Data Science Quick Start

An Introductory Crash Course for Technical Professionals
1. Auflage 2027
ISBN: 979-8-8688-3307-6
Verlag: APRESS L.P.

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Apress Pocket Guides

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Kasaraneni, Chaitanya Krishna

Thalapaneni, Sarmista

Pulipaka, Srikar Kashyap

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung

Inhaltsverzeichnis

Chapter 1: Introduction to Data Science.- Chapter 2: Data Collection and Management.- Chapter 3: Exploratory Data Analysis (EDA).- Chapter 4: Data Wrangling and Transformation.- Chapter 5: Statistical Foundations of Data Science.- Chapter 6: Machine Learning Concepts.- Chapter 7: Supervised Learning Techniques.- Chapter 8: Unsupervised Learning Techniques.- Chapter 9: Deep Learning and Neural Networks.- Chapter 10: Natural Language Processing (NLP).- Chapter 11: Data Visualization Techniques.- Chapter 12: Big Data and Its Role in Data Science.- Chapter 13: Cloud Computing for Data Science.- Chapter 14: Data Science for Business Analytics.- Chapter 15: Time Series Analysis.- Chapter 16: Data Ethics and Governance.- Chapter 17: Data Science Tools and Technologies.- Chapter 18: The Role of AI in Data Science.- Chapter 19: Building a Data Science Portfolio.- Chapter 20: The Future of Data Science.

Über Autor(innen)

Chaitanya Krishna Kasaraneni is a software engineer specializing in data engineering, cloud infrastructure, and machine learning for healthcare and life sciences. He develops data platforms and ML systems for clinical and genomic applications and contributes to open-source bioinformatics and machine learning tools. He holds an MS in Computer Engineering from San Jose State University and is an IEEE member.

Sarmista Thalapaneni is a Data Analytics Engineer with experience building cloud-based data pipelines, analytics platforms, and machine learning infrastructure across healthcare and insurance domains. Her work focuses on data engineering, AI/ML operations, and enterprise analytics. She holds an MS in Management Information Systems from Northern Illinois University.

Srikar Kashyap Pulipaka is a machine learning engineer and researcher specializing in applied AI, data science, and enterprise-scale machine learning systems. He combines industry experience with academic research in natural language processing and machine learning. He holds a Master of Science in Computer Science from Indiana University Bloomington.