Data Science 101- Methodology, Python, and Essential Math

Data Science 101: Methodology, Python, and Essential Math, available at $64.99, has an average rating of 4.1, with 185 lectures, 3 quizzes, based on 12 reviews, and has 107 subscribers.

You will learn about Explain data science methodology, starting with business understanding and ending at deployment Identify the various elements of machine learning and natural language processing involved in building a simple Chatbot Indicate how to create and work with variables, data structures, looping structures, decision structures, and functions. Recall the various functionality of the two main data science libraries: Numpy and Pandas Solve a system of linear equations Define the idea of a vector space Recognize the proper probability model for your use case Compute a least squares solution via pseudoinverse This course is ideal for individuals who are Beginners to Data Science or those interested in a data science career. or Individuals considering switching fields. or Individuals who want to get a big picture overview before focusing on specific Data Science topics. or You are interested in an Introduction to data science in Python. or You are interested in learning the essential math for data science. It is particularly useful for Beginners to Data Science or those interested in a data science career. or Individuals considering switching fields. or Individuals who want to get a big picture overview before focusing on specific Data Science topics. or You are interested in an Introduction to data science in Python. or You are interested in learning the essential math for data science.

Enroll now: Data Science 101: Methodology, Python, and Essential Math

Summary

Title: Data Science 101: Methodology, Python, and Essential Math

Price: $64.99

Average Rating: 4.1

Number of Lectures: 185

Number of Quizzes: 3

Number of Published Lectures: 185

Number of Published Quizzes: 3

Number of Curriculum Items: 189

Number of Published Curriculum Objects: 189

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Explain data science methodology, starting with business understanding and ending at deployment

Identify the various elements of machine learning and natural language processing involved in building a simple Chatbot

Indicate how to create and work with variables, data structures, looping structures, decision structures, and functions.

Recall the various functionality of the two main data science libraries: Numpy and Pandas

Solve a system of linear equations

Define the idea of a vector space

Recognize the proper probability model for your use case

Compute a least squares solution via pseudoinverse

Who Should Attend

Beginners to Data Science or those interested in a data science career.

Individuals considering switching fields.

Individuals who want to get a big picture overview before focusing on specific Data Science topics.

You are interested in an Introduction to data science in Python.

You are interested in learning the essential math for data science.

Target Audiences

Beginners to Data Science or those interested in a data science career.

Individuals considering switching fields.

Individuals who want to get a big picture overview before focusing on specific Data Science topics.

You are interested in an Introduction to data science in Python.

You are interested in learning the essential math for data science.

Welcome! Nice to have you. I’m certain that by the end you will have learned a lot and earned a valuable skill. You can think of the course as compromising 3 parts, and I present the material in each part differently. For example, in the last section, the essential math for data science is presented almost entirely via whiteboard presentation.

The opening section of Data Science 101 examines common questions asked by passionate learners like you (i.e., what do data scientists actually do, what’s the best language for data science, and addressing different terms (big data, data mining, and comparing terms like machine learning vs. deep learning).

Following that, you will explore data science methodology via a Healthcare Insurance case study. You will see the typical data science steps and techniques utilized by data professionals. You might be surprised to hear that other roles than data scientists do actually exist. Next, if machine learning and natural language processing are of interest, we will build a simple chatbot so you can get a clear sense of what is involved. One day you might be building such systems.

The following section is an introduction to Data Science in Python.You will have an opportunity to master python for data science as each section is followed by an assignment that allows you to practice your skills. By the end of the section, you will understand Python fundamentals, decision and looping structures, Python functions, how to work with nested data, and list comprehension. The final part will show you how to use the two most popular libraries for data science, Numpy, and Pandas.

The final section delves into essential math for data science. You will get the hang of linear algebra for data science, along with probability, and statistics. My goal for the linear algebra part was to introduce all necessary concepts and intuition so that you can gain an understanding of an often utilized technique for data fitting called least squares. I also wanted to spend a lot of time on probability, both classical and bayesian, as reasoning about problems is a much more difficult aspect of data science than simply running statistics.

So, don’t wait, start Data Science 101 and develop modern-day skills. If you should not enjoy the course for any reason, Udemy offers a 30-day money-back guarantee.

Course Curriculum

Chapter 1: Intro to Data Science 101

Lecture 1: Matching Activity – Match the Project to the Data Role

Lecture 2: Intro to Data Science

Lecture 3: What a Data Scientist Does?

Lecture 4: Big Data

Lecture 5: Data Mining

Lecture 6: Machine Learning vs. Deep Learning

Lecture 7: Advice to Data Scientists

Chapter 2: Best Language for Data Science?

Lecture 1: What IS the best language for Data Science?

Lecture 2: Python

Lecture 3: SAS

Lecture 4: R

Lecture 5: SQL

Chapter 3: Data Science Methodology

Lecture 1: Data Science Methodology/Process Intro

Lecture 2: Business Understanding

Lecture 3: Data Understanding

Lecture 4: Data Prep

Lecture 5: Modeling

Lecture 6: Evaluation

Lecture 7: Deployment

Chapter 4: Data Science Via Chatbot

Lecture 1: Purpose of Chatbot Section

Lecture 2: What is a Chatbot?

Lecture 3: Signing up for Watson Assistant

Lecture 4: Creating a name – Healthcare Service Chatbot

Lecture 5: Intents

Lecture 6: Entities

Lecture 7: Suggestions for More Learning

Lecture 8: Section Recap: Natural Language Processing , Machine Learning, and Use Cases

Chapter 5: Libraries, APIs, Datasets

Lecture 1: Libraries

Lecture 2: APIs

Lecture 3: Datasets

Chapter 6: Github

Lecture 1: Intro to Github

Lecture 2: Create a Repository

Lecture 3: Creating Branch and Commit Changes

Lecture 4: Pull Request and Merging Pull Request

Chapter 7: Introduction to Data Science in Python

Lecture 1: Welcome to the Python for Data Science and Machine Learning Section

Lecture 2: Options/Features When Watching Videos

Lecture 3: Resources (Data-sets and Notebooks)

Chapter 8: Installation/Jupyter/Comments (Windows and MacOS/Jupyter Notebook)

Lecture 1: Windows – Download Anaconda Distribution (includes Python!)

Lecture 2: Windows – Install Anaconda Distribution

Lecture 3: Windows – Setting Up Environment

Lecture 4: Windows – Opening Jupyter Notebook

Lecture 5: MacOS – Anaconda Download and Install

Lecture 6: MacOS – Conda Environment

Lecture 7: MacOS – Jupyter Notebook

Lecture 8: Jupyter Notebook Interface and Shortcuts

Chapter 9: Introduction to Data Science in Python – Python Fundamentals

Lecture 1: How to Use Markdown Cells (Adding Headers, Links, and Images)

Lecture 2: Comments – Inline and Block Comments

Lecture 3: Python Indentation

Lecture 4: Writing Single and Multiple Lines of Code

Lecture 5: Understanding Variables

Lecture 6: Main Data Types and Creating Them (Integer, Float, String, List, Dictionary)

Lecture 7: Lists – How To Use

Lecture 8: Dictionaries – How To Use

Lecture 9: Creating A Tuple

Lecture 10: Tuple – How To Use

Lecture 11: Creating a Set

Lecture 12: Set – How To Use

Lecture 13: Operators

Lecture 14: Fill in Activity 1 – Fundamentals

Chapter 10: Introduction to Data Science in Python – Decision and Looping Structures

Lecture 1: Introducing Decision and Looping Structures

Lecture 2: If statement

Lecture 3: Else Statement

Lecture 4: Elif

Lecture 5: For Loop

Lecture 6: While Loop

Lecture 7: Break and Continue Statements

Chapter 11: Introduction to Data Science in Python – Python Functions

Lecture 1: Introducing Functions

Lecture 2: Functions – General Syntax

Lecture 3: +1 Function

Lecture 4: Fav Band Function

Lecture 5: Celsius to Fahrenheit Function

Lecture 6: Optional Return Statement (and comparing it to Print Statement)

Lecture 7: Defining a Function vs. Calling a Function (including different ways to call)

Lecture 8: Practical/Real World Example: Function to Get Reddit Data

Lecture 9: Lambda Intro (Anonymous Functions)

Lecture 10: Formal Function vs. Lambda for splitting strings

Lecture 11: Fill in Activity 2 – Looping and Functions

Chapter 12: Introduction to Data Science – Nested Data, Iteration and List Comprehension

Lecture 1: Introducing you to Nested Data and Iteration

Lecture 2: Simple Nested Example

Lecture 3: Double Indexing

Lecture 4: Assigning Values

Lecture 5: List of Dicts and Dicts of Dicts Example

Lecture 6: Nested Iteration – Iterating through List of Lists

Lecture 7: Defining List Comprehension and Syntax

Instructors

Ermin Dedic
All Things Data.

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 4 votes

4 stars: 3 votes

5 stars: 4 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!