Business Intelligence and Analytics
Master Data Mastery: Transform, Analyze, and Visualize! Dive into the world of Big Data, Governance, Python Analytics, Machine Learning, and AI with Stephanie Powers. Unlock data's power and elevate your expertise in modern analytics and data engineering. Enroll now!
5.0
(4)
31 learners
What you'll learn
- Understand and apply data governance principles to manage data effectively.
- Analyze data types and structures using Python for data engineering tasks.
- Create dashboards and visualizations in Python to present analytical insights.
- Implement machine learning models in Python for classification and prediction tasks.
This course includes
- 56.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 186 lessons • 56.5 hours of video
Mastering Data and Python: From Basics to Advanced Analytics
186 lessons
• 56.5 hours
Mastering Data and Python: From Basics to Advanced Analytics
186 lessons
• 56.5 hours
- Big Data 20:58
- Data Governance 22:34
- Primary Data 27:51
- Secondary Data 14:13
- Data Types 17:59
- Data Dictionary 20:26
- Data Dictionary Practice 28:08
- Relational Database 36:13
- Types of Data Analytics 19:38
- Data Analytics uses the Project Management Process 25:58
- Data Analytics uses the Business Analysis Process 22:59
- Data Analytics uses the Information Systems Process 14:25
- Data Types in Python 15:16
- AI in Analytics 32:45
- Mathematical Operators and Defining Functions in Python 13:10
- Using Lambda in Python for Linear Algebra 16:44
- Logical Operators and Conditional Statements in Python 14:17
- Loops in Python 19:03
- Class in Python 17:37
- Creating a Pipeline in Python 13:55
- Cloud Computing 10:38
- SQL and Joins 12:57
- SQL and Google Cloud 32:39
- Export from Google Cloud 22:53
- Data Engineering - Cleaning 20:43
- Data Engineering - Coding 28:19
- Data Engineering - Manipulate 14:17
- Correlogram 03:16
- Intro to Exploratory Data Analysis (EDA) 24:50
- Intro to Descriptive Analytics 01:13
- Frequency and Mode in Python 24:25
- Median and Mean in Python 05:54
- Range and IQR in Python 09:27
- Standard Deviation and Variance in Python 04:59
- Descriptive Statistics Summary 13:01
- Inferential Statistics in Python - Compare to Value 37:07
- Inferential Statistics in Python - 2 Groups with Different People 24:02
- Inferential Statistics in Python - 2 Groups with Same People (paired) 24:41
- Inferential Statistics in Python - 3+ Groups with Different People 32:13
- Inferential Statistics in Python - 3+ Groups with Same People 36:59
- Inferential Statistics in Python - Association 10:22
- Introduction to Data Visualization 05:33
- Data Visualization in Python - Parts of a Whole - 1 Variable 29:15
- Data Visualization in Python - Parts of a Whole - 2 Variable 15:54
- Data Visualization in Python - Compare 1 or 2 Variables 23:47
- Data Visualization in Python - Compare 3 or More Variables 25:48
- Data Visualization in Python - Time 37:32
- Data Visualization in Python - Variability 31:46
- Data Visualization in Python - Association (relationship) of Variables 12:32
- Data Sources 02:10
- Web Analytics: YouTube Data 24:46
- Web Analytics: Twitter 17:46
- Reality Mining 06:58
- Reality Mining: Location Tracking 12:53
- Reality Mining: Create a Route 10:44
- Reality Mining: Bubble Map 08:53
- Text Mining Introduction 15:19
- Text Mining in Python - Import and Clean 13:03
- Text Mining in Python - Tokenize 15:44
- Text Mining in Python - Word Count 09:27
- Text Mining in Python - Word Cloud 13:54
- Text Mining in Python - Open Ended Survey Questions 13:03
- Text Mining in Python - Count Vectorizer 12:56
- Text Mining in Python - Ngrams 22:39
- Text Mining in Python - Zipfs law 16:25
- Text Mining in Python - Word Search 09:13
- Text Mining in Python - Word Tree 07:15
- Why You Need Tell Stories with Data 20:15
- How to tell stories with data 23:36
- Introduction to Dashboards 06:36
- Sankey Diagrams with Holoview and Plotly 14:38
- Gauge Charts in Python 06:32
- Dashboard in Power BI 26:04
- Using Plotly and Dash to Build a Dashboard 23:24
- Creating a Dashboard with Streamlit 01:04:10
- Intro to Diagnostic Analytics 02:50
- Intro to Supervised Learning 01:30
- Introduction Classification Models 10:31
- Classification Model in Python - K Nearest Neighbors (KNN) 41:52
- Classification Model in Python - General Linear Model (GLM) 27:36
- Classification Model in Python - Support Vector Machine (SVM) 27:54
- Classification Model in Python - Naive Bayes (NB) 29:36
- Classification Model in Python - Decision Tree 36:25
- Classification Model in Python - Random Forest 16:50
- Classification Model in Python - Gradient Boosting Machine (GBM) 17:43
- Classification Models in Python - Tuning Hyperparameters 33:01
- Classification Model Comparison 10:23
- Classification Models in Python - Use Cases 07:08
- Image Recognition Using KNN 13:05
- Denoise Images using KNN 12:09
- Recommendation Engine - Collaborative Filtering 26:12
- Webscraping 21:19
- Sentiment Analysis without Neural Networks 17:22
- Webscraping Static v Dynamic Websites 05:49
- Network (Graph) Theory 15:51
- Network Graphs 14:35
- Network Theory - Mapping the Spread of Information or Infection 07:49
- Network Graphs - Measuring Centrality 15:53
- Intro Enterprise Analytics 07:06
- Creating Organizational Charts in Python 14:52
- Organizational Hierarchy 13:42
- Voronoi Polygon using Python 20:39
- Process Flow in Visio 10:46
- Capacity Planning in Python 17:45
- Impact of Bottlenecks on Capacity Planning (analysis in Python) 10:47
- Performing Breakeven Analysis in Python 19:41
- Histograms in Python 08:32
- Pareto Chart in Python 10:34
- Control Chart in Python 17:35
- Intro to Predictive Analytics 01:32
- Demand Forecasting 14:27
- Forecasting Process 16:02
- Short Term Forecasting in Python 38:11
- Linear Forecasting in Python 15:10
- Trend Forecasting Using Polynomials in Python 23:34
- Forecasting using Logarithmic and Exponential Functions in Python 15:42
- Calculating Measurement Error using Python 14:54
- Creating Associative Models in Python 10:06
- ARIMA 20:54
- SARIMA 21:45
- Intro to Unsupervised Learning 01:02
- Intro to Cluster Analysis 10:08
- Cluster Analysis in Python - KMeans and Elbow Method 22:23
- Cluster Analysis in Python - Silhouette, Calinski Harabasz, and Davies Bouldin for KMeans 15:16
- Cluster Analysis in Python - Hierarchical Clustering (Agglomerative Clustering) 26:36
- Cluster Analysis in Python - Dendrograms 19:01
- Cluster Analysis in Python - How to use Cluster Analysis for Business Decisions 27:47
- Cluster Analysis in Python - DBSCAN 18:08
- Dimension Reduction in Python - Principal Component Analysis (PCA) 29:45
- Dimension Reduction in Python - Incremental PCA 20:31
- Dimension Reduction - Sparse and Kernel PCA 22:00
- Dimension Reduction in Python - Singular Value Decomposition (SVD) Latent Semantic Analysis (LSA) 25:12
- Dimension Reduction in Python - ISOMAP, LLE, and TSNE 18:50
- Dimension Reduction in Python - Nonlinear Methods 23:15
- Outlier Detection Models 15:38
- Benford's Law for Detecting Financial Fraud 13:42
- Outlier Detection Using Prophet Medium Term Forecasting Model 19:38
- Marketing Analytics 20:49
- Association Rule Analysis 21:11
- Intro to Prescriptive Analytics 02:11
- Linear Programming in Python 17:36
- Integer Programming in Python 15:25
- Integer Programming in Python Practice 08:56
- Binary Integer Programming 35:06
- Decision Tree Intro 03:40
- Creating Decision Trees in Schemdraw 09:19
- Decision Trees and Sensitivity Analysis (Quantity) 25:27
- Decision Trees and Sensitivity Analysis (Price) 09:46
- Decision Trees and Sensitivity Analysis (Price and Probability) 11:12
- Decision Tree with Imperfect Information (Bayes Theorem) 39:16
- Introduction to Monte Carlo Simulations 08:15
- Distributions in Python for Monte Carlo Simulations 16:08
- Monte Carlo Simulation Example 17:49
- PERT with Variability 36:35
- Decision Making with Uncertainty 14:49
- Queuing Models - Single Server 26:49
- Queuing Models - Multiple Servers 16:50
- Queuing Models - Number of Servers 12:19
- Queuing Models - Single Server with Finite Population 10:31
- Queuing Models - Single Server with Arbitrary Service Time 08:56
- Queuing Models - Single Server with Constant Service Time and Multiple Server with No Waitlist 11:42
- Logistic Regression in Python (smf and sklearn) 32:27
- Missingness in Data 11:40
- Poisson Regression in Python (smf and sklearn) 28:07
- Linear Regression in Python (smf and sklearn) 32:27
- Linear Regression Assumptions 09:32
- Ridge Regression in Python 28:09
- Lasso Regression in Python 20:58
- Intro to Reinforced Learning 01:45
- Intro to Reinforced Learning 12:56
- Reinforced Learning: Brute Force 11:10
- Reinforced Learning: Q Learning 16:32
- Reinforced Learning: SARSA 12:37
- Intro to Neural Networks 13:02
- Designing a Neural Network 13:05
- Layers of Neural Networks 13:41
- MLP Classifier 20:41
- MLP Regressor 22:13
- Convolution Neural Network (CNN) for Image Classification 25:33
- CNN for Sentiment 48:19
- RNN for Time Series Forecasts 43:24
- Creating Entity Relationship Diagrams using Draw.io 09:44
- Data Engineering Part 1 17:42
- Data Engineering Part 2 18:44
- Dealing with Missing Data Part 1 28:12
- Dealing with Missing Data Part 2 11:14
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