Data Analytics Bootcamp

The energy data field focuses on the analysis and management of data related to energy production, consumption, and distribution. This field involves using data analytics to optimize energy systems, forecast energy demands, and improve the efficiency of renewable and non-renewable energy sources. Professionals in this field work to enhance energy sustainability and develop strategies for smarter energy use, crucial for addressing global energy challenges.

The Finance data field focuses on analyzing financial markets, assessing investment opportunities, and managing economic data. It encompasses everything from stock market trends to corporate finance and banking statistics, employing various tools and techniques to forecast financial outcomes and advise on risk management. This specialization is crucial for making informed decisions in the finance industry, helping professionals optimize portfolios and improve financial strategies.

Programming skills: 

• python

• SQL

Python related Packages:

• Beautiful Soup

• hbmqtt

• gmqtt

• mosquitto-py

• PyModbus

• Paho MQTT

Hardwares: 

• Raspberry Pi

• Current Sensors

• Voltage Sensors

• DHT11/DHT22 Temperature and Humidity Sensors

• IR Proximity Sensor Module 

• PIR Motion Sensor

• Ultrasonic Sensor

• LDR (Light Dependent Resistor)

• Hall Effect Sensor

• Gas Sensors (MQ series)

• Load Cells

• Capacitive Touch Sensor

• Data Cleaning

• Data Transformation

• Data Visualization

• Descriptive and Inferential Statistics 

• Correlation and Causation Analysis

• Dimensionality Reduction(PCA)

• Feature Engineering

• Anomaly Detection

• Pattern and Trend Identification

• Hypothesis Testing

• Time Series Analysis

• Geospatial Analysis

• Comparative Analysis

• Sustainability Analysis

Dashboard Tools & packages: 

• Plotly (Dash) 

• Streamlit

• Bokeh

• Tableau 

Specific methods in each dashboard: 

• Operational Dashboards

• Strategic Dashboards

• Analytical Dashboards

• Tactical Dashboards