data lakes vs Data warehouses (AWS S3, Azure Data Lake)

  • Cloud Platforms: AWS (Redshift, S3), Google Cloud (BigQuery), Azure

  • Data Lakes and ETL Design Patterns

• Data Integration

  • APIs for data collection and integration (RESTful APIs, SOAP)

  • Real-time data ingestion, streaming

  • Data synchronization methods

4. Machine Learning

• Supervised Learning

  • Regression: Linear, Polynomial, Ridge, Lasso

  • Classification: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM)

  • Model Evaluation: Cross-validation, ROC/AUC, Precision, Recall, F1-Score, Confusion Matrix

• Unsupervised Learning

  • Clustering: K-means, DBSCAN, Hierarchical clustering

  • Dimensionality Reduction: PCA, t-SNE, Autoencoders

  • Anomaly Detection

• Reinforcement Learning

  • Markov Decision Processes

  • Q-learning, Deep Q Networks (DQN)

  • Policy Gradient Methods

• Model Deployment and Monitoring

  • Model serialization: Pickle, Joblib

  • Model Deployment platforms: Docker, Kubernetes, AWS Sagemaker, Google AI Platform

  • Model Versioning: DVC (Data Version Control)

  • Model monitoring and drift detection

5. Software Engineering & System Design

• Software Engineering Concepts

  • Data Structures (Queues, Stacks, Hash Tables, Graphs)

  • Algorithms: Sorting, Searching, Graph Algorithms

  • Design Patterns (Singleton, Factory, Observer)

  • Version Control with Git (Branching, Merging)

• System Design

  • Distributed Systems (CAP Theorem, Sharding, Consistency Models)

  • Designing scalable data systems

  • Load balancing and fault tolerance

  • Caching Mechanisms (Redis, Memcached)

  • Microservices Architecture

6. Cloud and DevOps

• Cloud Computing

  • Understanding AWS, GCP, and Azure basics

  • Cloud storage, computing, and networking

  • Serverless Computing (AWS Lambda, Google Cloud Functions)

  • Cloud-native services for machine learning (SageMaker, Vertex AI)

• DevOps for Data Engineering

  • Continuous Integration/Continuous Deployment (CI/CD)

  • Infrastructure as Code (Terraform, CloudFormation)

  • Containerization and Orchestration: Docker, Kubernetes

7. Data Visualization

• Data Visualization Principles

  • Understanding visual perception and effective charting

  • Choosing appropriate visualizations: bar charts, line graphs, heatmaps, histograms, scatter plots

• Visualization Tools

  • Python Libraries: matplotlib, seaborn, plotly, bokeh

  • Interactive Dashboards: dash, streamlit

  • Business Intelligence Tools: Tableau, Power BI

8. Advanced Topics

• Deep Learning (Optional but highly relevant)

  • Neural Networks, Backpropagation

  • Convolutional Neural Networks (CNNs) for image-related tasks

  • Recurrent Neural Networks (RNNs) and LSTMs for sequential data

  • Transfer Learning and Fine-tuning models

• Natural Language Processing (Optional)

  • Text Preprocessing: Tokenization, Lemmatization

  • Text Representation: Bag of Words, TF-IDF, Word2Vec, GloVe

  • Sentiment Analysis, Named Entity Recognition (NER)

9. Soft Skills

• Communication Skills

  • Writing clear documentation

  • Communicating findings to stakeholders

  • Presenting data-driven insights to non-technical teams

• Collaboration and Teamwork

  • Working with cross-functional teams

  • Agile development practices (Scrum, Kanban)

  • Code reviews, mentoring junior engineers

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Final Project

• Build a comprehensive data pipeline that ingests raw data, processes it, performs some machine learning, and outputs actionable insights or predictions. Use cloud services, containerization, and versioning systems.

This syllabus covers a broad spectrum of essential topics. Depending on the specific role and industry you’re aiming for, you can emphasize certain areas, such as cloud technologies or deep learning. If you’re looking for additional resources, I can suggest books, online courses, or tutorials to help you dive deeper into any of these topics.