Live training with Minerva Singh, PhD starts on March 23rd at 2 PM (EST)

Training duration: 4 hours (Hands-on)

Price with 30% discount

Regular Price: $210.00

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Instructor Bio:

Spatial Data Analysis Expert, Founder at Minerva's Data Lab

Minerva Singh, PhD

Minerva joined the Center for Environmental Policy (CEP), Imperial College London as a Research Fellow in 2018. Before that, she completed a PhD from the University of Cambridge in 2017 where she focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. She holds an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. She has nearly 10 year's experience in conducting academic research at the interface of tropical ecology, data science, earth observation (EO), and artificial intelligence (AI)and published 14 first-author peer-reviewed papers in international journals since 2013 including PLoS One. She has obtained research funding of approximately £275,000 since 2018 and provided research, consultancy, and data science support to startups and industry partners including Treeconomy and Morphobotics LLC. She is a Fellow of the Royal Geographic Society (RGS)and Royal Statistical Society (RSS) as well as serving as an Associate Fellow at the Data Science Institute, Imperial College London. She is also a best selling course-instructor on the online MOOC platform Udemy where she provides online teaching to more than 71,000 students from across the world on machine learning, earth observation, and deep learning related topic

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Learning Objectives

  • Obtaining and preprocessing real-life data

  • Develop powerful visualizations to identify the distributions and trends (including the spatial trends) in your data

  • Drawing business insights from basic statistical analysis and answer important business questions (how do AirBnB rentals vary across the different parts of Australia)

  • Time is money: Understand the temporal contex of your data and develop forests using time series models

  • Identify stock market clusters using unsupervised learning

  • Predict important business related values such as house prices using machine learning models

  • Identify the most important variables using machine learning


Course Outline

Module 1: Get Started With Business Intelligence (BI) using Python Data Science

* Training Overview

*How Data Science Can Give You An Edge In Your Business

*Get Your Python Environment Ready- Hello to Google Colab

*Introduction To Pandas

* Reading In Common Structured Data Formats in the Google Colab Environment 

* Read In Multiple CSVs

* Read In Data From SQL Databases

* Obtain Publicly Available (Financial) Data

Module 2:Data Pre-Processing

*Python Data Quality Assessment

* Python Data Cleaning

*Retain Columns Without NA Values

* Dealing With Missing Values

* Data Imputation

* Dealing With Dates

Module 3: Exploratory Data Analysis (EDA) With Data Visualizations

* Theoretical principles behind Data Visualizations

* What Are Histograms?

* What Are Barplots?

*Plotting Multi-Line Data

*Plotting Data By the Calender

*Geovisualization- identify the geographies of business 

* A Practical Business Problem Examination With Data Visualizations 

*Prettify Your Plots

Module 4: Basic Statistics For Business Intelligence

*  Correlation Analysis- theory

*  Correlation Analysis- Stock Market Correlations

* Crosstabulation

* Principal Component Analysis (PCA)- Theory

* Principal Component Analysis (PCA)- Practical Application

* Multiple Correspondence Analysis (MCA)- PCA On Qualitative Variables

*Principal Components When You Have Both Categorical and Quantitative Variables

* Case Study: Can you identify your best customers

Module 5: Basic Time Series Analysis

* Components of Time Series- Theory

* Basic time Series Forecasting

* Forecasting With Machine Learning

* Anomaly detection

* Business Use Case Study: Will Bitcoin Touch $100K?

Module 6: Machine Learning For Business Intelligence 

* Theory Of Unsupervised Learning

* Cluster your Stocks- K means Clustering

* Cluster your Stocks- Hierarchical Clustering

* Theory Of Supervised Learning

* Logistic Regression- Binary Outcomes

* Tree-Based Classifications For Multi-Class Classification

* Random Forest (RF) regressions

* Business Use Case Study: Predicting House Prices and Identifying the Most Important Drivers


This course is a hands-on training with real business related program - You will learn to use important Python data science techniques to derive information and insights from commonly used business data

This course provides a foundation to carry out practical real-life BI tasks using Python. By taking this course, you are taking an important step forward in your data science journey to become an expert in deploying Python data science techniques for answering practical business questions (e.g. what kind of customers sign up for a long-term phone plan?).

  • Are you interested in harnessing the power of data for informing business decisions?
  • Do you want to make data-driven financial decisions?
  • Do you want to develop cutting-edge analytics and visualizations to support business decisions?
  • Are you interested in deploying predictive modeling and forecasting methods to get an edge over the competition?

You Can Gain An Edge Over Other Data Scientists If You Can Apply Python Data Analysis Skills For Practical Business Intelligence (BI)

  • By enhancing the value of your company or business through the extraction of actionable insights from commonly used business data
  • Stand out from a pool of other data analysts by gaining proficiency in the most important pillars of BI applications

What background knowledge you should have?

  • Prior exposure to python programming concepts

  • Familiarity with Jupyter notebooks

  • A desire to learn about practical data science applications for business intelligence

What is included in your ticket?

  • Access to live training and QA session with the Instructor

  • Access to the on-demand recording

  • Certificate of completion

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