Python for Data Science: Learn Data Analysis from Scratch, including Business Analytics Tools and methods for Beginners and Applied Artificial Intelligence
- Length: 133 pages
- Edition: 1
- Language: English
- Publication Date: 2021-10-06
- ISBN-10: B09HXM9839
- Sales Rank: #0 (See Top 100 Books)
Python for Data Science
Learn Data Analysis from Scratch, including Business Analytics Tools and methods for Beginners and Applied Artificial Intelligence
Python For Data Science is written for people who are new to data analysis and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.
- Get started with data science and Python
- Visualize information
- Wrangle data
- Learn from data
The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
CHAPTER 1: INTRODUCTION TO DATA ANALYSIS PYTHON FOR DATA SCIENCE Why Select Python Python vs. R Widespread Application of Data Analysis Clarity TYPES OF DATA ANALYSIS Descriptive Analysis Predictive Analysis Prescriptive Analysis Why Data Analysis Is on the Rise? Summary of the Data Science Process Prerequisite and Reminders Do You Need Some Expertise in Mathematics? CHAPTER 2: PYTHON REVIEW PROPERTIES Getting help Data Analysis vs. Data Science vs. Machine Learning Possibilities Drawbacks of Data Analysis & Machine Learning Accuracy & Performance CHAPTER 3: IMPORTANT PYTHON LIBRARIES INSTALL THE SOFTWARE AND SETTING UP For Windows Numpy Arrays Using IPython as a Shell Web Scraping Using Python Libraries Web Scraping Matplotlib 2nd generation CHAPTER 4: DATA MANIPULATION 6 KEY THINGS YOU NEED TO KNOW ABOUT NUMPY AND PANDAS GETTING STARTED WITH NUMPY Array Indexing Array Slicing Array Concatenation GETTING STARTED WITH PANDAS Importing Data Missing Data Visualize the Data Transformation of Feature CHAPTER 5: DATA AGGREGATION DEFINITION OF DATA FRAME SPLIT-APPLY-COMBINE IMPLEMENTATION HOW TO GROUP DATA FRAMES? CHAPTER 6: DATA VISUALIZATION DATA VISUALIZATION TO THE END-USER MATPLOTLIB Line Chart Histogram Bar Chart VISUALIZATION USING PANDAS The Objective of Visualization THE SIMPLEST METHOD TO COMPLEX VISUALIZATION OF DATA OVERVIEW OF PLOTLY Building Attractive Plots Using Plotly Scatter Plots Box Plots Heat Maps CHAPTER 7: MACHINE LEARNING MACHINE LEARNING ALGORITHMS CLASSIFICATIONS Supervised Learning Unsupervised Learning Reinforcement Learning How to Approach a Problem WHAT IS DEEP LEARNING NEURAL NETWORKS WITH SCIKIT-LEARN THE STRUCTURE OF NEURON BACK PROPAGATION SCIKIT-LEARN THE NEURAL NETWORKS USING TENSORFLOW TensorFlow Preparing the Environment INSTALLING SCIKIT-LEARN Import Scikitlearn INSTALLING TENSORFLOW CHAPTER 8: ARTIFICIAL NEURAL NETWORKS HOW THE BRAIN WORKS CONSTRAINTS AND OPPORTUNITIES LET’S SEE AN EXAMPLE CHAPTER 9: HOW TO USE SCIKIT-LEARN LOADING DATASETS SIMPLE LINEAR REGRESSION Import Libraries Data Preparation Training the Algorithm Predicting Evaluating the Accuracy MULTIPLE LINEAR REGRESSION Data Preparation Training the Algorithm Predicting Evaluating the Accuracy CHAPTER 10: K-NEAREST NEIGHBORS ALGORITHM SPLITTING THE DATASET FEATURE SCALING Training the Algorithm Evaluating the Accuracy K MEANS CLUSTERING Data Preparation Visualizing the Data Creating Clusters CHAPTER 11: CLASSIFICATION LOGISTICS REGRESSION K-NEAREST NEIGHBORS THE DECISION TREE CLASSIFICATION RANDOM FOREST CLASSIFICATION CLUSTERING OBJECTIVES AND FUNCTION OF CLUSTERING K-MEANS CLUSTERING ANOMALY DETECTION CHAPTER 12: ASSOCIATION RULE LEARNING EXPLANATION APRIORI CHAPTER 13: REINFORCEMENT LEARNING WHAT IS REINFORCEMENT LEARNING? COMPARISON WITH SUPERVISED & UNSUPERVISED LEARNING APPLYING REINFORCEMENT LEARNING MASTERING THE BAGGING METHOD HOW TO DO IT CONCLUSION
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