Data Science, Analytics, and AI for Business and the Real World™ [Video]
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-
Free ChapterIntroduction to the Course
- The Data Science Hype
- About Our Case Studies
- Why Data is the New Oil
- Defining Business Problems for Analytic Thinking and Data-Driven Decision Making
- 10 Data Science Projects Every Business Should Do!
- How Deep Learning is Changing Everything
- The Career Paths of a Data Scientist
- The Data Science Approach to Problems
-
Set Up (Google Colab) and Download Code Files
-
Introduction to Python
-
Pandas
- Introduction to Pandas
- Pandas 1 - Data Series
- Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty Cells
- Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty Cells, and Filtering
- Pandas 3A - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations
- Pandas 3B - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations
- Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggregate Functions
- Feature Engineer, Lambda, and Apply
- Concatenating, Merging, and Joining
- Time Series Data
- Advanced Operations - Iterows, Vectorization, and NumPy
- Advanced Operations - Map, Filter, Apply
- Advanced Operations - Parallel Processing
- Map Visualizations with Plotly - Cloropeths from Scratch - USA and World
- Map Visualizations with Plotly - Heatmaps, Scatter Plots, and Lines
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Statistics and Visualizations
- Introduction to Statistics
- Descriptive Statistics - Why Statistical Knowledge is So Important
- Descriptive Statistics 1 - Exploratory Data Analysis (EDA) and Visualizations
- Descriptive Statistics 2 - Exploratory Data Analysis (EDA) and Visualizations
- Sampling, Averages, and Variance, and How to Lie and Mislead with Statistics
- Sampling - Sample Sizes and Confidence Intervals - What Can You Trust?
- Types of Variables - Quantitative and Qualitative
- Frequency Distributions
- Frequency Distributions Shapes
- Analyzing Frequency Distributions - What is the Best Type of Wine? Red or White?
- Mean, Mode, and Median - Not as Simple as You Think
- Variance, Standard Deviation, and Bessel’s Correction
- Covariance and Correlation - Do Amazon and Google Know You Better Than Anyone Else?
- Lying with Correlations - Divorce Rates in Maine Caused by Margarine Consumption
- The Normal Distribution and the Central Limit Theorem
- Z-Scores
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Probability Theory
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Hypothesis Testing
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A/B Testing - A Worked Example
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Data Dashboards - Google Data Studio
- Intro to Google Data Studio
- Opening Google Data Studio and Uploading Data
- Your First Dashboard Part 1
- Your First Dashboard Part 2
- Creating New Fields to Our data
- Pivot Tables - Total Profit
- Adding Filters to Tables
- Scorecard KPI Visualizations
- Scorecards with Time Comparison
- Bar Charts (Horizontal, Vertical, and Stacked)
- Line Charts
- Pie Charts, Donut Charts, and Tree Maps
- Time Series and Comparative Time Series Plots
- Scatter Plots
- Geographic Plots
- Bullet and Line Area Plots
- Sharing and Final Conclusions
- Our Executive Sales Dashboard
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Machine Learning
- Introduction to Machine Learning
- How Machine Learning enables Computers to Learn
- What is a Machine Learning Model?
- Types of Machine Learning
- Linear Regression - Introduction to Cost Functions and Gradient Descent
- Linear Regressions in Python from Scratch and Using Sklearn
- Polynomial and Multivariate Linear Regression
- Logistic Regression
- Support Vector Machines (SVMs)
- Decision Trees and Random Forests, and the Gini Index
- K-Nearest Neighbors (KNN)
- Assessing Performance - Confusion Matrix, Precision, and Recall
- Understanding the ROC and AUC Curve
- What Makes a Good Model? Regularization, Overfitting, Generalization, and Outliers
- Introduction to Neural Networks
- Types of Deep Learning Algorithms CNNs, RNNs, and LSTMs
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Deep Learning
- Neural Networks Chapter Overview
- Machine Learning Overview
- Neural Networks Explained
- Forward Propagation
- Activation Functions
- Training Part 1 - Loss Functions
- Training Part 2 - Backpropagation and Gradient Descent
- Backpropagation and Learning Rates - A Worked Example
- Regularization, Overfitting, Generalization, and Test Datasets
- Epochs, Iterations, and Batch Sizes
- Measuring Performance and the Confusion Matrix
- Review and Best Practices
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Unsupervised Learning - Clustering
- Introduction to Unsupervised Learning
- K-Means Clustering
- Choosing K
- K-Means - Elbow and Silhouette Method
- Agglomerative Hierarchical Clustering
- Mean Shift Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- DBSCAN in Python
- Expectation-Maximization (EM) Clustering Using Gaussian Mixture Models (GMM)
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Dimensionality Reduction
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Recommendation Systems
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Natural Language Processing
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Big Data
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Predicting the US 2020 Election
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Predicting Diabetes Cases
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Market Basket Analysis
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Predicting the World Cup Winner (Soccer/Football)
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Covid-19 Data Analysis and Flourish Bar Chart Race Visualization
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Analyzing Olympic Winners
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Is Home Advantage Real in Soccer and Basketball
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IPL Cricket Data Analysis
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Streaming Services (Netflix, Hulu, Disney Plus, and Amazon Prime)
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Micro Brewery and Pub Data Analysis
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Pizza Restaurant Data Analysis
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Supply Chain Data Analysis
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Indian Election Result Analysis
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Africa Economic Crisis Data Analysis
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Predicting Which Employees May Quit
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Figuring Out Which Customers May Leave
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Who to Target for Donations?
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Predicting Insurance Premiums
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Predicting Airbnb Prices
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Detecting Credit Card Fraud
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Analyzing Conversion Rates in Marketing Campaigns
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Predicting Advertising Engagement
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Product Sales Analysis
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Determining Your Most Valuable Customers
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Customer Clustering (K-Means, Hierarchical) - Train Passenger
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Build a Product Recommendation System
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Deep Learning Recommendation System
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Predicting Brent Oil Prices
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Detecting Sentiment in Tweets
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Spam or Ham Detection
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Explore Data with PySpark and Titanic Survival Prediction
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Newspaper Headline Classification Using PySpark
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Deployment into Production
About this
video
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-
- Publication date:
- March 2022
- Publisher
- Packt
- Duration
- 30 hours 50 minutes
- ISBN
- 9781803240848