Python Data Science with Python Masterclass E-Learning Kurs
Data Science with Python Masterclass E-Learning Kurs
€1.249,00 €999,00
Python
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Data Science with Python Masterclass E-Learning Kurs

EUR 999,00
EUR 1.249,00 exkl. MwSt.

Preisgekrönte Data Science mit Python Masterclass E-Learning mit Zugriff auf einen Online-Mentor per Chat oder E-Mail, Abschlussprüfung und Practice Labs.

  • E-Learning - Online-Zugang: 365 Tage
  • Englische Sprache
  • Teilnahmeurkunde
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Data Science with Python Masterclass E-Learning Ausbildung

Diese Reise bietet zunächst eine Grundlage für die Programmierkenntnisse in den Bereichen Datenarchitektur, Statistik und Datenanalyse mit Python und R. Anschließend lernen Sie, die Daten mit Python und R zu wringen und diese Daten in Spark und Hadoop zu integrieren. Anschließend lernen Sie, wie Sie Daten unter Berücksichtigung von Compliance und Governance operationalisieren und skalieren. Um die Reise abzuschließen, lernen Sie, wie Sie diese Daten erfassen und visualisieren, um kluge Geschäftsentscheidungen zu treffen.

Dieser Lernpfad mit mehr als 120 Stunden Online-Inhalten ist in die folgenden vier Titel unterteilt:

Data Science Track 1: Datenanalyst
Data Science Track 2: Data Wrangler
Data Science Track 3: Datenoperationen
Data Science Track 4: Data Scientist

Kursinhalt

Data Science Track 1: Data Analyst

In this track, the focus is the data analyst role with a focus on: Python, R, architecture, statistics, and Spark.

E-Learning courses

Data Wrangling with Pandas: Working with Series & DataFrames
Course: 1 Hour, 11 Minutes

Course Overview
Installing Pandas
Pandas Series Objects
Operations on Series
Appending and Sorting Series Values
Pandas DataFrames
Indexing Operations with DataFrames
Missing Data
Column Aggregations
Statistical Operations
Exercise: Operations on Series and DataFrames

Data Wrangling with Pandas: Visualizations and Time-Series Data
Course: 1 Hour, 29 Minutes

Course Overview
Pandas and Matplotlib for Visualizations
Pie Charts, Box Plots, and Scatter Plots
Time-Series Data
Deltas and Percentage Change Calculations
Time Deltas and Date Ranges
Mismatched DataFrames and Missing Data
Working with String Data
Advanced Operations on Strings
Applying Functions on Series
Transforming Data With User-Defined Functions
Applying Functions on DataFrames
Exercise: Plot Charts and Transform Column Values

Data Wrangling with Pandas: Advanced Features
Course: 1 Hour, 12 Minutes

Course Overview
Grouping and Aggregations
MultiIndex DataFrames
Grouping and Aggregations with MultiIndex DataFrames
General Aggregation Functions
Filtering
Masking Column Values
Working with Duplicates
Working with Categorical Data
Filtering, Adding, and Removing Categories
Reindexing
Exercise: Filtering, Duplicates and Categorical Data

Data Wrangler 4: Cleaning Data in R
Course: 1 Hour, 3 Minutes

Course Overview
Types of Unclean Data
Data Quality
Downloading JSON Data
Excel Sheets
Reading Dirty CSVs
Querying Relational Databases
Joining Tabular Data
Spreading Data
Summarizing Data
Imputing Data
Extracting Matches
Exercise: Wrangling Data

Data Tools: Technology Landscape & Tools for Data Management
Course: 27 Minutes

Course Overview
Technology Landscape and Tools
Tool Comparison
Machine Learning in Data Analytics
Machine Learning Tools
Machine Learning Implementation
Python and R for Data Management
Cloud and Machine Learning
Exercise: Implement Machine Learning on Scikit-learn

Data Tools: Machine Learning & Deep Learning in the Cloud
Course: 23 Minutes

Course Overview
Microsoft Machine Learning Toolkit
AWS and Machine Learning
Spark Machine Learning Capabilities
Deep Learning Frameworks
Deep Learning Implementation
Data Mining and Analytical Tools
KNIME Capabilities
Exercise: Implement Deep Learning

Trifacta for Data Wrangling: Wrangling Data
Course: 50 Minutes

Course Overview
Standardizing Data
Formatting Dates
Filtering Rows
Replacing Values
Counting Matches
Splitting Columns
Merging Columns
Extracting Data
Conditional Aggregation
Reshaping Data
Joining Data
Exercise: Wrangling Data

MongoDB for Data Wrangling: Querying
Course: 1 Hour, 8 Minutes

Course Overview
Introduction to PyMongo
Document Structure
CRUD Operations
ObjectID and Timestamp
Query Operations
Projection Queries
Comparison Operators
Element Query Operators
The Regex Operator
Using the Size and All Operators
Text Search
Using mongoimport
Using mongoexport
Exercise: Performing a Query

MongoDB for Data Wrangling: Aggregation
Course: 51 Minutes

Course Overview
Aggregation Framework
Using Group
Using Match
Using Project
Using Limit and Sort
Using Unwind
Using Lookup
Using Indexes
Using Geospatial Indexes
Exercise: Performing an Aggregate Query

Getting Started with Hive: Introduction
Course: 56 Minutes

Course Overview
Hive as a Data Warehouse
Overview of Relational Databases
OLTP and OLAP
Hive and the Hadoop Ecosystem
HiveServer and The Metastore
Hive on Cloud Computing Platforms
Data Types in Hive
Data and Tables in Hive
Exercise: Introduction to Hive

Getting Started with Hive: Loading and Querying Data
Course: 1 Hour, 20 Minutes

Course Overview
Setting up a Hadoop Cluster on the Google Cloud
Creating a Hive Table
Running Simple Queries in Hive
Executing Hive Queries from the Shell
Joining Tables in Hive
Exploring the Hive Warehouse
External Tables in Hive
Modifying Tables in Hive
Temporary Tables in Hive
Loading Data into Tables in Hive
Populating Multiple Tables in Hive
Exercise: Loading and Querying Data in Hive

Getting Started with Hive: Viewing and Querying Complex Data
Course: 1 Hour, 14 Minutes

Course Overview
The Array Data Type in Hive
The Map Data Type in Hive
The Struct Type in Hive
The explode and posexplode Functions in Hive
Lateral Views in Hive
Multiple Lateral Views in Hive
Set Operations in Hive
The IN and EXISTS clauses in Hive
Creating and Populating Tables in Hive
Views in Hive
Exercise: Viewing and Querying Complex Data

Getting Started with Hive: Optimizing Query Executions
Course: 43 Minutes

Course Overview
Hive Queries as MapReduce Jobs
Techniques to Improve Query Performance in Hive
Partitioning Tables in Hive
Bucketing Tables in Hive
Structuring Join Queries in Hive
Exercise: Optimizing Query Execution in Hive

Getting Started with Hive: Optimizing Query Executions with Partitioning
Course: 1 Hour, 1 Minute

Course Overview
Setting up a Hadoop Cluster on the Google Cloud
Creating a Partitioned Table in Hive
Working with Partitions in Hive
Populating Partitions in Hive
Partitioning External Tables in Hive
Modifying Partitions in Hive
Dynamic Partitions in Hive
Using Multiple Columns for Partitioning in Hive
Exercise: Optimize Executions with Partitioning

Getting Started with Hive: Bucketing & Window Functions
Course: 1 Hour, 4 Minutes

Course Overview
Apply Bucketing for a Table in Hive
Using Bucketing and Partitioning Together in Hive
Sorting a Bucket's Contents in Hive
Sampling a Table in Hive
Joining Multiple Tables in Hive
Introducing Window Functions in Hive
Windows Functions with Partitions in Hive
Exercise: Bucketing and Window Functions in Hive

Getting Started with Hadoop: Filtering Data Using MapReduce
Course: 59 Minutes

Course Overview
Counting the Data Points in Each Category
The Reducer and Driver Programs
Building and Executing the Application
A Simple Filter Using MapReduce
Executing and Examining the Output
Extracting the Unique Values in a Column
Viewing the Distinct Values Extracted
Exercise: Filtering Data Using MapReduce

Getting Started with Hadoop: MapReduce Applications With Combiners
Course: 1 Hour, 24 Minutes

Course Overview
Combiners in MapReduce
Revisiting MapReduce
Working with Combiners
Using Combiners for Calculating Averages
Creating a Project to Calculate Averages
Coding the Map and Reduce Phases
Configure the Application in the Driver
Executing the Application and Examining the Output
Adding a Combiner to a MapReduce Application
Conveying a Pair of Numbers from the Mapper
Running the Fixed Application
Exercise: Optimizing MapReduce With Combiners

Getting Started with Hadoop: Advanced Operations Using MapReduce
Course: 49 Minutes

Course Overview
Defining a User-Defined Type for a PriorityQueue
Implementing a PriorityQueue in a Mapper
Using a PriorityQueue in a Reducer
Running and Verifying the Results
Building an Inverted Index - Map Phase
Building an Inverted Index - Reduce Phase
Executing the Application and Viewing the Index
Exercise: Advanced Operations Using MapReduce

Accessing Data with Spark: Data Analysis Using the Spark DataFrame API
Course: 1 Hour, 12 Minutes

Course Overview
Performance Improvements in Spark
Broadcast Variables and Accumulators
Loading Data into a DataFrame
Sampling the Contents of a DataFrame
Grouping and Aggregations
Visualizing Data in a DataFrame
Trimming and Cleaning Data
User-Defined Functions and DataFrames
Combining Filters, Aggregations, and Sorting
Using Broadcast Variables
Using Accumulators
Exporting DataFrame Contents
Custom Accumulators
Join Operations
Exercise: Data Analysis Using the DataFrame API

Accessing Data with Spark: Data Analysis using Spark SQL
Course: 55 Minutes

Course Overview
The Spark Catalyst Optimizer
Introduction to Spark SQL
Preparing Data for Analysis
Running SQL Queries
Inferred and Explicit Schemas
Windowing in Spark
Applying Window Functions
Exercise: Data Analysis Using Spark SQL

Data Lake: Framework & Design Implementation
Course: 34 Minutes

Course Overview
Data Lakes and Data Warehouses
Data Lake Selection Criteria
Data Lake and Data Democratization
Data Lake Design Principles
AWS Data Lake Architecture
Implement AWS Data Store
Data Lake For On-Premise and Multi-Cloud
Data Processing Frameworks for Data Lake
Exercise: Implement AWS Data Store

Data Lake: Architectures & Data Management Principles
Course: 35 Minutes

Course Overvie
Real-Time Big Data Architectures
Data Lake Reference Architecture
Data Ingestion and File Formats
Ingestion Using Sqoop
Data Processing Strategies
Deriving Value from Data Lakes
Data Life Cycle
S3 and Glacier
Exercise: Ingest Data and Implement Archival Policy

Data Architecture - Deep Dive: Design & Implementation
Course: 36 Minutes

Course Overview
Data Complexity Management Strategies
Data Modeling Process
Distributed Data Management
Partitioning Methods and Criteria
MongoDB Partitioning
Hybrid Data Architectures
Implement Directed Acyclic Graph
CAP Theorem
Batch vs. Streaming
Read and Write Concerns
Exercise: Implement Serverless Architecture

Data Architecture - Deep Dive: Microservices & Serverless Computing
Course: 26 Minutes

Course Overview
Microservices and Data
Serverless and Lambda Architecture
Lambda Implementation
Cluster Benefits
Data Architecture Types
Data Discovery Process
Data Risk Types
Data POC
Exercise: Implement Lambda Architecture

Balancing the Four Vs of Data: The Four Vs of Data
Course: 40 Minutes

Course Overview
Overview of the Four Vs
The Importance of Volume
The Importance of Variety
The Importance of Velocity
The Importance of Veracity
The Relationship Between the Four Vs
Variety and Data Structure
Validity and Volatility
Finding Balance in the Four Vs
Use Cases
Extracting Value from the Four Vs
Exercise: Describe the Four Vs of Big Data

Data Driven Organizations
Course: 1 Hour, 15 Minutes

Course Overview
Data Driven Organizations
Decision Making
Analytic Maturity
Analytic Roles
Data Source Priority
Facets of Data Quality
Power BI Data Visualization
Missing Data
Duplicate Data
Truncated Data
Data Provenance
Exercise: Use Informatica Data Quality

Raw Data to Insights: Data Ingestion & Statistical Analysis
Course: 54 Minutes

Course Overview
Statistical Analysis
Data Correction
Outlier Detection
Data Architecture Pattern
Data Ingestion Tools
Kafka and Apache NiFi
Apache Sqoop Ingest
Ingest Using WaveFront
Exercise: Detecting Outliers and Ingesting Data

Raw Data to Insights: Data Management & Decision Making
Course: 57 Minutes

Course Overview
Data-driven Decision Making Framework
Loading Data into R
Preparing Data
Data Correction Approach
Data Correction Using Simple Transformation
Data Correction Using Deductive Correction
Distributed Data Management
Data Analytics
Data Analytics Using R
Predictive Modeling
Exercise: Correcting and Modelling Data

Tableau Desktop: Real Time Dashboards
Course: 1 Hour, 8 Minutes

Course Overview
Introducing Real Time Dashboards
Creating Real Time Dashboards with Tableau
Build a Tableau Dashboard
Real Time Dashboard Updates in Tableau
Organizing Your Tableau Dashboard
Formatting Your Tableau Dashboard
Interactive Tableau Dashboard
Tableau Dashboard Starters
Tableau Dashboard Extensions
Tableau Dashboards and Story Points
Sharing your Tableau Dashboard
Exercise: Creating a Tableau Dashboard Starter

Storytelling with Data: Introduction
Course: 47 Minutes

Course Overview
Storytelling Process
Interpreting Context
Analysis Types
Who, What, and How of Storytelling
Visualization for Storytelling
Graphical Tools for Data Elaboration
Storytelling Scenarios
Storyboarding
Exercise: Visualization and Graphical Tools

Storytelling with Data: Tableau & PowerBI
Course: 57 Minutes

Course Overview
Data-driven Decision Making Framework
Loading Data into R
Preparing Data
Data Correction Approach
Data Correction Using Simple Transformation
Data Correction Using Deductive Correction
Distributed Data Management
Data Analytics
Data Analytics Using R
Predictive Modeling
Exercise: Correcting and Modelling Data

Python for Data Science: Basic Data Visualization Using Seaborn
Course: 1 Hour, 7 Minutes

Course Overview
Introduction to Seaborn
Install Seaborn
Simple Univariate Distributions
Configure Univariate Distribution Plots
Simple Bivariate Distributions
Explore Different Types of Bivariate Distributions
Analyze Multiple Variable Pairs
Regression Plots
Themes and Styles in Seaborn
Exercise: Basic Data Visualization Using Seaborn

Python for Data Science: Advanced Data Visualization Using Seaborn
Course: 1 Hour, 4 Minutes

Course Overview
Searching for Patterns in a Dataset
Configuring Plot Aesthetics
Normal Distribution and Outliers
Distributions Within Categories - Part
Distributions Within Categories - Part
Analyzing Categories with Facet Grids - Part
Analyzing Categories with Facet Grids - Part
Introducing Color Palettes
Using Color Palettes
Exercise: Advanced Data Visualization Using Seaborn

Data Science Statistics: Using Python to Compute & Visualize Statistics
Course: 1 Hour, 16 Minutes

Course Overview
An Introduction to Matplotlib
Analyzing Data Using NumPy and Pandas
Visualizing Univariate and Bivariate Distributions
Summary Statistics Using Native Python Functions
Summary Statistics Using NumPy
Summary Statistics Using the SciPy Library
Correlation and Covariance
Z-score
Exercise: Compute and Visualize Statistics

R for Data Science: Data Visualization
Course: 33 Minutes

Course Overview
Using Scatter Plots
Using Line Graphs
Using Bar Charts
Using Box and Whisker Plots
Using Histograms
Using a Bubble Plot
Exercise: Data Visualization

Advanced Visualizations & Dashboards: Visualization Using Python
Course: 38 Minutes

Course Overview
Relevance of Data Visualization for Business
Libraries for Data Visualization in Python
Python Data Visualization Environment Configuration
Matplotlib Libraries for Visualization
Bar Chart Using ggplot
Bokeh and Pygal
Select Visualization Libraries
Interactive Graphs and Image Files
Plot Graphs
Multiple Lines in Graphs
Exercise: Create Line Charts with Pygal

Advanced Visualizations & Dashboards: Visualization Using R
Course: 35 Minutes

Course Overview
Chart Types
Stacked Bar Plot
Animate Plots with Matplotlib
Plotting in Jupyter Notebook
Graphics in R
Heat Map and Scatter Plot in R
Correlogram and Area Chart in R
ggplot2 Capabilities
Customize ggplot2 Graphs
Exercise: Creating Heat Maps and Scatter Plots

Powering Recommendation Engines: Recommendation Engines
Course: 1 Hour, 5 Minutes

Course Overview
Describing Recommendation Engines
Comparing the Types of Recommendation Engines
Collecting and Manipulating Data
Manipulating Data in R
Describing Similarity and Neighborhoods
Creating a Recommendation Engine
Recommending Another Item
Finding Items to Recommend
Recommending Items Based on Other Items
Evaluating a Recommendation System
Validating a Recommendation System
Exercise: Creating a Recommendation

Data Insights, Anomalies, & Verification: Handling Anomalies
Course: 46 Minutes

Course Overview
Data and Anomaly Sources
Decomposition and Forecasting
Examine Data Using Randomization Tests
Anomaly Detection
Anomaly Detection Techniques
Anomaly Detection with scikit-learn
Anomaly Detection Tools
Anomaly Detection Rules
Exercise: Detecting Anomalies

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
Course: 51 Minutes

Course Overview2
Machine Learning Anomaly Detection Techniques
Comparing Anomaly Detection Algorithms
Anomaly Detection Using R
Online Anomaly Detection Components
Online Anomaly Detection Approaches
Anomaly Detection Use Cases
Anomaly Detection with Visualization Tools
Anomaly Detection with Mathematical Approaches
Cluster-Based Anomaly Detection
Exercise: Detecting Anomalies

Data Science Statistics: Applied Inferential Statistics
Course: 1 Hour, 19 Minutes

Course Overview
The One-Sample T-test
Independent and Paired T-tests
Testing Hypotheses with T-tests
Loading and Analyzing a Skewed Dataset
Measuring Skewness and Kurtosis
Preparing a Dataset for Regression
Simple Linear Regression
Multiple Linear Regression
Exercise: Applied Inferential Statistics

Data Research Techniques
Course: 33 Minutes

Course Overview
Data Research Fundamentals
Data Research Steps
Values, Variables, and Observations
JMP Scale of Measurement
Non-experimental and Experimental Research
Descriptive and Inferential Statistical Analysis
Inferential Tests
Case Study of Clinical Data Research
Data Research in Sales Management
Exercise: Implement Data Research

Data Research Exploration Techniques
Course: 50 Minutes

Course Overview
Fundamentals of Exploratory Data Analysis
Data Exploration Types
Working with R
Data Exploration in R
Data Exploration Using Plots
Python Packages for Data Exploration
Data Exploration Using Python
Data Research Using Linear Algebra
Linear Algebra for Data Research
Exercise: R and Python for Data Exploration

Data Research Statistical Approaches
Course: 43 Minutes

Course Overview
Role of Statistics in Data Research
Discrete vs. Continuous Distribution
PDF and CDF
Binomial Distribution
Interval Estimation
Point and Interval Estimation
Data Visualization Techniques
Data Visualization Using R
Data Integration Techniques
Creating Plots
Missing Values and Outliers
Exercise: Statistical Methods for Data Research

Machine & Deep Learning Algorithms: Introduction
Course: 46 Minutes

Course Overview
Machine Learning Algorithms
How Machine Learning Works
Introduction to Pandas ML
Support Vector Machines
Overfitting
Exercise: Machine Learning and Classification

Machine & Deep Learning Algorithms: Regression & Clustering
Course: 49 Minutes

Course Overview
The Confusion Matrix
An Introduction to Regression
Applications of Regression
Supervised and Unsupervised Learning
Clustering
Principal Component Analysis
Exercise: Regression and Clustering

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML
Course: 1 Hour, 4 Minutes

Course Overview
Data Preparation in scikit-learn
Training and Evaluating Models in scikit-learn
Introducing the Pandas ML ModelFrame
Training and Evaluating Models in Pandas ML
Preparing Data for Regression
Evaluating Regression Models
Preparing Data for Clustering
The K-Means Clustering Algorithm
Exercise: Regression, Classification, and Clustering

Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML
Course: 1 Hour, 24 Minutes

Course Overview
Analyzing an Imbalanced Dataset
The RandomOverSampler
The SMOTE Oversampler
Undersampling Using imbalanced-learn
Ensemble Classifiers for Imbalanced Data
Combination Samplers
Finding Correlations in a Dataset
Building a Multi-Label Classification Model
Dimensionality Reduction with PCA
Imbalanced Learn and PCA
Exercise: Working with Imbalanced Datasets

Creating Data APIs Using Node.js
Course: 1 Hour, 31 Minutes

Course Overview
API Prerequisites
Building a RESTful API Using Node.js and Express.js
RESTful API with OAuth
HTTP Server with Hapi.js
API Modules
Returning Data with JSON
Nodemon for Development Workflow
API Requests
POSTman for API
Deploying APIs
Social Media APIs
Exercise: Building RESTful APIs

Balancing the Four Vs of Data: The Four Vs of Data
Course: 40 Minutes

Course Overview
Overview of the Four Vs
The Importance of Volume
The Importance of Variety
The Importance of Velocity
The Importance of Veracity
The Relationship Between the Four Vs
Variety and Data Structure
Validity and Volatility
Finding Balance in the Four Vs
Use Cases
Extracting Value from the Four Vs
Exercise: Describe the Four Vs of Big Data

Data Science Track 4: Data Scientist

For this track, the focus will be on the Data Scientist role. Here we will explore areas such as: visualization, APIs, and ML and DL algorithms.

E-Learning courses

Data Driven Organizations
Course: 1 Hour, 15 Minutes

Course Overview
Data Driven Organizations
Decision Making
Analytic Maturity
Analytic Roles
Data Source Priority
Facets of Data Quality
Power BI Data Visualization
Missing Data
Duplicate Data
Truncated Data
Data Provenance
Exercise: Use Informatica Data Quality

Raw Data to Insights: Data Ingestion & Statistical Analysis
Course: 54 Minutes

Course Overview
Statistical Analysis
Data Correction
Outlier Detection
Data Architecture Pattern
Data Ingestion Tools
Kafka and Apache NiFi
Apache Sqoop Ingest
Ingest Using WaveFront
Exercise: Detecting Outliers and Ingesting Data

Raw Data to Insights: Data Management & Decision Making
Course: 57 Minutes

Course Overview
Data-driven Decision Making Framework
Loading Data into R
Preparing Data
Data Correction Approach
Data Correction Using Simple Transformation
Data Correction Using Deductive Correction
Distributed Data Management
Data Analytics
Data Analytics Using R
Predictive Modeling
Exercise: Correcting and Modelling Data

Tableau Desktop: Real Time Dashboards
Course: 1 Hour, 8 Minutes

Course Overview
Introducing Real Time Dashboards
Creating Real Time Dashboards with Tableau
Build a Tableau Dashboard
Real Time Dashboard Updates in Tableau
Organizing Your Tableau Dashboard
Formatting Your Tableau Dashboard
Interactive Tableau Dashboard
Tableau Dashboard Starters
Tableau Dashboard Extensions
Tableau Dashboards and Story Points
Sharing your Tableau Dashboard
Exercise: Creating a Tableau Dashboard Starter

Storytelling with Data: Introduction
Course: 47 Minutes

Course Overview
Storytelling Process
Interpreting Context
Analysis Types
Who, What, and How of Storytelling
Visualization for Storytelling
Graphical Tools for Data Elaboration
Storytelling Scenarios
Storyboarding
Exercise: Visualization and Graphical Tools

Storytelling with Data: Tableau & PowerBI
Course: 57 Minutes

Course Overview
Visual Selection
Slopegraphs
Bar Charts and Types of Bar Charts
Clutter and Clutter Elimination
Gestalt Principle
Story Design Best Practices
Tools for Storytelling
Decluttering
Crafting Visual Data
Visual Design Concerns
Storytelling with Power BI
Model Visual and Tableau
Exercise: Storytelling with Power BI

Python for Data Science: Basic Data Visualization Using Seaborn
Course: 1 Hour, 7 Minutes

Course Overview
Introduction to Seaborn
Install Seaborn
Simple Univariate Distributions
Configure Univariate Distribution Plots
Simple Bivariate Distributions
Explore Different Types of Bivariate Distributions
Analyze Multiple Variable Pairs
Regression Plots
Themes and Styles in Seaborn
Exercise: Basic Data Visualization Using Seaborn

Python for Data Science: Advanced Data Visualization Using Seaborn
Course: 1 Hour, 4 Minutes

Course Overview
Searching for Patterns in a Dataset
Configuring Plot Aesthetics
Normal Distribution and Outliers
Distributions Within Categories - Part
Distributions Within Categories - Part
Analyzing Categories with Facet Grids - Part
Analyzing Categories with Facet Grids - Part
Introducing Color Palettes
Using Color Palettes
Exercise: Advanced Data Visualization Using Seaborn

Data Science Statistics: Using Python to Compute & Visualize Statistics
Course: 1 Hour, 16 Minutes

Course Overview
An Introduction to Matplotlib
Analyzing Data Using NumPy and Pandas
Visualizing Univariate and Bivariate Distributions
Summary Statistics Using Native Python Functions
Summary Statistics Using NumPy
Summary Statistics Using the SciPy Library
Correlation and Covariance
Z-score
Exercise: Compute and Visualize Statistics

R for Data Science: Data Visualization
Course: 33 Minutes

Course Overview
An Introduction to Matplotlib
Analyzing Data Using NumPy and Pandas
Visualizing Univariate and Bivariate Distributions
Summary Statistics Using Native Python Functions
Summary Statistics Using NumPy
Summary Statistics Using the SciPy Library
Correlation and Covariance
Z-score
Exercise: Compute and Visualize Statistics

Advanced Visualizations & Dashboards: Visualization Using Python
Course: 38 Minutes

Course Overview
Relevance of Data Visualization for Business
Libraries for Data Visualization in Python
Python Data Visualization Environment Configuration
Matplotlib Libraries for Visualization
Bar Chart Using ggplot
Bokeh and Pygal
Select Visualization Libraries
Interactive Graphs and Image Files
Plot Graphs
Multiple Lines in Graphs
Exercise: Create Line Charts with Pygal

Advanced Visualizations & Dashboards: Visualization Using R
Course: 35 Minutes

Course Overview
Chart Types
Stacked Bar Plot
Animate Plots with Matplotlib
Plotting in Jupyter Notebook
Graphics in R
Heat Map and Scatter Plot in R
Correlogram and Area Chart in R
ggplot2 Capabilities
Customize ggplot2 Graphs
Exercise: Creating Heat Maps and Scatter Plots

Powering Recommendation Engines: Recommendation Engines
Course: 1 Hour, 5 Minutes

Course Overview
Describing Recommendation Engines
Comparing the Types of Recommendation Engines
Collecting and Manipulating Data
Manipulating Data in R
Describing Similarity and Neighborhoods
Creating a Recommendation Engine
Recommending Another Item
Finding Items to Recommend
Recommending Items Based on Other Items
Evaluating a Recommendation System
Validating a Recommendation System
Exercise: Creating a Recommendation Engine

Data Insights, Anomalies, & Verification: Handling Anomalies
Course: 46 Minutes

Course Overview
Data and Anomaly Sources
Decomposition and Forecasting
Examine Data Using Randomization Tests
Anomaly Detection
Anomaly Detection Techniques
Anomaly Detection with scikit-learn
Anomaly Detection Tools
Anomaly Detection Rules
Exercise: Detecting Anomalies

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
Course: 51 Minutes

Course Overview
Machine Learning Anomaly Detection Techniques
Comparing Anomaly Detection Algorithms
Anomaly Detection Using R
Online Anomaly Detection Components
Online Anomaly Detection Approaches
Anomaly Detection Use Cases
Anomaly Detection with Visualization Tools
Anomaly Detection with Mathematical Approaches
Cluster-Based Anomaly Detection
Exercise: Detecting Anomalies

Data Science Statistics: Applied Inferential Statistics
Course: 1 Hour, 19 Minutes

Course Overview
The One-Sample T-test
Independent and Paired T-tests
Testing Hypotheses with T-tests
Loading and Analyzing a Skewed Dataset
Measuring Skewness and Kurtosis
Preparing a Dataset for Regression
Simple Linear Regression
Multiple Linear Regression
Exercise: Applied Inferential Statistics

Data Research Techniques
Course: 33 Minutes

Course Overview
Data Research Fundamentals
Data Research Steps
Values, Variables, and Observations
JMP Scale of Measurement
Non-experimental and Experimental Research
Descriptive and Inferential Statistical Analysis
Inferential Tests
Case Study of Clinical Data Research
Data Research in Sales Management
Exercise: Implement Data Research

Data Research Exploration Techniques
Course: 50 Minutes

Course Overview
Fundamentals of Exploratory Data Analysis
Data Exploration Types
Working with R
Data Exploration in R
Data Exploration Using Plots
Python Packages for Data Exploration
Data Exploration Using Python
Data Research Using Linear Algebra
Linear Algebra for Data Research
Exercise: R and Python for Data Exploration

Data Research Statistical Approaches
Course: 43 Minutes

Course Overview
Role of Statistics in Data Research
Discrete vs. Continuous Distribution
PDF and CDF
Binomial Distribution
Interval Estimation
Point and Interval Estimation
Data Visualization Techniques
Data Visualization Using R
Data Integration Techniques
Creating Plots
Missing Values and Outliers
Exercise: Statistical Methods for Data Research

Machine & Deep Learning Algorithms: Introduction
Course: 46 Minutes

Course Overview
Machine Learning Algorithms
How Machine Learning Works
Introduction to Pandas ML
Support Vector Machines
Overfitting
Exercise: Machine Learning and Classification

Machine & Deep Learning Algorithms: Regression & Clustering
Course: 49 Minutes

Course Overview
The Confusion Matrix
An Introduction to Regression
Applications of Regression
Supervised and Unsupervised Learning
Clustering
Principal Component Analysis
Exercise: Regression and Clustering

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML
Course: 1 Hour, 4 Minutes

Course Overview
Data Preparation in scikit-learn
Training and Evaluating Models in scikit-learn
Introducing the Pandas ML ModelFrame
Training and Evaluating Models in Pandas ML
Preparing Data for Regression
Evaluating Regression Models
Preparing Data for Clustering
The K-Means Clustering Algorithm
Exercise: Regression, Classification, and Clustering

Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML
Course: 1 Hour, 24 Minutes

Course Overview
Analyzing an Imbalanced Dataset
The RandomOverSampler
The SMOTE Oversampler
Undersampling Using imbalanced-learn
Ensemble Classifiers for Imbalanced Data
Combination Samplers
Finding Correlations in a Dataset
Building a Multi-Label Classification Model
Dimensionality Reduction with PCA
Imbalanced Learn and PCA
Exercise: Working with Imbalanced Datasets

Creating Data APIs Using Node.js
Course: 1 Hour, 31 Minutes

Course Overview
API Prerequisites
Building a RESTful API Using Node.js and Express.js
RESTful API with OAuth
HTTP Server with Hapi.js
API Modules
Returning Data with JSON
Nodemon for Development Workflow
API Requests
POSTman for API
Deploying APIs
Social Media APIs
Exercise: Building RESTful APIs

Online Mentor

You can reach your Mentor by entering chats or submitting an email.

Final Exam assessment

Estimated duration: 90 minutes

Practice Labs: Data Visualization with Python (estimated duration: 8 hours)

Perform data visualization tasks with Python such as creating scatter plots, plotting linear regression, using logistic regression and creating decision tree. Then, test your skills by answering assessment questions after creating time-series graphs, resampling observations, creating histograms and using a grid pair.

Eigenschaften
Unterrichtsdauer 71 Stunde
Sprache Englisch
Online-Zugang 365 Tage
Teilnahmeurkunde Ja
Preisgekröntes Online-Training Ja
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