Description

Start your data science journey with this carefully constructed comprehensive course and get hands-on experience with Python for data science. Gain in-depth knowledge about core Python and essential mathematical concepts in linear algebra, probability, and statistics. Complete data science training with 13+ hours of content.

The opening part of Data Science 101 examines some frequently asked questions. Following that, we will explore data science methodology with a case study. You will see the typical data science steps and techniques utilized by data professionals. Next, you will build a simple chatbot so you can get a clear sense of what is involved. The next part 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 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. Finally, we will wrap up the two most popular libraries for data science-NumPy and Pandas. The last part delves into essential math for data science. You will get the hang of linear algebra along with probability and statistics. Our goal for the linear algebra part is to introduce all necessary concepts and intuition for an in-depth understanding of an often-utilized technique for data fitting called least squares. We will spend a lot of time on probability, both classical and Bayesian, as reasoning about problems is a much more difficult aspect than simply running statistics. By the end of this course, you will understand data science methodology and how to use essential math in your real projects. All resources are available at https://github.com/PacktPublishing/Data-Science-101-Methodology-Python-and-Essential-Math

What You Will Learn

Examine frequent questions asked by passionate learners
Explore data science methodology with a healthcare insurance case study
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 through pseudoinverse

Audience

This course is designed for people who are new to data science or who are interested in pursuing a career in data science, as well as those who wish to obtain a broad overview before diving into specialized data science topics.

This course will also benefit students who want to master the fundamental arithmetic for data science or obtain an introduction to data science in Python.

You need not have any prior experience in data science to take up this course.

Approach

You will learn data science with Python and grasp how to use the key math for data science in this 101 data science course, which takes a practical approach. This course is packed with practice assignments and live demonstrations to help you hone your skills.

Key Features

Explain data science methodology, starting with business understanding and ending at deployment * Identify the various elements of ML and NLP involved in building a simple chatbot * Indicate how to create and work with variables, data structures, looping structures, and more

Github Repo

https://github.com/PacktPublishing/Data-Science-101-Methodology-Python-and-Essential-Math

About the Author

Ermin Dedic

Ermin Dedic started his studies by studying psychology for six years. He received his bachelor's degree from the University of Ottawa, Canada, and his master's degree from the University of Calgary, Canada. Ermin also spent two years in a master's program (school/child psychology) at the University of Calgary before voluntarily withdrawing, in part to focus more on his teaching. It was through academia that he was introduced to and fell in love with statistics and statistical programming with SAS. He is passionate about making education accessible and fun for students. Ermin believes that students learn better when they feel the passion that the instructor has for the content.

Course Outline

1. Introduction to Data Science 101

1. Matching Activity - Match the Project to the Data Role

In this video, we will cover a matching activity to match the project to the data role. Data scientist, data analyst, business analyst, and data engineer - these are the typical roles in the data science world. Do you know which position you want? Will this activity change your intentions for the future? Did you guess right?

2. Introduction to Data Science

In this video, we will cover an introduction to data science.

3. What a Data Scientist Does

In this video, we will understand what a data scientist does.

4. Big Data

In this video, we will cover Big Data.

5. Data Mining

In this video, we will cover data mining.

6. Machine Learning Versus Deep Learning

In this video, we will cover machine learning versus deep learning.

7. Advice to Data Scientists

In this video, we will cover some important advice to data scientists.

2. Best Language for Data Science

1. What IS the Best Language for Data Science?

In this video, we will understand what the best language for data science is.

2. Python

In this video, we will be introduced to Python.

3. SAS (Statistical Analysis System)

In this video, we will be introduced to SAS.

4. R

In this video, we will be introduced to R.

5. SQL

In this video, we will be introduced to SQL

3. Data Science Methodology

1. Data Science Methodology/Process Introduction

In this video, we will cover data science methodology/process introduction.

2. Business Understanding

In this video, we will cover business understanding.

3. Data Understanding

In this video, we will cover data understanding.

4. Data Prep

In this video, we will cover data prep.

5. Modelling

In this video, we will cover modelling.

6. Evaluation

In this video, we will cover evaluation.

7. Deployment

In this video, we will cover deployment

4. Data Science Through Chatbot

1. Purpose of Chatbot Section

In this video, we will cover the purpose of the chatbot section.

2. What is a Chatbot?

In this video, we will understand what a chatbot is.

3. Signing Up for Watson Assistant

In this video, we will be signing up for Watson Assistant.

4. Creating a Name - Healthcare Service Chatbot

In this video, you will learn how to create a name - healthcare service chatbot.

5. Intents

In this video, you will learn about intents.

6. Entities

In this video, you will learn about entities.

7. Suggestions for More Learning

In this video, we will share some suggestions for more learning.

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

In this video, we will do a quick section recap.

5. Libraries, APIs, Datasets

1. Libraries

In this video, we will cover libraries.

2. APIs

In this video, we will cover APIs.

3. Datasets

In this video, we will cover datasets.

6. GitHub

1. Introduction to GitHub

In this video, we will cover a quick introduction to GitHub.

2. Create a Repository

In this video, you will learn how to create a repository.

3. Create a Branch and Commit Changes

In this video, you will learn how to create a branch and commit changes.

4. Pull Request and Merging Pull Request

In this video, we will cover pull request and merging pull request.

7. Installation / Jupyter / Comments (Windows and MacOS/Jupyter Notebook)

1. Windows - Download Anaconda Distribution (Includes Python!)

In this video, we will work on Windows OS and download the Anaconda Distribution (including Python!).

2. Windows - Install Anaconda Distribution

In this video, we will work on Windows OS and install Anaconda Distribution.

3. Windows - Setting Up Environment

In this video, we will work on Windows OS and set up the environment.

4. Windows - Opening Jupyter Notebook

In this video, we will work on Windows OS and open Jupyter Notebook.

5. MacOS - Anaconda Download and Install

In this video, we will work on MacOS to download and install Anaconda.

6. MacOS - Conda Environment

In this video, we will work on MacOS to set up the Conda environment.

7. MacOS - Jupyter Notebook

In this video, we will work on MacOS to set up Jupyter Notebook.

8. Jupyter Notebook Interface and Shortcuts

In this video, we will cover Jupyter Notebook interface and shortcuts.

8. Introduction to Data Science in Python - Python Fundamentals

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

In this video, we will cover how to use markdown cells (adding headers, links, and images).

2. Comments - Inline and Block Comments

In this video, we will cover comments - inline and block comments.

3. Python Indentation

In this video, we will cover Python indentation.

4. Writing Single and Multiple Lines of Code

In this video, we will be working on writing single and multiple lines of code.

5. Understanding Variables

In this video, we will be working on understanding variables.

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

In this video, we will cover the main data types and create them (integer, float, string, list, dictionary).

7. Lists - How to Use

In this video, we will cover lists - how to use.

8. Dictionaries - How to Use

In this video, we will cover dictionaries - how to use them.

9. Creating a Tuple

In this video, we will be working on creating a tuple.

10. Tuple - How to Use

In this video, we will cover tuple - how to use.

11. Creating a Set

In this video, we will be working on creating a set.

12. Set - How to Use

In this video, we will cover the set - how to use it.

13. Operators

In this video, we will cover operators.

9. Introduction to Data Science in Python - Decision and Looping Structures

1. Introducing Decision and Looping Structures

In this video, we will be working on introducing decision and looping structures.

2. If Statement

In this video, we will cover the If statement.

3. Else Statement

In this video, we will cover the Else statement.

4. Elif

In this video, we will cover Elif.

5. For Loop

In this video, we will cover For Loop.

6. While Loop

In this video, we will understand While Loop.

7. Break and Continue Statements

In this video, we will cover break and continue statements.

10. Introduction to Data Science in Python - Python Functions

1. Introducing Functions

In this video, we will be working on introducing functions.

2. Functions - General Syntax

In this video, we will cover functions - general syntax.

3. +1 Function

In this video, we will cover +1 function.

4. Fav Band Function

In this video, we will cover Fav Band function.

5. Celsius to Fahrenheit Function

In this video, we will cover Celsius to Fahrenheit function.

6. Optional Return Statement (and Comparing It to Print Statement)

In this video, we will cover optional return statement (and compare it to print statement).

7. Defining a Function Versus Calling a Function

In this video, we will be working on defining a function versus calling a function, including different ways to call.

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

In this video, we will work on a practical/real world example: function to get Reddit data.

9. Lambda Introduction (Anonymous Functions)

In this video, we will be introduced to Lambda (anonymous functions).

10. Formal Function Versus Lambda for Splitting Strings

In this video, we will cover formal function versus Lambda for splitting strings.

11. Introduction to Data Science - Nested Data, Iteration, and List Comprehension

1. Introducing you to Nested Data and Iteration

In this video, we will be introduced to nested data and iteration.

2. Simple Nested Example

In this video, we will cover a simple nested example.

3. Double Indexing

In this video, we will cover double indexing.

4. Assigning Values

In this video, you will learn how to assign values.

5. List of Dicts and Dicts of Dicts Example

In this video, we will cover a list of Dicts and Dicts of Dicts example.

6. Nested Iteration - Iterating Through List of Lists

In this video, we will cover nested iteration - iterating through list of lists.

7. Defining List Comprehension and Syntax

In this video, we will be working on defining list comprehension and syntax.

8. List Comprehension - Simple Examples

In this video, we will cover list comprehension - simple examples.

9. List Comp as an Alternative to Loops

In this video, we will cover list comp as an alternative to loops.

10. Practical/Real World Example - Using Common Mathematical Notation

In this video, we will work on a practical/real world example - using a common mathematical notation.

11. Practical/Real World Example - Creating a Constrained ID

In this activity video, we will cover a practical/real world example - creating a constrained ID.

12. Activity: Building Intuition (Loops, Nested Data, Iteration, and List Comp)

In this video, we will be working on building intuition (loops, nested data, iteration, and list comp).

12. Introduction to Data Science in Python - Learn NumPy

1. Introducing NumPy

In this video, we will be introduced to NumPy.

2. Creating Our First NumPy Array

In this video, we will be working on creating our first NumPy array.

3. Shaping an Array (When You Know the Shape You Want)

In this video, you will learn how to shape an array (when you know the shape you want).

4. Creating a Sequence of Integers and Floats

In this video, we will be working on creating a sequence of integers and floats.

5. Element-Wise Operations

In this video, we will cover element-wise operations.

6. A Range with a Shape (Arrange Function with Reshape Function)

In this video, we will cover a range with a shape (arrange function with reshape function).

7. NumPy Indexing

In this video, we will cover NumPy indexing.

8. NumPy Slicing

In this video, we will cover NumPy slicing.

9. Indexing and Slicing with Breast Cancer Wisconsin Dataset

In this video, we will be working on indexing and slicing with breast cancer Wisconsin dataset.

10. Delete Elements

In this video, we will delete elements.

11. Append

In this video, we will cover append.

12. Insert Elements

In this video, we will cover insert elements.

13. Reshape -1 Feature

In this video, we will cover reshape -1 feature.

14. Flatten

In this video, we will cover Flatten.

15. Transpose

In this video, we will cover Transpose.

16. Concatenate

In this video, we will cover Concatenate.

17. Splitting

In this video, we will cover splitting.

18. Aggregate/Statistical Functions

In this video, we will cover aggregate/statistical functions.

13. Introduction to Data Science in Python - Pandas

1. Introducing Pandas

In this video, we will be introduced to Pandas.

2. For SAS Programmers: Analogous Terms in Pandas (Python)

In this video, we will cover analogous terms in Pandas (Python).

3. Using Series as Input into DataFrame

In this video, we will be using series as input into DataFrame.

4. Comparing Series and DataFrame

In this video, we will be comparing series and DataFrame.

5. Importing TSLA Dataset

In this video, we will be working on importing TSLA dataset.

6. Index-Based Selection (iloc)

In this video, we will cover index-based selection (iloc).

7. Label-Based Selection (loc)

In this video, we will cover label-based selection (loc).

8. Conditional Selection

In this video, we will cover conditional selection.

9. Summary Functions

In this video, we will cover summary functions.

10. Grouping (groupby)

In this video, we will cover grouping (groupby).

11. Sorting

In this video, we will cover sorting.

12. Checking Data Types and Converting

In this video, we will be working on checking data types and converting.

13. Dealing with Missing Values

In this video, we will be dealing with missing values.

14. Dropping Columns/Variables and Records/Rows

In this video, we will be working on dropping columns/variables and records/rows.

15. Renaming Columns/Variables and Records/Rows

In this video, we will be working on renaming columns/variables and records/rows.

16. Concat Function + Pop Quiz

In this video, we will cover Concat function + pop quiz.

17. Real-World Activity: Add New Columns and Predict Stock Movement

In this video, we will work on a real-world activity: add new columns and predict stock movement.

14. Introduction to Data Science in Python - Python Activity Solutions

1. Solution - Fill in Activity - Fundamentals

In this solution video, we will cover fundamentals.

2. Solution - Fill in Activity - Looping and Functions

In this solution video, we will cover looping and functions.

3. Solution - Fill in Activity - Nested and List Comprehension

In this solution video, we will cover nested and list comprehension.

4. Solution - Fill in Activity - NumPy

In this solution video, we will cover NumPy.

15. Essential Math for Data Science - Linear Algebra Made Easy

1. Linear Equation Definition

In this video, we will cover linear equation definition.

2. Forms of a Linear Equation

In this video, we will cover forms of a linear equation.

3. Systems of Linear Equations

In this video, we will cover systems of linear equations.

4. Line and Plane

In this video, we will cover the line and plane.

5. Aij Notation

In this video, we will cover Aij notation.

6. System of Equations as a Matrix

In this video, we will cover a system of equations as a matrix.

7. System in Corresponding Forms

In this video, we will cover a system in corresponding forms.

8. Row Echelon Form (Gaussian Elimination)

In this video, we will cover Row Echelon Form (Gaussian Elimination).

9. Reduced Row Echelon Form

In this video, we will cover Reduced Row Echelon Form.

10. Row Operations Rules

In this video, we will cover row operations rules.

11. Row Operations Example (REF)

In this video, we will cover Row Operations Example (REF).

12. Visualizing Ax=b

In this video, we will cover visualizing Ax=b.

13. General Formula - Matrix Vector Multiplication

In this video, we will cover general formula - matrix vector multiplication.

14. Tips for Row Operations

In this video, we will cover tips for row operations.

16. Essential Math for Data Science - Mathematical Structures

1. Mathematical Structures

In this video, we will cover mathematical structures.

2. Abelian Groups and Fields

In this video, we will cover Abelian groups and fields.

3. Vector Spaces 1

In this video, we will cover vector spaces 1.

4. Vector Spaces - Concrete Example

In this video, we will cover vector spaces - concrete example.

5. Subspaces

In this video, we will cover subspaces.

6. Linear Combinations and Span

In this video, we will cover linear combinations and span.

7. Is It in the Span?

In this video, we will understand if it is in the span.

8. Linear Independence

In this video, we will cover linear independence.

9. A Basis for a Vector Space

In this video, we will cover a basis for a vector space.

10. Dim of C(A) and N(A)

In this video, we will cover Dim of C(A) and N(A).

11. The Dimension of a Vector Space

In this video, we will cover the dimensions of a vector space.

12. Linear Maps

In this video, we will cover linear maps.

13. The Four Fundamental Subspaces

In this video, we will cover the four fundamental subspaces.

14. Adding Geometry to Vector Spaces

In this video, we will be working on adding geometry to vector spaces.

15. Orthogonal Projection - How to Derive Projection and Check for Orthogonality

In this video, we will cover orthogonal projection - how to derive projection and check for orthogonality.

16. Least Squares

In this video, we will cover least squares.

17. Least Squares Through Pseudoinverse - with Python and SAS code

In this video, we will cover least squares through Pseudoinverse - with Python and SAS code.

17. Essential Math for Data Science - Introduction to Probability

1. Probability Models and Axioms

In this video, we will cover probability models and axioms.

2. Simple Counting

In this video, we will cover simple counting.

3. Discrete Example

In this video, we will cover a discrete example.

4. Conditional Bayes

In this video, we will cover Conditional Bayes.

5. Conditional Example 1

In this video, we will cover conditional example 1.

6. Conditional Healthcare (Cancer) Example 2

In this video, we will cover conditional healthcare (cancer) example 2.

7. Independence of Events (What It Means and Does Not Mean)

In this video, we will understand what independence of events means and what it does not mean.

8. Permutations and Combinations

In this video, we will cover permutations and combinations.

18. Essential Math for Data Science - Random Variables and Multiple Variables

1. Random Variables

In this video, we will cover random variables.

2. Probability Mass Function and Discrete R.V.s

In this video, we will cover probability mass function and discrete R.V.s.

3. Expectation and Variance for Discrete Random Variables

In this video, we will cover expectation and variance for discrete random variables.

4. Joint PMFs (Multiple Discrete Variables)

In this video, we will cover joint PMFs (multiple discrete variables).

5. Continuous Random Variables

In this video, we will cover continuous random variables.

6. Continuous Random Variables and Probability Density Function

In this video, we will cover continuous random variables and probability density function.

7. Continuous R.V. Example

In this video, we will cover continuous R.V. example.

8. Joint PDF Example - Banking

In this video, we will cover a joint PDF example for banking.

9. Cumulative Distribution Function (CDF)

In this video, we will cover Cumulative Distribution Function (CDF).

10. Covariance, Correlation, and More on Variance

In this video, we will cover covariance, correlation, and more on variance.

11. Law of Large Numbers (LLN)

In this video, we will cover the Law of Large Numbers (LLN).

12. Central Limit Theorem (CLT)

In this video, we will cover Central Limit Theorem (CLT).

19. Essential Math for Data Science - Statistical Inference

1. Statistical Inference

In this video, we will cover statistical inference.

2. Bayesian Estimator

In this video, we will cover Bayesian Estimator.

3. Example - Bayesian Estimator

In this video, we will cover an example - Bayesian Estimator.

4. Mean Squared Error = Variance. Why?

In this video, we will understand the formula of Mean Squared Error = Variance.

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Data Science 101: Methodology, Python, and Essential Math

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