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Mastering Data and Python: From Basics to Advanced 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 (22)

240 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

▶

Big Data20:57
Data Governance22:33
Primary Data27:51
Secondary Data14:12
Data Types17:58
Data Dictionary20:25
Data Dictionary Practice28:07
Relational Database36:12
Types of Data Analytics19:38
Data Analytics uses the Project Management Process25:58
Data Analytics uses the Business Analysis Process22:59
Data Analytics uses the Information Systems Process14:24
Data Types in Python15:16
AI in Analytics32:44
Mathematical Operators and Defining Functions in Python13:09
Using Lambda in Python for Linear Algebra16:43
Logical Operators and Conditional Statements in Python14:17
Loops in Python19:02
Class in Python17:37
Creating a Pipeline in Python13:54
Cloud Computing10:38
SQL and Joins12:56
SQL and Google Cloud32:39
Export from Google Cloud22:53
Data Engineering - Cleaning20:43
Data Engineering - Coding28:19
Data Engineering - Manipulate14:16
Correlogram03:15
Intro to Exploratory Data Analysis (EDA)24:49
Intro to Descriptive Analytics01:12
Frequency and Mode in Python24:24
Median and Mean in Python05:53
Range and IQR in Python09:27
Standard Deviation and Variance in Python04:59
Descriptive Statistics Summary13:00
Inferential Statistics in Python - Compare to Value37:07
Inferential Statistics in Python - 2 Groups with Different People24:02
Inferential Statistics in Python - 2 Groups with Same People (paired)24:41
Inferential Statistics in Python - 3+ Groups with Different People32:13
Inferential Statistics in Python - 3+ Groups with Same People36:58
Inferential Statistics in Python - Association10:22
Introduction to Data Visualization05:33
Data Visualization in Python - Parts of a Whole - 1 Variable29:15
Data Visualization in Python - Parts of a Whole - 2 Variable15:54
Data Visualization in Python - Compare 1 or 2 Variables23:47
Data Visualization in Python - Compare 3 or More Variables25:48
Data Visualization in Python - Time37:31
Data Visualization in Python - Variability31:45
Data Visualization in Python - Association (relationship) of Variables12:31
Data Sources02:09
Web Analytics: YouTube Data24:46
Web Analytics: Twitter17:46
Reality Mining06:57
Reality Mining: Location Tracking12:53
Reality Mining: Create a Route10:43
Reality Mining: Bubble Map08:52
Text Mining Introduction15:18
Text Mining in Python - Import and Clean13:02
Text Mining in Python - Tokenize15:44
Text Mining in Python - Word Count09:26
Text Mining in Python - Word Cloud13:54
Text Mining in Python - Open Ended Survey Questions13:03
Text Mining in Python - Count Vectorizer12:56
Text Mining in Python - Ngrams22:38
Text Mining in Python - Zipfs law16:25
Text Mining in Python - Word Search09:13
Text Mining in Python - Word Tree07:15
Why You Need Tell Stories with Data20:15
How to tell stories with data23:36
Introduction to Dashboards06:35
Sankey Diagrams with Holoview and Plotly14:38
Gauge Charts in Python06:32
Dashboard in Power BI26:03
Using Plotly and Dash to Build a Dashboard23:23
Creating a Dashboard with Streamlit01:04:09
Intro to Diagnostic Analytics02:50
Intro to Supervised Learning01:30
Introduction Classification Models10:31
Classification Model in Python - K Nearest Neighbors (KNN)41:51
Classification Model in Python - General Linear Model (GLM)27:35
Classification Model in Python - Support Vector Machine (SVM)27:54
Classification Model in Python - Naive Bayes (NB)29:36
Classification Model in Python - Decision Tree36:25
Classification Model in Python - Random Forest16:50
Classification Model in Python - Gradient Boosting Machine (GBM)17:42
Classification Models in Python - Tuning Hyperparameters33:00
Classification Model Comparison10:23
Classification Models in Python - Use Cases07:08
Image Recognition Using KNN13:05
Denoise Images using KNN12:09
Recommendation Engine - Collaborative Filtering26:12
Webscraping21:18
Sentiment Analysis without Neural Networks17:22
Webscraping Static v Dynamic Websites05:48
Network (Graph) Theory15:50
Network Graphs14:34
Network Theory - Mapping the Spread of Information or Infection07:49
Network Graphs - Measuring Centrality15:52
Intro Enterprise Analytics07:05
Creating Organizational Charts in Python14:51
Organizational Hierarchy13:42
Voronoi Polygon using Python20:39
Process Flow in Visio10:45
Capacity Planning in Python17:44
Impact of Bottlenecks on Capacity Planning (analysis in Python)10:46
Performing Breakeven Analysis in Python19:40
Histograms in Python08:32
Pareto Chart in Python10:34
Control Chart in Python17:35
Intro to Predictive Analytics01:32
Demand Forecasting14:26
Forecasting Process16:02
Short Term Forecasting in Python38:11
Linear Forecasting in Python15:09
Trend Forecasting Using Polynomials in Python23:34
Forecasting using Logarithmic and Exponential Functions in Python15:42
Calculating Measurement Error using Python14:54
Creating Associative Models in Python10:05
ARIMA20:54
SARIMA21:44
Intro to Unsupervised Learning01:01
Intro to Cluster Analysis10:08
Cluster Analysis in Python - KMeans and Elbow Method22:23
Cluster Analysis in Python - Silhouette, Calinski Harabasz, and Davies Bouldin for KMeans15:16
Cluster Analysis in Python - Hierarchical Clustering (Agglomerative Clustering)26:36
Cluster Analysis in Python - Dendrograms19:01
Cluster Analysis in Python - How to use Cluster Analysis for Business Decisions27:47
Cluster Analysis in Python - DBSCAN18:08
Dimension Reduction in Python - Principal Component Analysis (PCA)29:44
Dimension Reduction in Python - Incremental PCA20:30
Dimension Reduction - Sparse and Kernel PCA22:00
Dimension Reduction in Python - Singular Value Decomposition (SVD) Latent Semantic Analysis (LSA)25:12
Dimension Reduction in Python - ISOMAP, LLE, and TSNE18:50
Dimension Reduction in Python - Nonlinear Methods23:14
Outlier Detection Models15:38
Benford's Law for Detecting Financial Fraud13:41
Outlier Detection Using Prophet Medium Term Forecasting Model19:37
Marketing Analytics20:48
Association Rule Analysis21:10
Intro to Prescriptive Analytics02:10
Linear Programming in Python17:35
Integer Programming in Python15:25
Integer Programming in Python Practice08:55
Binary Integer Programming35:05
Decision Tree Intro03:40
Creating Decision Trees in Schemdraw09:19
Decision Trees and Sensitivity Analysis (Quantity)25:27
Decision Trees and Sensitivity Analysis (Price)09:45
Decision Trees and Sensitivity Analysis (Price and Probability)11:12
Decision Tree with Imperfect Information (Bayes Theorem)39:15
Introduction to Monte Carlo Simulations08:15
Distributions in Python for Monte Carlo Simulations16:07
Monte Carlo Simulation Example17:48
PERT with Variability36:34
Decision Making with Uncertainty14:49
Queuing Models - Single Server26:49
Queuing Models - Multiple Servers16:49
Queuing Models - Number of Servers12:19
Queuing Models - Single Server with Finite Population10:30
Queuing Models - Single Server with Arbitrary Service Time08:56
Queuing Models - Single Server with Constant Service Time and Multiple Server with No Waitlist11:41
Logistic Regression in Python (smf and sklearn)32:26
Missingness in Data11:40
Poisson Regression in Python (smf and sklearn)28:07
Linear Regression in Python (smf and sklearn)32:26
Linear Regression Assumptions09:32
Ridge Regression in Python28:09
Lasso Regression in Python20:58
Intro to Reinforced Learning01:45
Intro to Reinforced Learning12:56
Reinforced Learning: Brute Force11:09
Reinforced Learning: Q Learning16:31
Reinforced Learning: SARSA12:37
Intro to Neural Networks13:02
Designing a Neural Network13:04
Layers of Neural Networks13:41
MLP Classifier20:41
MLP Regressor22:12
Convolution Neural Network (CNN) for Image Classification25:32
CNN for Sentiment48:18
RNN for Time Series Forecasts43:24
Creating Entity Relationship Diagrams using Draw.io09:44
Data Engineering Part 117:42
Data Engineering Part 218:43
Dealing with Missing Data Part 128:11
Dealing with Missing Data Part 211:14

Data Governance

Python Analytics

Machine Learning

Data Visualization

Data Engineering

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