DA 360: Complete Data Analytics Program

Explore how data is used to inform decision-making in various sectors

Learn the interface and basic functionality of tools like Excel

Structure messy datasets for analysis through formatting and standardization

Learn how to inspect and prepare unstructured data for analysis

Apply logical, mathematical and lookup functions to solve problems.

Bring in data from external sources and files using connectors

Automate data preparation tasks using Power Query Editor

Understand how to link multiple datasets using relationships

Learn how relationships affect filtering and summarization.

Summarize and filter data dynamically using pivot tools

Visualize insights using different types of charts.

Build custom calculations for advanced analytics

Design interactive dashboards for dynamic reporting

Learn the interface and basic functionality of tools like Excel

Learn how databases store structured data

Get familiar with SQL structure and types like VARCHAR, INT, etc

Retrieve and filter data from databases.

Use logical and comparison operators effectively

Create flexible queries using built-in functions and logic

Summarize data using count, average, min, max, etc.

Apply conditions to grouped data

Combine data across multiple tables

Build queries within queries for complex analysis

Combine or compare query results

Create reusable SQL code blocks

Perform advanced calculations across result sets.

Understand how visuals convey insights clearly.

Import and clean data for use in BI tools.

Combine and shape datasets effectively

Restructure data formats to suit reporting needs

Build models that support multi-source analysis.

Control data flow in visualizations.

Understand different ways to derive data fields.

Write custom logic using Data Analysis Expressions.

Calculate moving averages, YTD totals, and time trends.

Choose the best visuals for each kind of data story.

Apply consistency and clarity in reports

Use charts to monitor key performance metrics.

1. Deploy reports for end-user consumption.

Understand how Python enables end-to-end data analysis.

Work with strings, lists, dictionaries, and numeric types

Use logic and loops to automate processes.

Reuse and organize code efficiently.

Perform fast mathematical operations on arrays.

Summarize and analyze data distributions.

Use DataFrames for structured data manipulation

Tidy datasets by identifying and managing missing values

Combine and reshape datasets using Pandas

Build basic plots for data storytelling.

Create detailed statistical visuals with ease

Collect data from websites automatically

Extract and parse content from HTML pages.

Automate scraping of complex pages.

Analyze text data for sentiment and trends

Learn how statistics apply to real-world data analysis.

Summarize datasets using key measures.

Understand how randomness impacts data modeling.

Make inferences about populations from samples.

Design experiments to test business hypotheses.

Understand supervised vs. unsupervised learning

• Basic of python
• Operator in python
• History of pthon 
• Conditions
• Loops
• String operator 
• Logic bilding questions

• Oops in python
• basiv
• inhertence 
• traits
• abc