Visão Geral

O curso Data Analytics with IA foi desenvolvido para formar profissionais capazes de analisar, transformar e visualizar dados utilizando Excel, SQL, Python e Power BI — com o suporte de Inteligência Artificial para acelerar tarefas, criar códigos, detectar erros e automatizar processos analíticos.
Ao final, o aluno estará apto a realizar análises completas, construir dashboards, entregar projetos profissionais e se preparar para entrevistas técnicas.

Objetivo

Após realizar este curso Data Analytics with IA, você será capaz de:

• Analisar dados de maneira profissional utilizando Excel, SQL, Python e Power BI
• Usar Inteligência Artificial para acelerar análises, debugging e visualizações
• Criar dashboards completos e modelos de dados
• Realizar limpeza, transformação e exploração de dados
• Automatizar processos com IA, Kaggle, GitHub e Colab AI
• Dominar conceitos essenciais para entrevistas em Data Analytics

Publico Alvo

• Iniciantes em análise de dados
• Profissionais que desejam migrar para Data Analytics
• Analistas que querem incorporar IA em seus fluxos de trabalho
• Estudantes de TI, Engenharia, Administração, Finanças e áreas correlatas
• Pessoas que querem dominar Excel, SQL, Python e Power BI com foco em dados

Materiais

Conteúdo Programatico

Introduction to Excel for Data Analysis

1. Overview of Excel interface
2. Navigating sheets efficiently
3. Math & statistical functions (SUM, AVERAGE, COUNT)
4. Logical functions (IF, AND, OR)
5. Text functions for manipulation
6. VLOOKUP, VLOOKUP

Advanced Formulas & Dashboards Using Excel

1. INDEX MATCH
2. SUMIF, COUNTIFS, nested functions
3. Power Query: import, transform, merge, append
4. Dynamic dashboards
5. Pivot tables from multiple sources
6. Slicers, combo charts, layout optimization

Excel AI, Project, Kaggle & Github Introduction

1. AI-assisted data cleaning & transformation
2. Excel with AI project 
3. Kaggle and Github optimization

Introduction to SQL

1. Overview of SQL & databases
2. Basic syntax, SELECT, WHERE
3. Creating & modifying tables (CREATE/ALTER)
4. Understanding constraints

Aggregations & GROUP BY

1. COUNT, SUM, AVG, MIN, MAX
2. Filtering aggregates
3. GROUP BY, HAVING
4. ORDER BY, LIMIT, sorting
5. DISTINCT vs GROUP BY
6. LLM-powered optimization suggestions

Joins & Subqueries

1. Introduction to Joins
2. INNER, LEFT, RIGHT, FULL OUTER Joins & Self join
3. Deep Dive into Joins & Subquery Logic
4. Subqueries

Window Functions

1. Basic windows functions Aggregate functions
2. Rank functions( ROW_NUMBER(), RANK(), DENSE_RANK() ,PARTITION BY)
3. Advance windows functions (LAG(), LEAD(), SUM() OVER(), AVG() OVER())
4. AI error detection in analytical SQL

Data Cleaning & Recursive CTE’s

1. CTE ,SUBSTRING, LENGTH, TRIM, REPLACE
2. UPPER/LOWER
3. DATE_ADD, DATEDIFF, EXTRACT
4. AI-assisted SQL debugging

CASE WHEN, Optimization & Analytics

1. CASE WHEN & conditional logic
2. IF statements
3. Indexes, EXPLAIN plans
4. Funnel analysis, cohorts, retention

Python Fundamentals for Data Analysis

1. Introduction to python  & installation
2. Python basics, data types, variables
3. Loops (for, while)
4. Conditional statements (if, elif, else)

Python Fundamentals (Continued)

1. Lists, dictionaries, tuples
2. List comprehension ,operators
3. String methods,Indexing & slicing
4. Functions & error handling

Python Fundamentals (Continued)

1. Functions such as map,filter , lambda
2. Inbuilt functions len(), type(), sum(), sorted()
3. Working with external files
4. Using Colab AI for debugging and code generation

Pandas Data Cleaning

1. Data cleaning operations
2. String processing
3. DataFrames & Series
4. Missing values
5. Duplicate handling

Pandas Transformation

1. Data type conversions
2. Column renaming
3. Groupby, agg, apply
4. Pivot tables
5. Merging & joining
6. AI helpers for transformation scripts

NumPy & EDA

1. NumPy arrays and vectorization
2. Statistical operations
3. Outlier handling
4. EDA workflow

Visualization with Matplotlib, Seaborn & Plotly

1. Basic & advanced charts
2. Interactive visualizations
3. Customization & styling
4. AI-generated chart scripts

EDA Project (Python)

1. Data loading
2. Cleaning with Pandas
3. Visualizations

Python + AI EDA Project

1. Data acquisition
2. Cleaning & preparation
3. Numerical analysis
4. Visualization
5. AI-assisted EDA automation

Data Analysis with LLMs

1. How LLMs help in analysis
2. Pandas code generation
3. Cleaning assistance
4. Visualization generation

Power BI Fundamentals & Data Modeling

1. Power BI interface
2. Importing data
3. Relationships & schemas
4. Modeling best practices

DAX & KPIs

1. SUM, AVERAGE, COUNT, CALCULATE
2. SWITCH, FILTER
3. Time intelligence
4. Running totals & growth

Dashboard Design & ETL

1. Power Query for ETL
2. Visualization types
3. Interactive elements
4. Dashboard best practices

Interview Preparation

1. Resume building
2. LinkedIn optimization
3. Positioning your background
4. Identify target companies and roles

Presentation & Mock Interviews

1. STAR method
2. Project storytelling
3. Mock interviews
4. Common SQL interview patterns
5. Python coding (data manipulation, EDA) Interview Questions