Data Analytics with R, Python and SQL

Data Analytics with R, Python and SQL, available at $44.99, with 33 lectures, and has 9 subscribers.

You will learn about Develop data analysis methods, approaches and handling business problems using data analysis as a toolset Uses R, Python, and SQL languages to implement the required statistical and mathematical methods to analyze practical datasets that are similar to the ones used Gain insights, trends, patterns, and predict future course based on the historical data and present the findings Work on a project that involves solving a business problem from start to finish; achieve end-to-end solution for the problem This course is ideal for individuals who are Intended for beginners and intermediate learners/professionals who want to get into data analytics field and to accelerate their journey It is particularly useful for Intended for beginners and intermediate learners/professionals who want to get into data analytics field and to accelerate their journey.

Enroll now: Data Analytics with R, Python and SQL

Summary

Title: Data Analytics with R, Python and SQL

Price: $44.99

Number of Lectures: 33

Number of Published Lectures: 33

Number of Curriculum Items: 52

Number of Published Curriculum Objects: 52

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Develop data analysis methods, approaches and handling business problems using data analysis as a toolset

Uses R, Python, and SQL languages to implement the required statistical and mathematical methods to analyze practical datasets that are similar to the ones used

Gain insights, trends, patterns, and predict future course based on the historical data and present the findings

Work on a project that involves solving a business problem from start to finish; achieve end-to-end solution for the problem

Who Should Attend

Intended for beginners and intermediate learners/professionals who want to get into data analytics field and to accelerate their journey

Target Audiences

Intended for beginners and intermediate learners/professionals who want to get into data analytics field and to accelerate their journey

This course focusses on data analytic methods and approaches for getting business solutions using R and Python and SQL. Link business needs to data analytics. The course views data analytics as a set of tools to bringing business questions and problems addressed. High level goals of the course include:

Provide coding examples to look at the data from multiple perspectives

Learn the technologies for analyzing any dataset and to derive information

Learn on how to get the stories from the data and support business in potential opportunities

Learn key areas of analysis for any data and make meaningful insights into the data

At the end of course students will be able to do:

Code in R and Python for any dataset

Dig into the data to derive useful information

Handle problems from real world in both research and business areas

Gather smaller chunks of information from each analysis step

Prepare next set of steps to dig deeper into the data for additional information

Visualize data and present to audience for providing at each step of a data analytics project

Update and address related problems during and after the project is done and as data changes

Iterate the steps to achieve greater visibility and verify and validate the information before publishing

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: What is Data Analytics

Lecture 1: What is Data Analytics

Lecture 2: Examples of Data Analytics

Lecture 3: Starting to Interpret the Data

Lecture 4: Using Basic Stats to interpret the Data

Lecture 5: Using charts to Interpret the Data

Chapter 3: R and Python

Lecture 1: Use of R vs Python for Data Analysis

Chapter 4: Working Environment

Lecture 1: Getting Ready to Code

Lecture 2: Writing Data from R to a File

Lecture 3: Preparing Working Environment

Chapter 5: Getting Data Summary and Observations

Lecture 1: Summary

Lecture 2: Data Observations

Lecture 3: Data Observations – Filtering the Data

Chapter 6: RMarkdown

Lecture 1: R Markdwon

Chapter 7: Statistical Measures

Lecture 1: Stats Measure

Chapter 8: Plots and Charts

Lecture 1: Charting and Plotting

Lecture 2: Box Plots – five metrics

Chapter 9: Correlation

Lecture 1: Correlation Coefficient

Chapter 10: Mosaic Plots

Lecture 1: Mosaic Plot Construction

Chapter 11: Pie Chart

Lecture 1: Pie Charting

Chapter 12: Scatter Plots

Lecture 1: Scatter Plotting

Chapter 13: Line Graph

Lecture 1: Line Graph

Chapter 14: Q-Q Plots

Lecture 1: Q-Q Plots – Quantile-Quantile plots

Chapter 15: Python Environment

Lecture 1: Python Environment

Lecture 2: Getting Started with Python

Chapter 16: Python and Plotting

Lecture 1: Working Python code from R Code

Lecture 2: Python Nulls and NAs

Lecture 3: Plotting in Python

Chapter 17: Project

Lecture 1: Project Work

Chapter 18: Database and SQL

Lecture 1: Database and Structured Query Language

Lecture 2: Getting to work with Python plus SQL

Lecture 3: GUI tool for MySQL database

Lecture 4: Using Python with SQL

Instructors

Ramesh Babu Paramkusham
Instructor at Udemy

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!