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 Data Analytics Training Course Schedule

• April 27, 2021 - May 20, 2021 US Pacific time
• 4 Weeks | 2 Hours on Tuesdays, 2 Hours on Thursdays every week US Pacific time
• 8:30 AM - 10:30 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 April 27, 2021 at 8:30 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