Problem Solving using PySpark Regression Classification

Problem Solving using PySpark – Regression & Classification, available at $19.99, has an average rating of 5, with 35 lectures, based on 1 reviews, and has 14 subscribers.

You will learn about Data analysis and descriptive statistics with PySpark – Learning to compute essential descriptive statistics for data understanding and summarization Data Cleaning with PySpark Predictive modeling with PySpark using Regression Applying Classification techniques to a real world problem in PySpark Text analytics using PySpark and Spark NLP Time-Series modeling with PySpark and Prophet Introduction to Spark SQL for data querying This course is ideal for individuals who are This course is suited for anyone interested in the realm of analytics using PySpark – particularly useful for analysts and engineers interested in Big Data, someone with a basic knowledge of data science and ML principles It is particularly useful for This course is suited for anyone interested in the realm of analytics using PySpark – particularly useful for analysts and engineers interested in Big Data, someone with a basic knowledge of data science and ML principles.

Enroll now: Problem Solving using PySpark – Regression & Classification

Summary

Title: Problem Solving using PySpark – Regression & Classification

Price: $19.99

Average Rating: 5

Number of Lectures: 35

Number of Published Lectures: 35

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

Data analysis and descriptive statistics with PySpark – Learning to compute essential descriptive statistics for data understanding and summarization

Data Cleaning with PySpark

Predictive modeling with PySpark using Regression

Applying Classification techniques to a real world problem in PySpark

Text analytics using PySpark and Spark NLP

Time-Series modeling with PySpark and Prophet

Introduction to Spark SQL for data querying

Who Should Attend

This course is suited for anyone interested in the realm of analytics using PySpark – particularly useful for analysts and engineers interested in Big Data, someone with a basic knowledge of data science and ML principles

Target Audiences

This course is suited for anyone interested in the realm of analytics using PySpark – particularly useful for analysts and engineers interested in Big Data, someone with a basic knowledge of data science and ML principles

This course is based on real world problems in PySpark, surrounding Data Cleaning, Descriptive statistics, Classification and Regression Modeling.

The first segment introduces descriptive statistics in PySpark and computing fundamental measures such as mean, standard deviation and generating an extended statistical summary.

The second segment is based on cleaning the data in PySpark, working with null values,  redundant data and imputing the null values.

The third segment is about Predictive modeling with PySpark using Gradient Boosted Trees Regression

The fourth and fifth segments  are based on applying classification techniques in PySpark. The fourth Segment introduces the application of Spark XGB Classifier for a classification problem and the fifth segment is about using a deep learning model for text sentiment classification.

The sixth segment is about time series analytics and modeling using PySpark and Prophet

The seventh segment introduces  Spark SQL for data querying and analysis.

These segments also include advanced visualization techniques through Seaborn and Plotly libraries including  Box plots to understand the distribution of the data and assessment of outliers, Count plots to understand balance in the proportion of data, Bar chart to represent feature importance as part of the Gradient Boosted Trees Regression Model, Word Cloud for text analytics and analyzing time series data to extract seasonality and trend components.

Each of these segments, has a Google Colab notebook included aligning with the lecture.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Problem Solving with PySpark : Regression and Classification

Chapter 2: Data analysis and descriptive statistics with PySpark

Lecture 1: Setting up PySpark Environment in Google Colab

Lecture 2: Understanding Descriptive Statistics in PySpark

Lecture 3: Understanding Data Filtering and Slicing in PySpark

Lecture 4: Summary of Descriptive Statistics in PySpark and Quiz

Chapter 3: Data Cleaning with PySpark

Lecture 1: Introduction to Data Cleaning with PySpark

Lecture 2: Setting up PySpark Environment for Data Cleaning on Google Colab

Lecture 3: Understanding the Dataset : Explanatory Analysis and Data Cleaning with PySpark

Lecture 4: PySpark Data Cleaning : Assessment of Null Values and Outliers

Lecture 5: Data Cleaning with PySpark : Imputation Strategy Quiz

Lecture 6: Introduction to Pivot Tables in PySpark

Chapter 4: Predictive modeling with PySpark using Regression

Lecture 1: Introduction to Regression and Classification Problems in PySpark

Lecture 2: Understanding the Data Set through Explanatory Analysis

Lecture 3: Correlation Analysis and Data Preparation

Lecture 4: Modeling the data using Gradient Boosted Trees Regression

Lecture 5: Understanding Feature Importance

Lecture 6: Gradient Boosted Trees Regression – Quiz

Chapter 5: Predictive Modeling with PySpark using Classification

Lecture 1: Classification Problem Statement : Supervised Machine Learning

Lecture 2: Data Cleaning and Preparation for XGBoost Classification Model

Lecture 3: XGBoost Classification Model Pipeline using PySpark

Lecture 4: Summary of the segment on Spark XGBoost Classifier

Chapter 6: Text analytics using PySpark and Spark NLP

Lecture 1: Classification Model for Text Data

Lecture 2: Understanding the Data for Text Classification

Lecture 3: Word Cloud : Text Analytics Quiz

Lecture 4: Spark NLP Pipeline : Classification Model

Chapter 7: Time Series Analysis and Forecast with PySpark and Prophet

Lecture 1: Introduction to Time Series Analysis : Setting up the Google Colab Notebook

Lecture 2: Explanatory Analysis and Data Cleaning

Lecture 3: Analysis of time series components using advanced visualization techniques

Lecture 4: Use of Prophet Model for Time Series Forecasting

Lecture 5: Time Series Forecasting – Quiz

Chapter 8: Introduction to Spark SQL

Lecture 1: Introduction to Spark SQL Querying

Lecture 2: Comparison of PySpark statements and Spark SQL Query

Lecture 3: Join in Spark SQL

Lecture 4: Join in Spark SQL – Quiz

Instructors

Sathish Jayaraman
PySpark – Data Cleaning

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