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Data Analytics 360

Module 1: Understanding and Visualizing Data

Gather and Qualify Data
Visualization and Analysis
Bring the Data into the Decision

Module 2: Implementing Scientific Decision Making

Define a Hypothesis
Test the Hypothesis
Testing and Conclusions

Module 3: Using Predictive Data Analysis

Discovering Relationships
Quantifying Impact
Assessing and Validating our Model
Applying the Predictive Analytics Framework

Module 4: Modeling Uncertainty and Risk

Making One-off and Repeating Decisions
Adjusting and Accounting for Risk
Using Monte Carlo Simulation for Nuanced Decision Making

Module 5: Optimization and Modeling Simultaneous Decisions

Using Optimization
Developing Nonlinear Models
Creating Non-continuous Models That Work

Data Analytics

Module 1: Introduction to Data Analytics

 What is Data Analytics
 Difference between Data Analysis and Data Analytics
 Why do We Analyze a Data
 Type of Analytics
 Tools

Module 2: Business Analytics with Excel

 Microsoft Excel fundamentals
 Entering and editing texts and formulae.
 Working with basic Excel functions.
 Inserting images and shapes into an Excel worksheet.
 Creating Basic charts in Excel.
 Importing and exporting data.
 Excel pivot tables.
 Conditional function.
 Lookup functions.
 Data Analysis Tools

Module 3: Tableau

 What is Data Visualization
 Working with Dimensions
 Data Management Filters
 Filters in Detail
 Advance Visuals and Features in Tableau

Module 4: Power BI

 Introduction to Power BI
 Getting and Transforming Data in Power Bi Desktop
 Modeling with Power BI
 DAX Function
 Visualization of Data
 Publishing Reports

5. Data Analytics with Python

 Intro to NUMPY.
 Arrays
 Series.
 Data frames.
 Reading and writing text files.
 Matplotlib
 Sci-Kit Learn
 Introduction to SQL with Python.
 SQL WILDCARDS, ORDER BY, GROUP BY, and Aggregate Functions.

6. SQL for Data Analytics

 Introduction
 ER Diagram.
 Schema Design.
 Normalization.
 SQL SELECT and its Functions
 SQL JOIN and its Function
 Sorting.
 Analytic function.
 Set operations.
 SQL views.
 SQL constraints
 SQL DDL and DML operation

7. Data Analyst Project.