Data Science Essentials Practice Exam

Data Science Essentials Practice Exam

About the Data Science Essentials Exam

Data Science Essentials covers the foundational skills and concepts needed to analyze and interpret data. It includes key areas such as data cleaning, exploratory data analysis, statistical methods, machine learning algorithms, and data visualization. With tools like Python, R, and SQL, learners gain the ability to work with large datasets, build predictive models, and communicate insights effectively. These essentials form the backbone of data science, making it an invaluable skill for solving real-world problems and driving business decisions.

Skills Required

• Basic Programming Knowledge – Familiarity with Python or R for data manipulation and analysis.
• Mathematics and Statistics – Understanding basic concepts like probability, distributions, and statistical inference.
• Linear Algebra and Calculus – Helpful for understanding machine learning algorithms and optimization techniques.
• Data Handling and Manipulation – Basic knowledge of libraries like Pandas and NumPy to handle datasets.
• Data Visualization Skills – Familiarity with visualization tools like Matplotlib, Seaborn, or Tableau.
• Database Querying – Basic knowledge of SQL to retrieve and work with data from databases.
• Problem-Solving Skills – Ability to approach and analyze data-driven problems logically.

Knowledge Gained 

In this course you will gain:

• A strong foundation in data cleaning and preprocessing, ensuring high-quality data for analysis.
• Proficiency in exploratory data analysis (EDA) to uncover patterns, trends, and relationships in data.
• Understanding of key statistical methods and techniques for analyzing data and making inferences.
• Experience with machine learning algorithms, including supervised and unsupervised models, for predictive analysis.
• Skills in data visualization using tools like Matplotlib, Seaborn, and Tableau to effectively communicate insights.
• Hands-on knowledge of Python or R, along with essential libraries like Pandas, NumPy, and Scikit-learn.
• The ability to work with databases using SQL to query and manipulate large datasets.
• Practical experience in solving real-world data science problems and building data-driven solutions.

Who should take the Exam?

• Aspiring data scientists and analysts looking to test their foundational knowledge in data science.
• Professionals transitioning into data science roles from other fields, such as business analysis, engineering, or IT.
• Students pursuing data science, statistics, or related degrees who want to validate their skills.
• Data professionals who want to assess their understanding of key concepts like machine learning, data manipulation, and visualization.
• Anyone seeking a certification to enhance their resume and career opportunities in data science.
• Business analysts or managers who want to expand their analytical capabilities and understand data science principles.

Course Outline

Introduction to Data Science 101

• Matching Activity - Match the Project to the Data Role
• Introduction to Data Science
• What a Data Scientist Does
• Big Data
• Data Mining
• Machine Learning Versus Deep Learning
• Advice to Data Scientists

Best Language for Data Science

• What IS the Best Language for Data Science?
• Python
• SAS (Statistical Analysis System)
• R
• SQL

Data Science Methodology

• Data Science Methodology/Process Introduction
• Business Understanding
• Data Understanding
• Data Prep
• Modelling
• Evaluation
• Deployment

Data Science Through Chatbot

• Purpose of Chatbot Section
• What is a Chatbot?
• Signing Up for Watson Assistant
• Creating a Name - Healthcare Service Chatbot
• Intents
• Entities
• Suggestions for More Learning
• Section Recap: Natural Language Processing, Machine Learning, and Use Cases

Libraries, APIs, Datasets

• Libraries
• APIs
• Datasets

GitHub

• Introduction to GitHub
• Create a Repository
• Create a Branch and Commit Changes
• Pull Request and Merging Pull Request

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

• Windows - Download Anaconda Distribution (Includes Python!)
• Windows - Install Anaconda Distribution
• Windows - Setting Up Environment
• Windows - Opening Jupyter Notebook
• MacOS - Anaconda Download and Install
• MacOS - Conda Environment
• MacOS - Jupyter Notebook
• Jupyter Notebook Interface and Shortcuts

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

Introduction to Data Science in Python - Decision and Looping Structures

• Introducing Decision and Looping Structures
• If Statement
• Else Statement
• Elif
• For Loop
• While Loop
• Break and Continue Statements

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

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)

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
• Element-Wise 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

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
• Index-Based Selection (iloc)
• Label-Based 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
• Real-World Activity: Add New Columns and Predict Stock Movement

Introduction to Data Science in Python - Python Activity Solutions

• Solution - Fill in Activity - Fundamentals
• Solution - Fill in Activity - Looping and Functions
• Solution - Fill in Activity - Nested and List Comprehension
• Solution - Fill in Activity - NumPy

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

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

Essential Math for Data Science - Introduction to Probability

• Probability Models and Axioms
• Simple Counting
• Discrete Example
• Conditional Bayes
• Conditional Example 1
• Conditional Healthcare (Cancer) Example 2
• Independence of Events (What It Means and Does Not Mean)
• Permutations and Combinations

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)

Essential Math for Data Science - Statistical Inference

• Statistical Inference
• Bayesian Estimator
• Example - Bayesian Estimator
• Mean Squared Error = Variance. Why?