Python for Data Science: Clear and Complete Guide to Data Science and Analysis with Python
- Length: 203 pages
- Edition: 1
- Language: English
- Publication Date: 2021-08-13
- ISBN-10: B09CN2F35T
- Sales Rank: #0 (See Top 100 Books)
Are you interested in learning data science with Python? Do you want to know what you need to get started? Then you have picked up the right guide.
As more and more data becomes available and accessible, we need to find bigger and better ways of processing it. That’s where data science comes in. It is the future of AI, and that makes it important to understand, if not learn. It’s also important to understand the value data science can add to businesses, and by the end of this guide, you will know what it is, how it works, and how it can use data to extract meaningful, valuable insights.
Here’s what you will learn:
- The difference between data analysis, data science, and machine learning
- The implications and potential of data science
- How to get your data and process it
- What feature selection is
- Data sources
- How to use data visualization with matplotlib
- The difference between supervised, unsupervised, and reinforcement learning
- What simple and multiple linear regression is
- A look at decision trees and random forests
- What classification is, including logistic regression and K-Nearest Neighbors
- Decision tree and random forest classification
- A discussion on clustering
- A deeper look into reinforcement learning and how it works
- A brief look at artificial neural networks
And so much more!
Introduction Chapter 1: Basics of Data Science What is Data Science? Career Scope and Impact of Data Science Using Python Chapter 2: Various Aspects of Data Science Steps Involved in a Data Science Project Defining the Problem Collecting Data from Sources Data Processing Feature Engineering Algorithm Selection Hyperparameter Tuning Data Visualization Interpretation of Results How to Solve the Problems with Python Chapter 3: Python Exploratory Analysis with Pandas Understanding DataFrames and Series Data Set For Practice – A Loan Prediction Problem Beginning with Exploration How to Import the Data Set and Libraries Quick Data Exploration Distribution Analysis Categorical Variable Analysis Using Pandas for Data Munging in Python Checking for Missing Values Treating Extreme Values in a Distribution Chapter 4: Basics of Python for Data Analysis Why Do We Use Python v.3 And Not V.2|? Data Structures of Python Data Analysis in Python Using Pandas Data Science Using Python: Start Instantly Read the Tutorial Carefully Anaconda Jupyter Notebook Open New Notebook Math Calculations Data Importing Importing Dataset Exploration Clean the Dataset Features Develop an Easy Model Using Matplotlib Chapter 5: Metrics in Python along with Demo Changes when a System shows unusual Behavior What Are Metrics And How Many Types Of Metrics Are There? Counters Gauges Histograms or Timers Demo 1 Mean Median Percentile Histogram and Cumulative Histogram Demo 2 Network Applications Long Processes How to Monitor in a Python Application Chapter 6: How to Build a Predictive Model in Python Logistic Regression Decision Tree Data Prediction and Analysis Chapter 7: Income Increment using Data Science with Python Search Options Churn Prediction Churn Categories Data Science and Python: The Essential Relationship Learning Python for Data Science Conclusion Introduction Chapter 1: Data Analysis? Data Science? Or Machine Learning? Machine Learning and Data Analysis Limitations Performance and Accuracy The Potential and the Implications Chapter 2: Get and Process Your Data CSV Files Feature Selection Online Data Internal Data Chapter 3: Data Visualization Importing and Using Matplotlib Supervised and Unsupervised Learning Chapter 4: A Deeper Look at Regression Multiple Linear Regression Decision Tree Regression Random Forest Regression Chapter 5: Digging into Classification Logistic Regression K-Nearest Neighbors Decision Tree Classification Random Forest Classification Chapter 6: A Look at Clustering Clustering Goals and Uses K-Means Clustering Anomaly Detection Chapter 7: What Is Reinforcement Learning? Reinforcement Learning Compared with Supervised and Unsupervised Learning How to Apply Reinforcement Learning Chapter 8: The Artificial Neural Network Imitating the Human Brain Conclusion References
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