Transforming Business with Data Science, Analytics, and AI Practice Exam

Transforming Business with Data Science, Analytics, and AI Practice Exam

About the Transforming Business with Data Science, Analytics, and AI Exam

Data science, analytics, and AI are transforming businesses by enabling data-driven decision-making, automating processes, and uncovering valuable insights. With advanced analytics, companies can optimize operations, predict customer behavior, and create personalized experiences. AI and machine learning further enhance efficiency by automating routine tasks and enabling smarter forecasting and problem-solving. Together, these technologies help businesses stay competitive, improve performance, and drive innovation across various industries, from healthcare and finance to retail and manufacturing.

Skills Required

• Basic programming skills in Python or R for data analysis and machine learning.
• Understanding of mathematics and statistics, including probability, linear algebra, and basic statistical concepts.
• Knowledge of data manipulation and analysis tools like Pandas, NumPy, and SQL.
• Basic skills in data visualization using tools like Matplotlib, Seaborn, or Tableau.
• Familiarity with basic machine learning algorithms such as regression, classification, and clustering.
• Exposure to database management (SQL and NoSQL) and big data tools like Hadoop or Spark.
• Problem-solving mindset to approach data challenges analytically and creatively.

Knowledge Gained

In this course you will gain:

• Understanding of core data science concepts, including data cleaning, exploration, and visualization.
• Proficiency in programming languages like Python or R for data analysis and machine learning tasks.
• Ability to work with data manipulation libraries such as Pandas, NumPy, and SQL for handling datasets.
• Skills in applying statistical analysis and machine learning algorithms to extract insights from data.
• Knowledge of data visualization techniques using tools like Matplotlib, Seaborn, or Tableau.
• Familiarity with basic machine learning models, including regression, classification, and clustering.
• Experience with databases and big data technologies like Hadoop or Spark for managing large-scale datasets.
• Strong problem-solving and analytical skills to approach real-world data challenges across different industries.

Who should take the Exam?

• Aspiring data scientists looking to build a career in data analysis and machine learning.
• Machine learning engineers who want to strengthen their understanding of data science fundamentals.
• Data analysts aiming to transition into data science or enhance their skills with machine learning knowledge.
• Software developers interested in expanding their skill set into data science and AI.
• Business analysts seeking to leverage data science and analytics for better decision-making.
• AI/ML enthusiasts eager to validate their skills through a formal exam.
• Career changers looking to enter the data science field and gain a strong foundational knowledge.

Course Outline

Introduction 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

• Downloading and Running Your Code

Introduction to Python

• Why Use Python for Data Science?
• Python Introduction - Part 1 - Variables
• Python - Variables (Lists and Dictionaries)
• Python - Conditional Statements
• Python - Loops
• Python - Functions
• Python - Classes

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

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 Besselâ€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

Probability Theory

• Introduction to Probability
• Estimating Probability
• Probability - Addition Rule
• Probability - Permutations and Combinations
• Bayes Theorem

Hypothesis Testing

• Introduction to Hypothesis Testing
• Statistical Significance
• Hypothesis Testing - P Value
• Hypothesis Testing - Pearson Correlation

A/B Testing - A Worked Example

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• A/B Test Result Analysis
• A/B Testing a Worked Real-Life Example - Designing an A/B Test
• Statistical Power and Significance
• Analysis of A/B Test Results

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

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

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

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)

Dimensionality Reduction

• Principal Component Analysis
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• PCA and t-SNE in Python with Visualization Comparisons

Recommendation Systems

• Introduction to Recommendation Engines
• Before Recommending, How Do We Rate or Review Items?
• User Collaborative Filtering and Item/Content-Based Filtering
• The Netflix Prize and Matrix Factorization and Deep Learning as Latent-Factor Me

Natural Language Processing

• Introduction to Natural Language Processing
• Modeling Language - The Bag of Words Model
• Normalization, Stop Word Removal, Lemmatizing/Stemming
• TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency)
• Word2Vec - Efficient Estimation of Word Representations in Vector Space

Big Data

• Introduction to Big Data
• Challenges in Big Data
• Hadoop, MapReduce, and Spark
• Introduction to PySpark
• RDDs, Transformations, Actions, Lineage Graphs, and Jobs

Predicting the US 2020 Election

• Understanding Polling Data
• Cleaning and Exploring Our Dataset
• Data Wrangling Our Dataset
• Understanding the US Electoral System
• Visualizing Our Polling Data
• Statistical Analysis of Polling Data
• Polling Simulations
• Polling Simulation Result Analysis
• Visualizing Our results on a US Map

Predicting Diabetes Cases

• Understanding and Preparing Our Healthcare Data
• First Attempt - Trying a Naive Model
• Trying Different Models and Comparing the Results

Market Basket Analysis

• Understanding Our Dataset
• Data Preparation
• Visualizing Our Frequent Sets

Predicting the World Cup Winner (Soccer/Football)

• Understanding and Preparing Our Soccer Datasets - Part 1
• Understanding and Preparing Our Soccer Datasets - Part 2
• Predicting Game Outcomes with Our Model
• Simulating the World Cup Outcome with Our Model

Covid-19 Data Analysis and Flourish Bar Chart Race Visualization

• Understanding Our Covid-19 Data
• Analysis of the Most Recent Data
• World Visualizations
• Analyzing Confirmed Cases in Each Country
• Mapping Covid-19 Cases
• Animating Our Maps
• Comparing Countries and Continents
• Flourish Bar Chart Race - 1
• Flourish Bar Chart Race - 2

Analyzing Olympic Winners

• Understanding Our Olympic Dataset
• Getting the Medals Per Country
• Analyzing the Winter Olympic Data and Viewing Medals Won Over Time

Is Home Advantage Real in Soccer and Basketball

• Understanding Our Dataset and EDA
• Goal Difference Ratios Home Versus Away
• How Home Advantage Have Evolved Over Time

IPL Cricket Data Analysis

• Loading and Understanding Our Cricket Dataset
• Man of the Match and Stadium Analysis
• Do Toss Winners Win More? And Team Versus Team Comparisons

Streaming Services (Netflix, Hulu, Disney Plus, and Amazon Prime)

• Understanding Our Dataset
• EDA and Visualizations
• Best Movies Per Genre Platform Comparisons

Micro Brewery and Pub Data Analysis

• EDA, Visualizations, and Map

Pizza Restaurant Data Analysis

• EDA and Visualizations
• Analysis Per State
• Pizza Maps

Supply Chain Data Analysis

• Understanding Our Dataset
• Visualizations and EDA
• More Visualizations

Indian Election Result Analysis

• Introduction
• Visualizations of Election Results
• Visualizing Gender Turnout

Africa Economic Crisis Data Analysis

• Economic Dataset Understanding
• Visualizations and Correlations

Predicting Which Employees May Quit

• Figuring Out Which Employees May Quit - Understanding the Problem and EDA
• Data Cleaning and Preparation
• Machine Learning Modeling + Deep Learning

Figuring Out Which Customers May Leave

• Understanding the Problem
• Exploratory Data Analysis and Visualizations
• Data Pre-Processing
• Machine Learning Modeling + Deep Learning

Who to Target for Donations?

• Understanding the Problem
• Exploratory Data Analysis and Visualizations
• Preparing Our Dataset for Machine Learning
• Modeling Using Grid Search to Find the best parameters

Predicting Insurance Premiums

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• Data Preparation and Machine Learning Modeling

Predicting Airbnb Prices

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• Machine Learning Modeling
• Using Our Model for Value Estimation for New Clients

Detecting Credit Card Fraud

• Understanding Our Dataset
• Exploratory Analysis
• Feature Extraction
• Creating and Validating Our Model

Analyzing Conversion Rates in Marketing Campaigns

• Exploratory Analysis of Understanding Marketing Conversion Rates

Predicting Advertising Engagement

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• Data Preparation and Machine Learning Modeling

Product Sales Analysis

• Problem and Plan of Attack
• Sales and Revenue Analysis
• Analysis Per Country, Repeat Customers, and Items

Determining Your Most Valuable Customers

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• Customer Lifetime Value Modeling

Customer Clustering (K-Means, Hierarchical) - Train Passenger

• Data Exploration and Description
• Simple Exploratory Data Analysis and Visualizations
• Feature Engineering
• K-Means Clustering of Customer Data
• Cluster Analysis

Build a Product Recommendation System

• Dataset Description and Data Cleaning
• Making a Customer-Item Matrix
• User-User Matrix - Getting Recommended Items
• Item-Item Collaborative Filtering - Finding the Most Similar Items

Deep Learning Recommendation System

• Understanding Our Wikipedia Movie Dataset
• Creating Our Dataset
• Deep Learning Embeddings and Training
• Getting Recommendations Based on Movie Similarity

Predicting Brent Oil Prices

• Understanding Our Dataset and Its Time Series Nature
• Creating Our Prediction Model
• Making Future Predictions

Detecting Sentiment in Tweets

• Understanding Our Dataset and Word Clouds
• Visualizations and Feature Extraction
• Training Our Model

Spam or Ham Detection

• Loading and Understanding Our Spam/Ham Dataset
• Training Our Spam Detector

Explore Data with PySpark and Titanic Survival Prediction

• Exploratory Analysis of Our Titanic Dataset
• Transformation Operations
• Machine Learning with PySpark

Newspaper Headline Classification Using PySpark

• Loading and Understanding Our Dataset
• Building Our Model with PySpark

Deployment into Production

• Introduction to Production Deployment Systems
• Creating the Model
• Introduction to Flask
• About Our WebApp
• Deploying Our WebApp on Heroku