16 Hours Only Data Analytics training is a 4 weeks long Instructor-led and guided training with Practical Hands-On Lab exercises to be taught over 4 Weeks, 2 sessions per week, 2 hours per session.

• The medium of instruction is English.
• All Published Ticket Prices are in US Dollars.

16 Hours Only Data Analytics Training Schedule

• December 22, 2020 - January 14, 2021 US Pacific time
• 16 Hours | 2 Hours on Tuesday, 2 Hours on Thursday every week US Pacific time
• 7:30 AM - 9:30 AM US Pacific time each of those days
• Please click here to add your city name and check your local date and time for the first session to be held on December 22, 2020 at 7.30 AM US Pacific Time.

Features and Benefits

• 4 weeks, 8 sessions, 16 hours of total Instructor-led and guided training
• Training material, instructor handouts and access to useful resources on the cloud provided
• Practical Hands-on Lab exercises provided
• Actual code and scripts provided
• Real-life Scenarios

What you will learn?

• What is Data Analytics
• How to prepare and analyze data
• When and How to use SQL for Data Analytics
• When and how to use Python and Pandas for Data Analytics
• Data Visualization

Who should attend

• Anyone who is interested in learning Data Analytics.

Prerequisites

• You don't any background in python or data analysis
• Basic computer literacy

Course Outline

1. Introduction to Data Analytics

2. Learn how to prepare and analyze data

3. Data Analytics Process

• Accessing Data
• Importing Data
• Understanding data and meaning of data
• Data Cleansing
• Data Transformation
• Preparing for Data Visualization

4. Data Analysis and Business

• Understanding business requirements and business rules
• Interpreting and preparing data according to business requirements

5. Applying Database and SQL Concepts to Data for Data Analytics

• Joins

6. Using Python and Pandas for Data Analytics

1) Python:

• Data Types
• Operators
• Decision Making
• Loops
• String,List,Tuple functions
• List Functions
• Tuple functions
• Functions
• Date and Time
• Regular Expressions

2) Pandas:

• Data Types
• Descriptive Statistics
• Function Table/row/column wise
• Iterating through Dataframe
• Sorting
• Series String Functions
• Indexing and Selecting data
• Group_By
• Merging of Data, Concatenation
• Date and Time
• SQL statements in Pandas

7. Data Visualization Methods and Techniques