Data science from scratch the 1 data science guide for everything a data scientist needs to know Python linear algebra statistics c neural networks and decision trees 1st Edition by Steven Cooper ISBN 3903331163  978-3903331167

Data science from scratch the 1 data science guide for everything a data scientist needs to know Python linear algebra statistics c neural networks and decision trees 1st Edition by Steven Cooper – Ebook PDF Instant Download/Delivery: 3903331163  978-3903331167
Full download Data science from scratch the 1 data science guide for everything a data scientist needs to know Python linear algebra statistics c neural networks and decision trees 1st edition after payment

Product details: 

ISBN 10: 3903331163

ISBN 13:  978-3903331167

Author:  Steven  Steven Cooper

The Power of Data Science!

If you are looking to start a new career that is in high demand, then you need to continue reading.

Data scientists are changing the way big data is used in different institutions.

Big data is everywhere, but without the right person to interpret it, it means nothing.

So where do business find these people to help change their business?

You could be that person!

It has become a universal truth that businesses are full of data.

With the use of big data, the US healthcare could reduce their health-care spending by $300 billion to $450 billion.

It can easily be seen that the value of big data lies in the analysis and processing of that data, and that’s where data science comes in.

Data Science from Scratch includes:

• In-depth information about what data science is and why it is important.
• The prerequisites you will need to get started in data science.
• What it means to be a data scientist.
• The roles that hacking and coding play in data science.
• The different coding languages that can be used in data science.
• Why python is so important.
• How to use linear algebra and statistics.
• The different applications for data science.
• How to work with the data through munging, cleaning, and more.

And much more!

The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow.

As businesses and the internet change, so will data science. This means it’s important to be flexible.

When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in?

If you want to get started in a new, ever-growing, career, don’t wait any longer. Buy this book today.

Data science from scratch the 1 data science guide for everything a data scientist needs to know Python linear algebra statistics c neural networks and decision trees 1st Table of contents: 

Preface

• Introduction to Data Science
• Why Learn Data Science from Scratch?
• How to Use This Book

Chapter 1: Introduction to Data Science

• What is Data Science?
• The Role of a Data Scientist
• Tools and Skills Required
• The Data Science Workflow

Chapter 2: Python for Data Science

• Introduction to Python Programming
• Setting Up Your Python Environment
• Working with Python Libraries (NumPy, Pandas, Matplotlib)
• Basic Python Syntax and Data Structures
• Writing Your First Python Scripts for Data Analysis

Chapter 3: Linear Algebra for Data Science

• Introduction to Linear Algebra
• Vectors, Matrices, and Their Operations
• Matrix Multiplication and Inverses
• Eigenvalues and Eigenvectors
• Linear Algebra in Data Science: Applications to Machine Learning

Chapter 4: Statistics Essentials

• Descriptive Statistics: Mean, Median, Mode
• Probability Theory and Distributions
• Hypothesis Testing and Confidence Intervals
• Sampling Techniques and Their Importance
• Statistical Inference for Data Analysis

Chapter 5: Data Exploration and Visualization

• Understanding Data Exploration
• Cleaning and Preprocessing Data
• Data Visualization with Matplotlib and Seaborn
• Interpreting Graphs and Charts
• Exploratory Data Analysis (EDA) Best Practices

Chapter 6: Supervised Learning Basics

• Introduction to Supervised Learning
• The Concepts of Training and Testing Data
• Regression and Classification
• Evaluating Model Performance: Accuracy, Precision, Recall, and F1-Score
• Practical Examples of Supervised Learning Algorithms

Chapter 7: Decision Trees and Random Forests

• The Theory Behind Decision Trees
• Building a Decision Tree Model
• Overfitting and Pruning
• Random Forests: Aggregating Multiple Trees
• Practical Applications of Decision Trees

Chapter 8: Neural Networks and Deep Learning

• Introduction to Neural Networks
• Understanding Perceptrons and Layers
• Training Neural Networks with Backpropagation
• Deep Learning and Deep Neural Networks (DNNs)
• Applications of Neural Networks in Data Science

Chapter 9: Unsupervised Learning and Clustering

• What is Unsupervised Learning?
• K-Means Clustering
• Hierarchical Clustering and DBSCAN
• Dimensionality Reduction (PCA and t-SNE)
• Applications of Unsupervised Learning

Chapter 10: Natural Language Processing (NLP)

• Introduction to NLP and Text Data
• Text Preprocessing and Tokenization
• Word Embeddings and Vectorization
• Sentiment Analysis and Classification
• NLP Algorithms and Their Applications

Chapter 11: Model Evaluation and Selection

• Cross-Validation Techniques
• Hyperparameter Tuning and Grid Search
• Bias-Variance Tradeoff
• Choosing the Right Model for the Task
• Model Evaluation Metrics for Regression and Classification

Chapter 12: Deployment and Productionizing Models

• The Process of Deploying Data Science Models
• Tools and Platforms for Model Deployment
• Monitoring and Updating Deployed Models
• Real-World Challenges in Production

Chapter 13: Ethics and Responsibility in Data Science

• Data Privacy and Security
• Ethical Issues in Data Science
• Bias in Algorithms and Fairness in AI
• Social Impacts of Data Science

Chapter 14: The Future of Data Science

• Emerging Trends and Technologies in Data Science
• Artificial Intelligence and Machine Learning Evolution
• The Role of Automation in Data Science
• Building a Career in Data Science

---

Appendices

• A. Python Libraries for Data Science
• B. Further Reading and Resources
• C. Sample Projects and Datasets
• D. Glossary of Key Terms

References

Index

People also search for Data science from scratch the 1 data science guide for everything a data scientist needs to know Python linear algebra statistics c neural networks and decision trees 1st:

data science from scratch first principles with python pdf

data science from scratch second edition pdf

data science from scratch first principles with python 2nd edition

data science from scratch 2nd edition

a data scientist

 

 

Tags:

Steven Cooper,Data science,Python linear,coding applications