Everyday buzzwords like "analytics," "insights" and "big data," permeate the pages of our business journals. Companies and departments are aware of their huge troves of data, and they have access to common tools for leveraging this data. However, much less available are the actual analysis skills to truly understand and realize the benefits of this information. The potential is very real, but comprehensive skills can be scarce, and outside consultants are expensive. If you have a basic familiarity with Excel, this three-day course can teach you practical applied analysis techniques to leverage data for relatively common decision-making methods.

‘Data Analytics’ this course, organized into key topic areas, leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality and how to translate data into the analysis of business problems to begin making informed intelligent decisions. Get an overview of data quality and data management, followed by foundational analysis and statistical techniques. Throughout the course, you will learn to communicate about data and findings to stakeholders who need to quickly make the decisions that drive your organization forward.

• Learn the data analysis process of wrangling, exploring, analyzing, and communicating data.
• Work with data in Python, using libraries like NumPy and Pandas, etc.

• What is Data Science
• Goal of DS
• DS Steps
• DS Workflow
• DS Skill
• DS Roles
• DS Tools
• Roles & Tools

• Process
• Interpret data
• Examples

• What is
• DA Compare with DS
• Types of DA
• Analytic Data
  -  Collect Data
  -  Analyze
  -  Make Decision
  -  Communicate
• Distributed Value
  -  Raw Data
  -  Dimension Data
  -  Data Attribute
• Examples

• Math in DS
• Probability
• Normal Distribution
• Examples

• Introduction to Python
  -  What is Python?
  -  Why Python
  -  Python Features
  -  Python Environments
  -  Python Installation & IDEs
• Python Syntax
  -  Data Types
  -  Python Operators
  -  Condition Statements
  -  Looping Statements
• Functions
  -  What is
  -  Benefits
  -  Syntax
  -  Types
  -  Design Functions
  -  Calling Function

• What is
• Benefits
• Steps

• What is 
• Why Use NumPy
• Installing NP
• use
• Create Array
• Accessing
• operations

• Matplotlib
  -  What is
  -  use for
  -  can 
  -  installation
  -  Types of Charts
  -  Examples
• Pandas
  -  What is
  -  use to
  -  Installation
  -  Ex
  -  Series
  -  Data Frame
  -  Reading Data
  -  Analyzing Data Frames
  -  operations
  -  Cleaning Data
  -  Plotting

• What is
• Why it important
• Dashboard vs Chart
• How to Create Dashboard

• Data Scientist
• Data Analyst
• IT Business Analyst
• Anyone involved in operations, project management, business analysis, or management who needs an introduction to Data Analysis

Basic Python