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Book Overview & Buying
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Table Of Contents
Data Science, Analytics, and AI for Business and the Real World™
By :
Data Science, Analytics, and AI for Business and the Real World™
By:
Overview of this book
Right now, despite the Covid-19 economic contraction, traditional businesses are hiring data scientists in droves! Therefore, data scientist has become the top job in the U.S. for the last four years running.
However, data science has a difficult learning curve. This course seeks to fill all those gaps and has a comprehensive syllabus that tackles all the major components of data science knowledge.
You will be using data science to solve common business problems throughout this course. You will start with the basics of Python, Pandas, Scikit-learn, NumPy, Keras, Prophet, statsmod, SciPy, and more. You will learn statistics and probability for data science in detail. Then, you will learn visualization theory for data science and analytics using Seaborn, Matplotlib, and Plotly.
You will look at dashboard design using Google Data Studio along with machine learning and deep learning theory/tools.
Then, you will be solving problems using predictive modeling, classification, and deep learning. After this, you will move your focus to data analysis and statistical case studies, data science in marketing, and data science in retail.
Finally, you will see deployment to the cloud using Heroku to build a machine learning API.
By the end of this course, you will learn all the major components of data science and gain the confidence to enter the world of data science.
All the code files and the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Data-Science-Analytics-AI-for-Business-the-Real-World-
Table of Contents (49 chapters)
Introduction to the Course
Set Up (Google Colab) and Download Code Files
Introduction to Python
Pandas
Statistics and Visualizations
Probability Theory
Hypothesis Testing
A/B Testing - A Worked Example
Data Dashboards - Google Data Studio
Machine Learning
Deep Learning
Unsupervised Learning - Clustering
Dimensionality Reduction
Recommendation Systems
Natural Language Processing
Big Data
Predicting the US 2020 Election
Predicting Diabetes Cases
Market Basket Analysis
Predicting the World Cup Winner (Soccer/Football)
Covid-19 Data Analysis and Flourish Bar Chart Race Visualization
Analyzing Olympic Winners
Is Home Advantage Real in Soccer and Basketball
IPL Cricket Data Analysis
Streaming Services (Netflix, Hulu, Disney Plus, and Amazon Prime)
Micro Brewery and Pub Data Analysis
Pizza Restaurant Data Analysis
Supply Chain Data Analysis
Indian Election Result Analysis
Africa Economic Crisis Data Analysis
Predicting Which Employees May Quit
Figuring Out Which Customers May Leave
Who to Target for Donations?
Predicting Insurance Premiums
Predicting Airbnb Prices
Detecting Credit Card Fraud
Analyzing Conversion Rates in Marketing Campaigns
Predicting Advertising Engagement
Product Sales Analysis
Determining Your Most Valuable Customers
Customer Clustering (K-Means, Hierarchical) - Train Passenger
Build a Product Recommendation System
Deep Learning Recommendation System
Predicting Brent Oil Prices
Detecting Sentiment in Tweets
Spam or Ham Detection
Explore Data with PySpark and Titanic Survival Prediction
Newspaper Headline Classification Using PySpark