Python Programming and Data Exploration in Python

Program starts with basics of Python Programming and covers the essential programming knowledge required for conducting data analysis in Python, evolving into How to work with Data in Python and applying machine learning algorithms on data for analysing and visualizing data in python.

  Learning Objectives

1. Understanding Python, Python Installation, Python Interface and python IDE

2. Understanding and working with Python Constructs

        a. Jump/Branching

        b. Loops

        c. Functions

        d. Variables and their scope

        e. Modules

        f. Operators and expressions

3. Importing/Exporting data in python

4. Exceptions Handling in python

5. Collections and dictionaries

6. Object oriented programming in python

7.Data Modelling using Machine Learning Techniques

8.Data Visualization

Modules Covered:

• Programming in Python
• Working with Data in Python
• Data Modelling using Machine Learning
• Data Visualization

Case Studies: 

1. Case Study on online credit card fraud detection

Industry: Banking, Finance and Economics

Description: In this case study, we will focus on a particular form of credit card fraud—buying from an online store. We are assuming that for some of those transactions (of a higher value), some retailers require the customers to call in and confirm their credit card details. Then we identify the fraudulent merchant from the data provided, In order to catch the thief you need to find the merchant to which, all the affected parties shopped at, before the first fraudulent transaction occurred against their credit card.

Dataset: The dataset consists of data for 1,000 customers and 20 merchants. Over a period of 50 days, customers made over 225 K transactions for a total value of over $57 M.

2. Case Study on classifying the outbound call data of a bank

Industry: Banking, Telemarketing

Description: In this case study we will classify the outbound calls of a bank to see if such a call will result in a credit application or not using three most popular classification methods Gradient Boosting Naïve Bias, Generalized Linear Model and Random Forest. We will compare the performance of these methods using various performance and cost metrics for example, precision, recall, F1-score and Receiver Operating Characteristic (ROC).

Dataset: We will use the data related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The dataset has 45211 records across 17 attributes ordered by date (from May 2008 to November 2010).

3. Case Study on Forecasting River flow using Time Series models

Industry: Natural resource management

Description: We will see various techniques of handling, analyzing, and building models for time series data. We will use the autoregressive moving average (ARMA) model and its generalization—the autoregressive integrated moving average(ARIMA) model to predict the future from time series data.

Dataset: The datasets for this chapter come from the web archive of monthly river flows where in all the time series data is in chronological order (reading across).The river flow data units of measurement are cubic meters per second.

4. Case Study on Price Distribution Analysis of Sacramento’s Houses.

Industry: Real Estate, Sales

Description: In this case study we will process real estate transactions data of houses sold in Sacramento by imputing missing observations and normalizing and standardizing the features. Then we will investigate the correlations by calculating the Pearson, Kendall, and Spearman correlation between the features of interest. Lastly we will visualize the interactions between interesting features by creating, displaying, and saving histograms.

Data-set: The Data set used consists of 985 real estate sales transactions took place in the Sacramento area over a period of five consecutive days.

• Have basic knowledge of working in the Windows environment and Microsoft excel
• Knowledge of Maths/Statistics upto Class XII