Data Analysis with Python (SKO 8102)

NOS 8102

This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! This NASSCOM approved course will satisfy NOS 8102, PCs 1-9.

N8102

PC1. Define the format and structure for the dataset

PC2. Define indexes and organize variables as per the defined format

PC3. Identify data types for each variable of the dataset

PC4. Identify and fix missing values in each variable of the dataset

PC5. Identify and fix incorrect data types in each variable of the dataset

PC6. Sort the data and create subsets of the data as required

PC7. Perform operations to transform data types of variables as required

PC8. Identify and deal with data redundancy by normalizing the dataset

PC9. Validate preprocessed data using appropriate tools and processes

Module 1 - Introduction Learning Objectives Understanding the Domain Understanding the Dataset Python package for data science Importing and Exporting Data in Python Basic Insights from Datasets

Module 2 - Data Wrangling Identify and Handle Missing Values Data Formatting Data NormalizationSets Binning Indicator variables

Module 3 - Exploratory Data Analysis Descriptive Statistics Basic of Grouping ANOVA Correlation Correlation 2

Module 4 - Model Development Simple and Multiple Linear Regression Model Evaluation using Visualization Polynomial Regression and Pipelines R-squared and MSE for In-Sample Evaluation Prediction and Decision Making

Module 5 - Working with Data in Python Model Evaluation Over Fitting, Under fitting and Model Selection Ridge Regression Grid Search Model Refinement