Data Science Quick Start
An Introductory Crash Course for Technical Professionals
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Beschreibung
A fast, practical path into the data science life cycle for technical professionals.
Starting with data collection and management, you learn hands‑on data cleaning and wrangling, exploratory data analysis and visualization, and statistical modeling and inference that lead naturally into supervised and unsupervised learning. Clear guidance on model evaluation metrics, feature engineering, and time series forecasting helps you match methods to workloads with reasons grounded in practice.
The book then extends into deep learning and natural language processing, covering neural network foundations alongside applied text workflows such as sentiment analysis, named entity recognition, topic modeling, and transformer techniques. Cloud‑oriented deployment concepts, reproducible workflows, and governance are treated vendor‑neutral. Dedicated coverage of data ethics, privacy, fairness, and accountability ensures responsible practice. Business analytics use cases, tool fundamentals, portfolio‑building advice, and future trends round out a graduate‑level yet accessible crash course aimed at quick adoption and durable skills.
What You Will Learn
- Execute the complete data science life cycle from collection to deployment
- Conduct exploratory data analysis and create effective visualizations
- Apply statistical modeling and inference to real analytical problems
- Build supervised and unsupervised learning workflows with rigorous evaluation
- Design and train deep learning models for vision and sequence data
- Implement NLP pipelines including sentiment analysis, NER, topic modeling, and transformer methods
- Develop time series forecasting with seasonality and validation strategies
- Apply reproducible workflows and deployment choices for cloud environments
- Integrate ethical AI, privacy, fairness, and governance into projects
A fast, practical path into the data science life cycle for technical professionals.
Starting with data collection and management, you learn hands‑on data cleaning and wrangling, exploratory data analysis and visualization, and statistical modeling and inference that lead naturally into supervised and unsupervised learning. Clear guidance on model evaluation metrics, feature engineering, and time series forecasting helps you match methods to workloads with reasons grounded in practice.
The book then extends into deep learning and natural language processing, covering neural network foundations alongside applied text workflows such as sentiment analysis, named entity recognition, topic modeling, and transformer techniques. Cloud‑oriented deployment concepts, reproducible workflows, and governance are treated vendor‑neutral. Dedicated coverage of data ethics, privacy, fairness, and accountability ensures responsible practice. Business analytics use cases, tool fundamentals, portfolio‑building advice, and future trends round out a graduate‑level yet accessible crash course aimed at quick adoption and durable skills.
What You Will Learn
- Execute the complete data science life cycle from collection to deployment
- Conduct exploratory data analysis and create effective visualizations
- Apply statistical modeling and inference to real analytical problems
- Build supervised and unsupervised learning workflows with rigorous evaluation
- Design and train deep learning models for vision and sequence data
- Implement NLP pipelines including sentiment analysis, NER, topic modeling, and transformer methods
- Develop time series forecasting with seasonality and validation strategies
- Apply reproducible workflows and deployment choices for cloud environments
- Integrate ethical AI, privacy, fairness, and governance into projects
Who This Book Is For
Technical professionals with basic coding and quantitative fundamentals who need a concise, hands-on ramp into the data science life cycle.
Concise crash course for technical professionals across the complete data science life cycle Hands-on workflows from data cleaning and EDA to ML, deep learning, NLP, and time series Responsible practice and deployment with ethical AI governance, reproducible pipelines and cloud readiness
Autor*in
Chaitanya Krishna Kasaraneni
Themen in »Data Science Quick Start«
model evaluation metrics cross validation strategies supervised learning techniques unsupervised learning clustering feature engineering methods dimensionality reduction pca convolutional neural networks recurrent neural networks lstm transformer nlp techniques sentiment analysis topic modeling classification text preprocessing workflows exploratory data analysis visualization design principles time series forecasting