Practical Data Science

Practical Data Science, available at $29.99, has an average rating of 3.9, with 48 lectures, based on 34 reviews, and has 809 subscribers.

You will learn about Understand the entire Data Science Process Use Python and its Scientific Libraries: Pandas, NumPy, StatsModels and more Put Theory and Concepts into action through Practical Application Use various Statistical Methods to Extract useful Information from Data Hands on Experience with handling Big Data This course is ideal for individuals who are Junior Data Scientist or Statistical Analyst or Data Analyst or This course is suited for individuals who want to advance their career in data science or data analytics It is particularly useful for Junior Data Scientist or Statistical Analyst or Data Analyst or This course is suited for individuals who want to advance their career in data science or data analytics.

Enroll now: Practical Data Science

Summary

Title: Practical Data Science

Price: $29.99

Average Rating: 3.9

Number of Lectures: 48

Number of Published Lectures: 41

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 41

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the entire Data Science Process

Use Python and its Scientific Libraries: Pandas, NumPy, StatsModels and more

Put Theory and Concepts into action through Practical Application

Use various Statistical Methods to Extract useful Information from Data

Hands on Experience with handling Big Data

Who Should Attend

Junior Data Scientist

Statistical Analyst

Data Analyst

This course is suited for individuals who want to advance their career in data science or data analytics

Target Audiences

Junior Data Scientist

Statistical Analyst

Data Analyst

This course is suited for individuals who want to advance their career in data science or data analytics

“Junior Level Data Scientist Median Salary from $91,000 and up to $250,000“.

As an experienced Data Analyst I understand the job market and the expectations of employers. This data science course is specifically designed with those expectations and requirements in mind. As a result you will be exposed to the most popular data mining tools, and you will be able to leverage my knowledge to jump start (or further advance) your career in Data Science.

You do not need an advanced degree in mathematics to learn what I am about to teach you. Where books and other courses fail, this data science course excels; that is each section of code is broken down through the use of Jupyter and explained in a easy to digest manner. Furthermore, you will get exposed to real data and solve real problems which gives you valuable experience!

Course Curriculum

Chapter 1: What is Data Science?

Lecture 1: Introduction

Lecture 2: The Process

Chapter 2: Python Basics

Lecture 1: Python Installation

Lecture 2: Jupyter (formerly iPython) Introduction

Lecture 3: NumPy

Lecture 4: Matplotlib

Lecture 5: Pandas

Chapter 3: Statistical Methods → Data Summarization

Lecture 1: Data Types (Part 1) — Identifying Types of Variables

Lecture 2: Data Types (Part 2) — Summarizing Variables Numerically

Lecture 3: Descriptive Statistics (in Python)

Lecture 4: Descriptive Statistics (in Excel)

Lecture 5: Descriptive Statistics (in SAS)

Chapter 4: Statistical Methods → Exploratory Data Analysis

Lecture 1: Analyzing Individual Variables — Histograms

Lecture 2: Analyzing Individual Variable — Probability Mass Functions

Lecture 3: Analyzing Individual Variable — Cumulative Distribution Functions

Lecture 4: Probability Density Functions & Modelling Empirical Distribution

Lecture 5: Smoothing Variable Distribution — Kernel Density Estimation

Lecture 6: Relationship Between Two Variables — Box Plots

Lecture 7: Relationship Between Two Variables — Scatter Plots

Lecture 8: Relationship Between Two Variables — Correlation & Covariance

Lecture 9: Bivariate Relationship Between Categorical Variables

Chapter 5: Exploratory Data Analysis (EDA) → Practical Example

Lecture 1: Exploratory Data Analysis of The Titanic Disaster

Chapter 6: Statistical Methods → Statistical Analysis

Lecture 1: Central Limit Theorem

Lecture 2: Estimation

Lecture 3: Linear Algebra and Matrices — Basics

Lecture 4: Linear Algebra and Matrices — Summary Statistics

Lecture 5: Parametric Statistical Analysis — Linear Response Models

Lecture 6: Linear Regression

Lecture 7: Linear Algebra and Matrices — Ordinary Least Squares

Chapter 7: Application of Statistical Methods

Lecture 1: Multiple Regression (in Excel)

Lecture 2: Linear Regression (in Python)

Lecture 3: Multiple Regression (in Python)

Chapter 8: Information Retrieval Using Query Language

Lecture 1: Getting Started with SQL

Lecture 2: CREATE TABLE Statement — Creating a Table in Database

Lecture 3: SELECT & LIMIT — Selecting Data from Database

Lecture 4: ORDER BY — Sorting Query Output

Lecture 5: GROUP BY — Grouping Output

Chapter 9: Big Data

Lecture 1: Data Integration — Introduction to HDF (Hierarchical Data Format)

Lecture 2: Data Integration — A Practical Example

Lecture 3: Data Integration — A Practical Example (Update)

Chapter 10: Data Science for Business & Marketing

Lecture 1: Product Promotion (in Python)

Instructors

Atul Bhardwaj
Data Analyst

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 8 votes

4 stars: 8 votes

5 stars: 13 votes

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!