Curso Data Science & Business Analytics

1. Data Science Fundamentals

• Data Science Definition /Applications
• Data Mining main branches/techniques
• Python Fundamentals – Key concepts
• Data Understanding
• Data loading and cleansing, Plotting, Feature extraction

2. Databases Fundamental

• Database concept
• Datawarehouse concept
• Relational and non-relational
• Major providers
• Architecture, Models, Definition
• Types of Data Models
• Fact and Dimension tables, Keys
• Star and Snowflake schema
• SQL Fundamentals, Key concepts

3. Big Data Technologies

• Big Data Ecossystem
• Introduction to Big Data
• Defining a Big Data Cluster
• Distributed Query SQL Query Engine
• Distributed Data Processing
• Distributed Processing with Spark
• Data Pipelines

4. Business Analytics

• Business Analytics
• Introdução ao Google Analytics – overview do software
• Diferentes soluções/ferramentas de Web Analytics para além do Google Analytics
• Perspetivas de métricas e dimensões (KPIs técnicos, quantitativos e qualitativos)
• Objetivos e KPI’s (O que são? Para que servem? Exemplos de utilização)
• Account e Tracking Code – o que é e onde encontrar, como criar e instalar
• Análise dos vários painéis (público-alvo, aquisição, comportamento, conversões e real-time)
• Relatórios personalizados
• Dashboards (com Data Studio)
• Funis de conversão: O que são? Qual a sua importância?Otimização de um funil de conversão
• Perceber para onde fogem as visitas? Porquê? Como evitar
• Segmentos avançados. Qual a utilidade? Como se criam
• UTMS – o que são? Para que servem?

5. Analyzing & Visualizing Data

• Introduction to Information Visualisation
• The role of visualisation in Data Science
• Good/Bad examples
• Plotting with python
• Visualisations (and dashboards) for different audiences
• Building a dashboard with python

6. Exploratory Data Analysis

• Types of data
• Descriptive Statistics
• Dirty & missing data
• Cleaning data – techniques and tools
• Visual exploration

7. Quantitative & Statistical Analysis and Modeling

• Quantitative Data Definition
• Types of Quantitative Data
• Collection Methods
• Analysis Methods
• Steps to conduct Quantitative Data Analysis
• Quantitative Data Examples
• Advantages & Disadvantages of Quantitative Data
• Differences between Quantitative and Qualitative Data

8. Machine Learning Models

• Introduction to Machine
• Learning Models
• Supervised Learning: Regression
• Supervised Learning:
• Classification
• Machine Learning: Advanced Concepts
• Unsupervised Learning

9. Introduction to Neural Networks

• Introduction to Neural Networks
• Different Types of Neural Networks
• Some Selected Examples

10. Applied Practice

• Identify a problem
• Obtain the data
• Understand the data
• Prepare, clean, and format the data
• Analyze the data
• Create visualizations or a dashboard to display insights both numerically and graphically.
• Present high level insights and the resulting actions to key stakeholders