LIVE TRAINING

SAVE THE DATE: March 23rd, 2022 @ 2 pm EST

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Dr Singh, a fellow of the Royal Statistical Society (RSS), Associate Fellow at the Data Science Institutue, Imperial College London. Her specialties ranges from deep learning (Tensorflow, Keras), machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among many others.

LIVE TRAINING: Introduction to PYTHON for Programming 

October 12th @12 PM EST

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Price: $147

Regular price $210, discounted 10%
  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

30% Discount Ends in:

Subscribe and get an additional 10% to 35% off ALL live training session
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Pricing: $147

Regular price $210, discounted 30%
  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

30% Discount Ends in:

Subscribe and get an additional 10% to 35% off ALL live training session
View Plans

Meet Your Instructor

Minerva Singh 

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. As part of her research, she carried out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. These are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.

Course Overview

What’s the plan? 

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?).

This course will help you gain fluency in deploying data science-based BI solutions using a powerful clouded based python environment called GoogleColab. Specifically, you will 

  • Learn the main aspects of implementing a Python data science framework within  Google Colab
  • Learn to obtain both unstructured and structured data from different sources including SQL databases and freely available financial data
  • Implemented unsupervised learning algorithms to obtain insights from real-life business and financial datasets such as those related to stock market performance
  • Implement common statistical techniques to extract valuable insights and answer questions such as which customers are likely to sign up for a long-term phone plan or how do Airbnb rentals vary across the different cities in Australia. 
  • Implement powerful machine learning algorithms to build predictive and forecasting models

Course Outline

What’s the plan? 

Matt Harrison has been working with Python and data since 2000. He has a computer science degree from Stanford. He has worked at many amazing companies, created cool products, wrote a couple books, and taught thousands Python and Data Science. Currently, he is working as a corporate trainer, author, and consultant through his company Metasnake, which provides consulting and teaches corporations how to be effective with Python and data science.

Course Overview

Python is a powerful tools that XXX

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Course Outline

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

Module 2:
Data Pre-Processing  

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

Module 4:
Basic Statistics For Business Intelligence

  • 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
  • Python Data Quality Assessment
  • Python Data Cleaning
  • Retain Columns Without NA Values
  • Dealing With Missing Values
  • Data Imputation
  • Dealing With Dates
  • 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
  • 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

Module 6:
Machine Learning For Business Intelligence  

  • Components of Time Series- Theory
  • Basic time Series Forecasting
  • Forecasting With Machine Learning
  • Anomaly detection
  • Business Use Case Study: Will Bitcoin Touch $100K?
  • 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

Key on emojidex Key Details

DATE

TIME:

DURATION:

LEVEL:

MARCH 23rd, 2022

TIME: 2 PM EST, 11 AM PST

4 HOURS

INTERMEDIATE

Prerequisites

  • Prior exposure to python programming concepts 
  • Familiarity with Jupyter notebooks 
  • A desire to learn about practical data science applications for business intelligence

Upcoming Live Training

March 16th, 2022

Part 4. Natural Language Processing

 
This course is divided in 3 main topics: deep learning for NLP, modelling natural language data and recurrent and advanced neural networks. In the first topic, we will focus on easy, intermediate, and complex NLP applications, word vectors: representing language as embeddings and an interactive visualization of vector-space embeddings. In the second topic, the course will cover using word2vec to create word vectors and document classification with a dense neural network and convolutional neural network. In the final topic, you will learn about RNNs, LSTMs and transformers: BERT, ELMo & Friends
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Upcoming Live Training

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Open Data Science

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