Data Analytics Courses

Week 1: Introduction to R and Basic Concepts (4 Hours)

Session 1 (2 Hours): Getting Started with R

Introduction to R and its Importance in Data Science

Setting Up the R Environment (R and RStudio Installation)

Basic Syntax, Variables, and Data Types in R

Session 2 (2 Hours): Data Manipulation Basics

Reading and Writing Data in R

Introduction to Data Manipulation with dplyr

Basic Data Cleaning Techniques

Week 2: Data Analysis and Visualization in R (6 Hours)

Session 3 (2 Hours): Exploratory Data Analysis (EDA)

Conducting EDA with R

Descriptive Statistics and Summarization

Handling Missing Values and Outliers

Session 4 (2 Hours): Data Visualization with ggplot2

Basics of ggplot2 for Data Visualization

Creating Various Types of Plots (Bar, Line, Scatter, Histogram)

Customizing Plots for Clarity and Aesthetics

Session 5 (2 Hours): Advanced Data Visualization

Advanced ggplot2 Features

Interactive Visualization with Plotly

Creating Dashboards and Reports

Week 3: Statistical Modeling and Machine Learning in R (6 Hours)

Session 6 (2 Hours): Introduction to Statistical Modeling

Linear Regression Analysis

Logistic Regression for Categorical Data

Model Diagnostics and Interpretation

Session 7 (2 Hours): Machine Learning Basics in R

Introduction to Machine Learning with R

Building Classification and Regression Models

Evaluating Model Performance

Session 8 (2 Hours): Advanced Topics in Machine Learning

Decision Trees and Random Forests

Clustering Techniques (k-means, Hierarchical)

Introduction to Text Mining and Sentiment Analysis

Week 4: Business Applications and Capstone Project (4 Hours)

Session 9 (2 Hours): R in Business Contexts

Case Studies: Real-World Applications of R in Business

Data-Driven Decision-Making in Business

Ethical Considerations in Data Science

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Applying R Skills to a Business-Related Data Science Project

Presentation and Discussion of Capstone Projects

Course Summary and Recommendations for Further Learning

The course should include a mix of theoretical instruction, practical demonstrations, and hands-on exercises using R. The capstone project in the final week should involve applying R skills to a real-world business problem, enabling students to demonstrate their ability to use R for data-driven decision-making in a business context.

Week 1: Introduction to SAS and Basic Data Handling (4 Hours)

Session 1 (2 Hours): Introduction to SAS

Overview of SAS Software: History and Industry Applications

Navigating the SAS Environment (SAS Studio, SAS Enterprise Guide)

Basic Syntax and Commands in SAS

Session 2 (2 Hours): Data Management Basics in SAS

Importing and Exporting Data in SAS

Data Manipulation Techniques (SORT, SET, MERGE)

Introduction to SAS Libraries and Data Sets

Week 2: Data Analysis and Reporting in SAS (6 Hours)

Session 3 (2 Hours): Descriptive Statistics and Data Summarization

Generating Descriptive Statistics in SAS (MEANS, FREQ, SUMMARY)

Data Summarization Techniques

Creating Basic Reports and Outputs

Session 4 (2 Hours): Data Visualization in SAS

Introduction to SAS Graphical Procedures (SGPLOT, SGPANEL)

Creating Charts and Graphs for Data Presentation

Customizing Visual Outputs in SAS

Session 5 (2 Hours): Advanced Data Analysis Techniques

Conducting Correlation and Regression Analysis

ANOVA and Other Statistical Tests

Introduction to Predictive Modeling in SAS

Week 3: Advanced SAS Programming and Analytics (6 Hours)

Session 6 (2 Hours): Advanced Data Manipulation

Advanced SAS Functions and Procedures

Data Cleaning and Preprocessing Techniques

Working with Dates and Times in SAS

Session 7 (2 Hours): SQL Programming in SAS

Introduction to PROC SQL for Data Querying

Combining SAS Datasets with SQL Joins

Advanced SQL Queries in SAS

Session 8 (2 Hours): Macro Programming in SAS

Basics of SAS Macro Language

Automating Tasks with Macros

Building and Using Macro Variables

Week 4: Business Applications and Capstone Project (4 Hours)

Session 9 (2 Hours): SAS in Business Contexts

Case Studies: SAS Applications in Finance, Marketing, and Operations

Discussing Ethical Implications of Data Analytics

Integrating SAS Analysis into Business Decision-Making

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Applying SAS Skills to a Real-World Business Problem

Presenting Capstone Project Findings

Course Summary and Recommendations for Continued Learning

Week 1: Introduction to Big Data and Apache Spark (4 Hours)

Session 1 (2 Hours): Fundamentals of Big Data

Introduction to Big Data: Concepts and Relevance in Business

Big Data Challenges and Technologies

Overview of the Big Data Ecosystem

Session 2 (2 Hours): Getting Started with Apache Spark

Introduction to Apache Spark and its Advantages

Understanding Spark’s Architecture and Components

Setting Up a Spark Environment (e.g., Databricks or Local Setup)

Week 2: Spark RDDs and DataFrames (6 Hours)

Session 3 (2 Hours): Working with RDDs (Resilient Distributed Datasets)

Creating and Manipulating RDDs

Performing Transformations and Actions on RDDs

Understanding Partitioning and Persistence in RDDs

Session 4 (2 Hours): Introduction to Spark DataFrames

Creating and Using DataFrames in Spark

DataFrame Operations and SQL Queries

Data Aggregation and Grouping Operations

Session 5 (2 Hours): Advanced DataFrame Operations

Advanced Data Processing Techniques

Working with Various Data Formats (JSON, CSV, Parquet)

Data Importing/Exporting Techniques in Spark

Week 3: Spark for Advanced Analytics (6 Hours)

Session 6 (2 Hours): Spark SQL for Big Data Analysis

Using Spark SQL for Complex Queries

Integrating SQL and DataFrame API

Exploring Spark SQL’s Optimization Techniques

Session 7 (2 Hours): Machine Learning with Spark MLlib

Introduction to Spark’s Machine Learning Library (MLlib)

Building Basic Machine Learning Models in Spark

Evaluating Model Performance

Session 8 (2 Hours): Streaming Data Analysis with Spark Streaming

Basics of Real-Time Data Processing

Building Streaming Applications in Spark

Integrating Streaming Data with Static Data Sources

Week 4: Business Applications and Capstone Project (4 Hours)

Session 9 (2 Hours): Applying Spark in Business Contexts

Case Studies: Real-World Applications of Spark in Business

Best Practices for Leveraging Spark for Business Insights

Discussing Ethical and Privacy Considerations in Big Data

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Developing a Comprehensive Big Data Project Using Apache Spark

Presentation of Capstone Projects

Course Summary and Pathways for Further Learning

The course should be a mix of theoretical explanations, demonstrations, and hands-on exercises, ideally using a cloud-based Spark environment like Databricks for practical sessions. The capstone project in the final week would allow students to apply their learning to a real-world business dataset, ensuring they understand how to use Apache Spark for big data analysis effectively in a business context.

Week 1: Introduction to Looker and Basic Concepts (4 Hours)

Session 1 (2 Hours): Overview of Looker and Business Intelligence

Introduction to Business Intelligence and Looker's Role

Navigating the Looker Interface

Overview of Key Features and Functionalities

Session 2 (2 Hours): Connecting Data and Basic Reporting

Setting Up and Connecting Data Sources

Creating Basic Reports (Looks) and Exploring Data

Introduction to LookML (Looker Modeling Language)

Week 2: Advanced Reporting and Visualization (6 Hours)

Session 3 (2 Hours): Advanced Reporting Techniques

Building More Complex Reports and Dashboards

Exploring Advanced Visualization Options

Utilizing Filters, Parameters, and Derived Tables

Session 4 (2 Hours): Interactive Dashboards and Data Exploration

Designing Interactive and Dynamic Dashboards

Best Practices in Dashboard Layout and User Experience

Analyzing Data Trends and Patterns

Session 5 (2 Hours): Data Exploration and Analytics

Drill-Downs and Detailed Data Analysis

Utilizing Looker for Business Analytics

Sharing Insights and Collaborative Features

Week 3: LookML and Data Modeling (6 Hours)

Session 6 (2 Hours): Introduction to LookML

Basics of LookML and its Role in Looker

Creating and Managing LookML Models

Defining Dimensions, Measures, and Views

Session 7 (2 Hours): Advanced LookML Features

Advanced Model and View Development

Utilizing LookML for Complex Data Relationships

Implementing Data Governance in Looker

Session 8 (2 Hours): Hands-on LookML Workshop

Practical Exercise in Building and Optimizing a LookML Model

Troubleshooting Common Issues in LookML

Best Practices for Scalable and Maintainable Models

Week 4: Business Application and Capstone Project (4 Hours)

Session 9 (2 Hours): Looker in the Business Context

Case Studies: Real-world Applications of Looker in Various Industries

Strategic Decision-Making with Looker

Discussing Ethical Considerations in Data Analysis

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Developing a Comprehensive Business Analytics Project using Looker

Presentation and Critique of Capstone Projects

Course Summary and Future Learning Pathways

The course should be a mix of lectures, demonstrations, hands-on exercises, and case studies. The capstone project in the final week would allow students to apply their learning to develop a full-scale business analytics project, ensuring they understand not only how to use Looker but also how to apply it strategically in a business context.

Week 1: Introduction to Big Data and Apache Spark (4 Hours)

Session 1 (2 Hours): Fundamentals of Big Data

Introduction to Big Data: Concepts and Relevance in Business

Big Data Challenges and Technologies

Overview of the Big Data Ecosystem

Session 2 (2 Hours): Getting Started with Apache Spark

Introduction to Apache Spark and its Advantages

Understanding Spark’s Architecture and Components

Setting Up a Spark Environment (e.g., Databricks or Local Setup)

Week 2: Spark RDDs and DataFrames (6 Hours)

Session 3 (2 Hours): Working with RDDs (Resilient Distributed Datasets)

Creating and Manipulating RDDs

Performing Transformations and Actions on RDDs

Understanding Partitioning and Persistence in RDDs

Session 4 (2 Hours): Introduction to Spark DataFrames

Creating and Using DataFrames in Spark

DataFrame Operations and SQL Queries

Data Aggregation and Grouping Operations

Session 5 (2 Hours): Advanced DataFrame Operations

Advanced Data Processing Techniques

Working with Various Data Formats (JSON, CSV, Parquet)

Data Importing/Exporting Techniques in Spark

Week 3: Spark for Advanced Analytics (6 Hours)

Session 6 (2 Hours): Spark SQL for Big Data Analysis

Using Spark SQL for Complex Queries

Integrating SQL and DataFrame API

Exploring Spark SQL’s Optimization Techniques

Session 7 (2 Hours): Machine Learning with Spark MLlib

Introduction to Spark’s Machine Learning Library (MLlib)

Building Basic Machine Learning Models in Spark

Evaluating Model Performance

Session 8 (2 Hours): Streaming Data Analysis with Spark Streaming

Basics of Real-Time Data Processing

Building Streaming Applications in Spark

Integrating Streaming Data with Static Data Sources

Week 4: Business Applications and Capstone Project (4 Hours)

Session 9 (2 Hours): Applying Spark in Business Contexts

Case Studies: Real-World Applications of Spark in Business

Best Practices for Leveraging Spark for Business Insights

Discussing Ethical and Privacy Considerations in Big Data

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Developing a Comprehensive Big Data Project Using Apache Spark

Presentation of Capstone Projects

Course Summary and Pathways for Further Learning

The course should be a mix of theoretical explanations, demonstrations, and hands-on exercises, ideally using a cloud-based Spark environment like Databricks for practical sessions. The capstone project in the final week would allow students to apply their learning to a real-world business dataset, ensuring they understand how to use Apache Spark for big data analysis effectively in a business context.

Week 1: Introduction to Looker and Basic Concepts (4 Hours)

Session 1 (2 Hours): Overview of Looker and Business Intelligence

Introduction to Business Intelligence and Looker's Role

Navigating the Looker Interface

Overview of Key Features and Functionalities

Session 2 (2 Hours): Connecting Data and Basic Reporting

Setting Up and Connecting Data Sources

Creating Basic Reports (Looks) and Exploring Data

Introduction to LookML (Looker Modeling Language)

Week 2: Advanced Reporting and Visualization (6 Hours)

Session 3 (2 Hours): Advanced Reporting Techniques

Building More Complex Reports and Dashboards

Exploring Advanced Visualization Options

Utilizing Filters, Parameters, and Derived Tables

Session 4 (2 Hours): Interactive Dashboards and Data Exploration

Designing Interactive and Dynamic Dashboards

Best Practices in Dashboard Layout and User Experience

Analyzing Data Trends and Patterns

Session 5 (2 Hours): Data Exploration and Analytics

Drill-Downs and Detailed Data Analysis

Utilizing Looker for Business Analytics

Sharing Insights and Collaborative Features

Week 3: LookML and Data Modeling (6 Hours)

Session 6 (2 Hours): Introduction to LookML

Basics of LookML and its Role in Looker

Creating and Managing LookML Models

Defining Dimensions, Measures, and Views

Session 7 (2 Hours): Advanced LookML Features

Advanced Model and View Development

Utilizing LookML for Complex Data Relationships

Implementing Data Governance in Looker

Session 8 (2 Hours): Hands-on LookML Workshop

Practical Exercise in Building and Optimizing a LookML Model

Troubleshooting Common Issues in LookML

Best Practices for Scalable and Maintainable Models

Week 4: Business Application and Capstone Project (4 Hours)

Session 9 (2 Hours): Looker in the Business Context

Case Studies: Real-world Applications of Looker in Various Industries

Strategic Decision-Making with Looker

Discussing Ethical Considerations in Data Analysis

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Developing a Comprehensive Business Analytics Project using Looker

Presentation and Critique of Capstone Projects

Course Summary and Future Learning Pathways

The course should be a mix of lectures, demonstrations, hands-on exercises, and case studies. The capstone project in the final week would allow students to apply their learning to develop a full-scale business analytics project, ensuring they understand not only how to use Looker but also how to apply it strategically in a business context.

Week 1: Introduction to R and Basic Concepts (4 Hours)

Session 1 (2 Hours): Getting Started with R

Introduction to R and its Importance in Data Science

Setting Up the R Environment (R and RStudio Installation)

Basic Syntax, Variables, and Data Types in R

Session 2 (2 Hours): Data Manipulation Basics

Reading and Writing Data in R

Introduction to Data Manipulation with dplyr

Basic Data Cleaning Techniques

Week 2: Data Analysis and Visualization in R (6 Hours)

Session 3 (2 Hours): Exploratory Data Analysis (EDA)

Conducting EDA with R

Descriptive Statistics and Summarization

Handling Missing Values and Outliers

Session 4 (2 Hours): Data Visualization with ggplot2

Basics of ggplot2 for Data Visualization

Creating Various Types of Plots (Bar, Line, Scatter, Histogram)

Customizing Plots for Clarity and Aesthetics

Session 5 (2 Hours): Advanced Data Visualization

Advanced ggplot2 Features

Interactive Visualization with Plotly

Creating Dashboards and Reports

Week 3: Statistical Modeling and Machine Learning in R (6 Hours)

Session 6 (2 Hours): Introduction to Statistical Modeling

Linear Regression Analysis

Logistic Regression for Categorical Data

Model Diagnostics and Interpretation

Session 7 (2 Hours): Machine Learning Basics in R

Introduction to Machine Learning with R

Building Classification and Regression Models

Evaluating Model Performance

Session 8 (2 Hours): Advanced Topics in Machine Learning

Decision Trees and Random Forests

Clustering Techniques (k-means, Hierarchical)

Introduction to Text Mining and Sentiment Analysis

Week 4: Business Applications and Capstone Project (4 Hours)

Session 9 (2 Hours): R in Business Contexts

Case Studies: Real-World Applications of R in Business

Data-Driven Decision-Making in Business

Ethical Considerations in Data Science

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Applying R Skills to a Business-Related Data Science Project

Presentation and Discussion of Capstone Projects

Course Summary and Recommendations for Further Learning

The course should include a mix of theoretical instruction, practical demonstrations, and hands-on exercises using R. The capstone project in the final week should involve applying R skills to a real-world business problem, enabling students to demonstrate their ability to use R for data-driven decision-making in a business context.

Week 1: Introduction to SAS and Basic Data Handling (4 Hours)

Session 1 (2 Hours): Introduction to SAS

Overview of SAS Software: History and Industry Applications

Navigating the SAS Environment (SAS Studio, SAS Enterprise Guide)

Basic Syntax and Commands in SAS

Session 2 (2 Hours): Data Management Basics in SAS

Importing and Exporting Data in SAS

Data Manipulation Techniques (SORT, SET, MERGE)

Introduction to SAS Libraries and Data Sets

Week 2: Data Analysis and Reporting in SAS (6 Hours)

Session 3 (2 Hours): Descriptive Statistics and Data Summarization

Generating Descriptive Statistics in SAS (MEANS, FREQ, SUMMARY)

Data Summarization Techniques

Creating Basic Reports and Outputs

Session 4 (2 Hours): Data Visualization in SAS

Introduction to SAS Graphical Procedures (SGPLOT, SGPANEL)

Creating Charts and Graphs for Data Presentation

Customizing Visual Outputs in SAS

Session 5 (2 Hours): Advanced Data Analysis Techniques

Conducting Correlation and Regression Analysis

ANOVA and Other Statistical Tests

Introduction to Predictive Modeling in SAS

Week 3: Advanced SAS Programming and Analytics (6 Hours)

Session 6 (2 Hours): Advanced Data Manipulation

Advanced SAS Functions and Procedures

Data Cleaning and Preprocessing Techniques

Working with Dates and Times in SAS

Session 7 (2 Hours): SQL Programming in SAS

Introduction to PROC SQL for Data Querying

Combining SAS Datasets with SQL Joins

Advanced SQL Queries in SAS

Session 8 (2 Hours): Macro Programming in SAS

Basics of SAS Macro Language

Automating Tasks with Macros

Building and Using Macro Variables

Week 4: Business Applications and Capstone Project (4 Hours)

Session 9 (2 Hours): SAS in Business Contexts

Case Studies: SAS Applications in Finance, Marketing, and Operations

Discussing Ethical Implications of Data Analytics

Integrating SAS Analysis into Business Decision-Making

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Applying SAS Skills to a Real-World Business Problem

Presenting Capstone Project Findings

Course Summary and Recommendations for Continued Learning