Video description
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 indepth understanding of an oftenutilized 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.
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 leastsquares 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.
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.
Table of contents
 Chapter 1 : Introduction to Data Science 101
 Chapter 2 : Best Language for Data Science
 Chapter 3 : Data Science Methodology
 Chapter 4 : Data Science Through Chatbot
 Chapter 5 : Libraries, APIs, Datasets
 Chapter 6 : GitHub
 Chapter 7 : Installation / Jupyter / Comments (Windows and MacOS/Jupyter Notebook)

Chapter 8 : Introduction to Data Science in Python  Python Fundamentals
 How to Use Markdown Cells (Adding Headers, Links, and Images)
 Comments  Inline and Block Comments
 Python Indentation
 Writing Single and Multiple Lines of Code
 Understanding Variables
 Main Data Types and Creating Them (Integer, Float, String, List, Dictionary)
 Lists  How to Use
 Dictionaries  How to Use
 Creating a Tuple
 Tuple  How to Use
 Creating a Set
 Set  How to Use
 Operators
 Chapter 9 : Introduction to Data Science in Python  Decision and Looping Structures

Chapter 10 : Introduction to Data Science in Python  Python Functions
 Introducing Functions
 Functions  General Syntax
 +1 Function
 Fav Band Function
 Celsius to Fahrenheit Function
 Optional Return Statement (and Comparing It to Print Statement)
 Defining a Function Versus Calling a Function
 Practical/Real World Example: Function to Get Reddit Data
 Lambda Introduction (Anonymous Functions)
 Formal Function Versus Lambda for Splitting Strings

Chapter 11 : Introduction to Data Science  Nested Data, Iteration, and List Comprehension
 Introducing you to Nested Data and Iteration
 Simple Nested Example
 Double Indexing
 Assigning Values
 List of Dicts and Dicts of Dicts Example
 Nested Iteration  Iterating Through List of Lists
 Defining List Comprehension and Syntax
 List Comprehension  Simple Examples
 List Comp as an Alternative to Loops
 Practical/Real World Example  Using Common Mathematical Notation
 Practical/Real World Example  Creating a Constrained ID
 Activity: Building Intuition (Loops, Nested Data, Iteration, and List Comp)

Chapter 12 : Introduction to Data Science in Python  Learn NumPy
 Introducing NumPy
 Creating Our First NumPy Array
 Shaping an Array (When You Know the Shape You Want)
 Creating a Sequence of Integers and Floats
 ElementWise Operations
 A Range with a Shape (Arrange Function with Reshape Function)
 NumPy Indexing
 NumPy Slicing
 Indexing and Slicing with Breast Cancer Wisconsin Dataset
 Delete Elements
 Append
 Insert Elements
 Reshape 1 Feature
 Flatten
 Transpose
 Concatenate
 Splitting
 Aggregate/Statistical Functions

Chapter 13 : Introduction to Data Science in Python  Pandas
 Introducing Pandas
 For SAS Programmers: Analogous Terms in Pandas (Python)
 Using Series as Input into DataFrame
 Comparing Series and DataFrame
 Importing TSLA Dataset
 IndexBased Selection (iloc)
 LabelBased Selection (loc)
 Conditional Selection
 Summary Functions
 Grouping (groupby)
 Sorting
 Checking Data Types and Converting
 Dealing with Missing Values
 Dropping Columns/Variables and Records/Rows
 Renaming Columns/Variables and Records/Rows
 Concat Function + Pop Quiz
 RealWorld Activity: Add New Columns and Predict Stock Movement
 Chapter 14 : Introduction to Data Science in Python  Python Activity Solutions

Chapter 15 : Essential Math for Data Science  Linear Algebra Made Easy
 Linear Equation Definition
 Forms of a Linear Equation
 Systems of Linear Equations
 Line and Plane
 Aij Notation
 System of Equations as a Matrix
 System in Corresponding Forms
 Row Echelon Form (Gaussian Elimination)
 Reduced Row Echelon Form
 Row Operations Rules
 Row Operations Example (REF)
 Visualizing Ax=b
 General Formula  Matrix Vector Multiplication
 Tips for Row Operations

Chapter 16 : Essential Math for Data Science  Mathematical Structures
 Mathematical Structures
 Abelian Groups and Fields
 Vector Spaces 1
 Vector Spaces  Concrete Example
 Subspaces
 Linear Combinations and Span
 Is It in the Span?
 Linear Independence
 A Basis for a Vector Space
 Dim of C(A) and N(A)
 The Dimension of a Vector Space
 Linear Maps
 The Four Fundamental Subspaces
 Adding Geometry to Vector Spaces
 Orthogonal Projection  How to Derive Projection and Check for Orthogonality
 Least Squares
 Least Squares Through Pseudoinverse  with Python and SAS code
 Chapter 17 : Essential Math for Data Science  Introduction to Probability

Chapter 18 : Essential Math for Data Science  Random Variables and Multiple Variables
 Random Variables
 Probability Mass Function and Discrete R.V.s
 Expectation and Variance for Discrete Random Variables
 Joint PMFs (Multiple Discrete Variables)
 Continuous Random Variables
 Continuous Random Variables and Probability Density Function
 Continuous R.V. Example
 Joint PDF Example  Banking
 Cumulative Distribution Function (CDF)
 Covariance, Correlation, and More on Variance
 Law of Large Numbers (LLN)
 Central Limit Theorem (CLT)
 Chapter 19 : Essential Math for Data Science  Statistical Inference
Product information
 Title: Data Science 101: Methodology, Python, and Essential Math
 Author(s):
 Release date: April 2022
 Publisher(s): Packt Publishing
 ISBN: 9781803242125
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