Section 1 : Introduction to the Course
Lecture 1 A Practical Example - What Will You Learn in Th 00:04:47 Duration
Lecture 2 About Certification
Lecture 3 Download All Resources
Lecture 4 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 2 : Introduction to Data Analytics
Lecture 1 Introduction to the World of Business and Data 00:02:26 Duration
Lecture 2 Relevant Terms Explained 00:05:46 Duration
Lecture 3 Data Analyst Compared to Other Data Jobs 00:02:28 Duration
Lecture 4 Data Analyst Job Description 00:05:43 Duration
Lecture 5 Why Python 00:05:08 Duration

Section 3 : Setting up the Environment
Lecture 1 Introduction 00:01:24 Duration
Lecture 2 Programming Explained in a Few Minutes 00:05:04 Duration
Lecture 3 Jupyter - Introduction 00:03:29 Duration
Lecture 4 Jupyter - Installing Anaconda 00:04:00 Duration
Lecture 5 Jupyter - Intro to Using Jupyter 00:03:11 Duration
Lecture 6 Jupyter - Working with Notebook Files 00:06:09 Duration
Lecture 7 Jupyter - Using Shortcuts 00:03:07 Duration
Lecture 8 Jupyter - Handling Error Messages 00:05:53 Duration
Lecture 9 Jupyter - Restarting the Kernel 00:02:18 Duration

Section 4 : Python Basics
Lecture 1 Python Variables 00:03:37 Duration
Lecture 2 Types of Data - Numbers and Boolean Values 00:03:06 Duration
Lecture 3 Types of Data - Strings 00:05:40 Duration
Lecture 4 Basic Python Syntax - Arithmetic Operators 00:03:23 Duration
Lecture 5 Basic Python Syntax - The Double Equality Sign 00:01:34 Duration
Lecture 6 Basic Python Syntax - Reassign Values 00:01:08 Duration
Lecture 7 Basic Python Syntax - Add Comments 00:01:34 Duration
Lecture 8 Basic Python Syntax - Line Continuation 00:00:50 Duration
Lecture 9 Basic Python Syntax - Indexing Elements 00:01:18 Duration
Lecture 10 Basic Python Syntax - Indentation 00:01:45 Duration
Lecture 11 Operators - Comparison Operators 00:02:10 Duration
Lecture 12 Operators - Logical and Identity Operators 00:05:36 Duration
Lecture 13 Conditional Statements - The IF Statement 00:03:02 Duration
Lecture 14 Conditional Statements - The ELSE Statement 00:02:45 Duration
Lecture 15 Conditional Statements - The ELIF Statement 00:05:34 Duration
Lecture 16 Conditional Statements - A Note on Boolean Val 00:02:14 Duration
Lecture 17 Functions - Defining a Function in Python 00:02:02 Duration
Lecture 18 Functions - Creating a Function with a Paramet 00:03:50 Duration
Lecture 19 Functions - Another Way to Define a Function 00:02:36 Duration
Lecture 20 Functions - Using a Function in Another Functi 00:01:49 Duration
Lecture 21 Functions - Combining Conditional Statements a 00:03:07 Duration
Lecture 22 Functions - Creating Functions That Contain a 00:01:17 Duration
Lecture 23 Functions - Notable Built-in Functions in Pyth 00:03:56 Duration
Lecture 24 Sequences - Lists 00:04:02 Duration
Lecture 25 Sequences - Using Methods 00:03:19 Duration
Lecture 26 Sequences - List Slicing 00:04:31 Duration
Lecture 27 Sequences - Tuples 00:03:11 Duration
Lecture 28 Sequences - Dictionaries 00:04:04 Duration
Lecture 29 Iteration - For Loops 00:02:57 Duration
Lecture 30 Iteration - While Loops and Incrementing 00:02:26 Duration
Lecture 31 Iteration - Create Lists with the range() Func 00:03:50 Duration
Lecture 32 Iteration - Use Conditional Statements and Loo 00:03:12 Duration
Lecture 33 Iteration - Conditional Statements, Functions, 00:02:27 Duration
Lecture 34 Iteration - Iterating over Dictionaries 00:03:08 Duration

Section 5 : Fundamentals for Coding in Python
Lecture 1 Object-Oriented Programming (OOP) 00:05:00 Duration
Lecture 2 Modules, Packages, and the Python Standard Lib 00:04:24 Duration
Lecture 3 Importing Modules 00:03:25 Duration
Lecture 4 Introduction to Using NumPy and pandas 00:09:09 Duration
Lecture 5 What is Software Documentation 00:03:58 Duration
Lecture 6 The Python Documentation 00:06:23 Duration

Section 6 : Mathematics for Python
Lecture 1 What Is ? Matrix 00:03:37 Duration
Lecture 2 Scalars and Vectors 00:02:59 Duration
Lecture 3 Linear Algebra and Geometry 00:03:06 Duration
Lecture 4 Arrays in Python 00:05:09 Duration
Lecture 5 What Is a Tensor 00:03:00 Duration
Lecture 6 Adding and Subtracting Matrices 00:03:36 Duration
Lecture 7 Errors When Adding Matrices 00:02:01 Duration
Lecture 8 Transpose 00:05:13 Duration
Lecture 9 Dot Product of Vectors 00:03:48 Duration
Lecture 10 Dot Product of Matrices 00:08:23 Duration
Lecture 11 Why is Linear Algebra Useful 00:10:10 Duration

Section 7 : NumPy Basics
Lecture 1 The NumPy Package and Why We Use It 00:04:03 Duration
Lecture 2 InstallingUpgrading NumPy 00:02:02 Duration
Lecture 3 Ndarray 00:03:06 Duration
Lecture 4 The NumPy Documentation 00:04:43 Duration
Lecture 5 NumPy Basics - Exercise

Section 8 : Pandas - Basics
Lecture 1 Introduction to the pandas Library 00:05:41 Duration
Lecture 2 Installing and Running pandas 00:05:57 Duration
Lecture 3 Introduction to pandas Series 00:08:41 Duration
Lecture 4 Working with Attributes in Python 00:05:22 Duration
Lecture 5 Using an Index in pandas 00:04:01 Duration
Lecture 6 Label-based vs Position-based Indexing 00:04:32 Duration
Lecture 7 More on Working with Indices in Python 00:05:37 Duration
Lecture 8 Using Methods in Python - Part I 00:04:55 Duration
Lecture 9 Using Methods in Python - Part II 00:02:36 Duration
Lecture 10 Parameters vs Arguments 00:04:35 Duration
Lecture 11 the pandas Documentation 00:09:55 Duration
Lecture 12 Introduction to pandas DataFrames 00:05:23 Duration
Lecture 13 Creating DataFrames from Scratch - Part I 00:05:56 Duration
Lecture 14 Creating DataFrames from Scratch - Part II 00:05:03 Duration
Lecture 15 Additional Notes on Using DataFrames 00:01:58 Duration
Lecture 16 pandas Basics - Conclusion

Section 9 : Working with Text Files
Lecture 1 Working with Files in Python - An Introduction 00:03:47 Duration
Lecture 2 File vs File Object, Read vs Parse 00:02:52 Duration
Lecture 3 Structured vs Semi-Structured and Unstructured 00:03:10 Duration
Lecture 4 Data Connectivity through Text Files 00:03:07 Duration
Lecture 5 Principles of Importing Data in Python 00:04:50 Duration
Lecture 6 More on Text Files (.txt vs .csv) 00:04:33 Duration
Lecture 7 Fixed-width Files 00:01:26 Duration
Lecture 8 Common Naming Conventions Used in Programming 00:03:50 Duration
Lecture 9 Importing Text Files in Python ( open() ) 00:09:01 Duration
Lecture 10 Importing Text Files in Python ( with open() 00:04:53 Duration
Lecture 11 Importing .csv Files with pandas - Part I 00:05:35 Duration
Lecture 12 Importing .csv Files with pandas - Part II 00:02:37 Duration
Lecture 13 Importing .csv Files with pandas - Part III 00:05:58 Duration
Lecture 14 Importing Data with the index_col Parameter 00:02:36 Duration
Lecture 15 Importing Data with NumPy - .loadtxt 00:10:44 Duration
Lecture 16 Importing Data with NumPy - Partial Cleaning 00:07:21 Duration
Lecture 17 Importing Data with NumPy - Exercise
Lecture 18 Importing .json Files 00:05:15 Duration
Lecture 19 Prelude to Working with Excel Files in Python 00:03:41 Duration
Lecture 20 Working with Excel Data (the .xlsx Format) 00:01:56 Duration
Lecture 21 An Important Exercise on Importing Data in 00:05:44 Duration
Lecture 22 Importing Data with the pandas' Squeeze Param 00:02:37 Duration
Lecture 23 A Note on Importing Files in Jupyter 00:03:10 Duration
Lecture 24 Saving Your Data with pandas 00:03:12 Duration
Lecture 25 Saving Your Data with NumPy - np.save() 00:05:23 Duration
Lecture 26 Saving Your Data with NumPy - np.savez() 00:05:12 Duration
Lecture 27 Saving Your Data with NumPy - np.savetxt() 00:03:58 Duration
Lecture 28 Saving Your Data with NumPy - Exercise
Lecture 29 Working with Text Files - Conclusion 00:00:42 Duration

Section 10 : Working with Text Data
Lecture 1 Using the .format() Method 00:09:03 Duration

Section 11 : Must-Know Python Tools
Lecture 1 Iterating Over Range Objects 00:04:17 Duration
Lecture 2 Nested For Loops - Introduction 00:06:00 Duration
Lecture 3 Triple Nested For Loops 00:05:37 Duration
Lecture 4 List Comprehensions 00:08:30 Duration
Lecture 5 Anonymous (Lambda) Functions 00:07:00 Duration

Section 12 : Data GatheringData Collection
Lecture 1 What is data gatheringdata collection 00:06:32 Duration

Section 13 : APIs (POST requests are not needed for this course)
Lecture 1 Overview of APIs 00:03:10 Duration
Lecture 2 GET and POST Requests 00:02:36 Duration
Lecture 3 Data Exchange Format for APIs JSON 00:02:24 Duration
Lecture 4 Introducing the Exchange Rates API 00:04:57 Duration
Lecture 5 Including Parameters in a GET Request 00:03:18 Duration
Lecture 6 More Functionalities of the Exchange Rates 00:04:40 Duration
Lecture 7 Coding a Simple Currency Conversion Calculato 00:04:52 Duration
Lecture 8 iTunes API 00:04:41 Duration
Lecture 9 iTunes API Homework
Lecture 10 iTunes API Structuring and Exporting the Data 00:02:10 Duration
Lecture 11 Pagination GitHub API
Lecture 12 APIs Exercise

Section 14 : Data Cleaning and Data Preprocessing
Lecture 1 Data Cleaning and Data Preprocessing 00:05:27 Duration

Section 15 : pandas Series
Lecture 1 unique(), .nunique() 00:03:49 Duration
Lecture 2 Converting Series into Arrays 00:05:29 Duration
Lecture 3 .sort_values() 00:03:58 Duration
Lecture 4 Attribute and Method Chaining 00:04:21 Duration
Lecture 5 .sort_index() 00:03:59 Duration

Section 16 : pandas DataFrames
Lecture 1 A Revision to pandas DataFrames 00:05:06 Duration
Lecture 2 Common Attributes for Working with DataFrames 00:04:16 Duration
Lecture 3 Data Selection in pandas DataFrames 00:06:56 Duration
Lecture 4 Data Selection - Indexing with .iloc[] 00:05:57 Duration
Lecture 5 Data Selection - Indexing with .loc[] 00:04:02 Duration
Lecture 6 A Few Comments on Using .loc[] and .iloc[] 00:11:40 Duration

Section 17 : NumPy Fundamentals
Lecture 1 Indexing in NumPy 00:05:52 Duration
Lecture 2 Assigning Values in NumPy 00:04:16 Duration
Lecture 3 Elementwise Properties of Arrays 00:04:29 Duration
Lecture 4 Types of Data Supported by NumPy 00:05:57 Duration
Lecture 5 Characteristics of NumPy Functions Part 1 00:04:43 Duration
Lecture 6 Characteristics of NumPy Functions Part 2 00:03:31 Duration
Lecture 7 NumPy Fundamentals - Exercise

Section 18 : NumPy DataTypes
Lecture 1 ndarrays 00:09:52 Duration
Lecture 2 Arrays vs Lists 00:06:55 Duration
Lecture 3 Strings vs Object vs Number 00:07:15 Duration
Lecture 4 NumPy DataTypes - Exercise

Section 19 : Working with Arrays
Lecture 1 Basic Slicing in NumPy 00:10:04 Duration
Lecture 2 Stepwise Slicing in NumPy 00:04:58 Duration
Lecture 3 Conditional Slicing in NumPy 00:04:51 Duration
Lecture 4 Dimensions and the Squeeze Function 00:06:52 Duration
Lecture 5 Working with Arrays - Exercise

Section 20 : Generating Data with NumPy
Lecture 1 Arrays of 0s and 1s 00:05:33 Duration
Lecture 2 _like functions in NumPy 00:03:13 Duration
Lecture 3 A Non-Random Sequence of Numbers 00:05:02 Duration
Lecture 4 Random Generators and Seeds 00:05:21 Duration
Lecture 5 Basic Random Functions in NumPy 00:03:57 Duration
Lecture 6 Probability Distributions in NumPy
Lecture 7 Applications of Random Data in NumPy 00:04:09 Duration
Lecture 8 Generating Data with NumPy - Exercise

Section 21 : Statistics with NumPy
Lecture 1 Using Statistical Functions in NumPy 00:07:45 Duration
Lecture 2 Minimal and Maximal Values in NumPy 00:06:02 Duration
Lecture 3 Statistical Order Functions in NumPy 00:06:26 Duration
Lecture 4 Averages and Variance in NumPy 00:04:17 Duration
Lecture 5 Covariance and Correlation in NumPy 00:02:59 Duration
Lecture 6 Histograms in NumPy (Part 1) 00:07:36 Duration
Lecture 7 Histograms in NumPy (Part 2) 00:04:15 Duration
Lecture 8 NAN Equivalent Functions in NumPy
Lecture 9 Statistics with NumPy - Exercise

Section 22 : NumPy - Preprocessing
Lecture 1 Checking for Missing Values in Ndarrays 00:09:24 Duration
Lecture 2 Substituting Missing Values in Ndarrays 00:08:30 Duration
Lecture 3 Reshaping Ndarrays 00:06:31 Duration
Lecture 4 Removing Values from Ndarrays 00:04:21 Duration
Lecture 5 Sorting Ndarrays 00:09:45 Duration
Lecture 6 Argument Sort in NumPy 00:05:49 Duration
Lecture 7 Argument Where in NumPy 00:11:13 Duration
Lecture 8 Shuffling Ndarrays 00:06:52 Duration
Lecture 9 Casting Ndarrays 00:06:14 Duration
Lecture 10 Striping Values from Ndarrays 00:04:44 Duration
Lecture 11 Stacking Ndarrays 00:10:31 Duration
Lecture 12 Concatenating Ndarrays 00:06:28 Duration
Lecture 13 Finding Unique Values in Ndarrays 00:05:04 Duration

Section 23 : A Loan Data Example with NumPy
Lecture 1 Setting Up Introduction to the Practical Exam 00:04:50 Duration
Lecture 2 Setting Up Importing the Data Set 00:04:10 Duration
Lecture 3 Setting Up Checking for Incomplete Data 00:04:35 Duration
Lecture 4 Setting Up Splitting the Dataset 00:05:28 Duration
Lecture 5 Setting Up Creating Checkpoints 00:02:50 Duration
Lecture 6 Manipulating Text Data Issue Date 00:05:27 Duration
Lecture 7 Manipulating Text Data Loan Status and Term 00:07:08 Duration
Lecture 8 Manipulating Text Data Grade and Sub Grade 00:08:55 Duration
Lecture 9 Manipulating Text Data Verification Status 00:05:20 Duration
Lecture 10 Manipulating Text Data State Address 00:06:02 Duration
Lecture 11 Manipulating Text Data Converting Strings and 00:03:29 Duration
Lecture 12 Manipulating Numeric Data Substitute Filler 00:07:52 Duration
Lecture 13 Manipulating Numeric Data Currency Change 00:06:32 Duration
Lecture 14 Manipulating Numeric Data Currency Change 00:08:22 Duration
Lecture 15 Completing the Dataset 00:06:46 Duration

Section 24 : The Absenteeism Exercise - Introduction
Lecture 1 An Introduction to the Absenteeism Exercise 00:01:12 Duration
Lecture 2 The Absenteeism Exercise from a Business Pers 00:02:19 Duration
Lecture 3 The Dataset 00:01:34 Duration

Section 25 : Solution to the Absenteeism Exercise
Lecture 1 How to Complete the Absenteeism Exercise 00:01:58 Duration
Lecture 2 Eyeball Your Data First 00:05:54 Duration
Lecture 3 Note Programming vs the Rest of the World 00:03:28 Duration
Lecture 4 Using a Statistical Approach to Solve Our Exe 00:02:18 Duration
Lecture 5 Dropping the 'ID' Column 00:06:27 Duration
Lecture 6 Analysis of the 'Reason for Absence' Column 00:05:04 Duration
Lecture 7 Splitting the Reasons for Absence into Multip 00:08:38 Duration
Lecture 8 Working with Dummy Variables - A Statistical 00:01:28 Duration
Lecture 9 Grouping the Reason for Absence Columns 00:08:35 Duration
Lecture 10 Concatenating Columns in a pandas DataFrame 00:04:35 Duration
Lecture 11 Reordering Columns in a DataFrame 00:01:43 Duration
Lecture 12 Working on the 'Date' Column 00:07:49 Duration
Lecture 13 Extracting the Month Value from the 'Date' 00:07:00 Duration
Lecture 14 Creating the 'Day of the Week' Column 00:03:36 Duration
Lecture 15 Understanding the Meaning of 5 More Columns 00:03:18 Duration
Lecture 16 Modifying the 'Education' Column 00:04:38 Duration
Lecture 17 Final Remarks on the Absenteeism Exercise 00:01:41 Duration

Section 26 : Data Visualization
Lecture 1 What Is Data Visualization and Why Is It Impo 00:04:31 Duration
Lecture 2 Why Learn Data Visualization 00:06:08 Duration
Lecture 3 Choosing the Right Visualization – What Are S 00:06:58 Duration
Lecture 4 Introduction into Colors and Color Theory 00:08:56 Duration
Lecture 5 Bar Chart - Introduction - General Theory and 00:01:30 Duration
Lecture 6 Bar Chart - How to Create a Bar Chart Using 00:11:28 Duration
Lecture 7 Bar Chart – Interpreting the Bar Graph. How 00:02:50 Duration
Lecture 8 Pie Chart - Introduction - General Theory and 00:04:04 Duration
Lecture 9 Pie Chart - How to Create a Pie Chart Using 00:06:39 Duration
Lecture 10 Pie Chart – Interpreting the Pie Chart 00:01:32 Duration
Lecture 11 Pie Chart - Why You Should Never Create a Pie 00:07:33 Duration
Lecture 12 Stacked Area Chart - Introduction - General T 00:03:16 Duration
Lecture 13 Stacked Area Chart - How to Create a Stacked 00:07:48 Duration
Lecture 14 Stacked Area Chart - Interpreting the Stacked 00:02:30 Duration
Lecture 15 Stacked Area Chart - How to Make a Good Stack
Lecture 16 Line Chart - Introduction - General Theory 00:02:04 Duration
Lecture 17 Line Chart - How to Create a Line Chart in Py 00:08:06 Duration
Lecture 18 Line Chart - Interpretation 00:03:11 Duration
Lecture 19 Line Chart - How to Make a Good Line Chart 00:06:30 Duration
Lecture 20 Histogram - Introduction - General Theory. Ge
Lecture 21 Histogram - How to Create a Histogram Using 00:05:43 Duration
Lecture 22 Histogram – Interpreting the Histogram 00:02:12 Duration
Lecture 23 Histogram – Choosing the Number of Bins in a 00:05:28 Duration
Lecture 24 Histogram - How to Make a Good Histogram 00:04:43 Duration
Lecture 25 Scatter Plot - Introduction - General Theory 00:02:29 Duration
Lecture 26 Scatter Plot - How to Create a Scatter Plot 00:08:39 Duration
Lecture 27 Scatter Plot – Interpreting the Scatter Plot 00:02:42 Duration
Lecture 28 Scatter Plot - How to Make a Good Scatter Plo 00:02:57 Duration
Lecture 29 Regression Plot - Introduction - General Theo 00:03:03 Duration
Lecture 30 Regression Plot - How to Create a Regression 00:07:09 Duration
Lecture 31 Regression Plot – Interpreting the Regression 00:04:36 Duration
Lecture 32 Regression Plot - How to Make a Good Regressi 00:03:14 Duration
Lecture 33 Bar and Line Chart - Introduction - General T 00:03:10 Duration
Lecture 34 Bar and Line Chart - How to Create a Combinat 00:07:40 Duration
Lecture 35 Bar and Line Chart – Interpreting the Combina 00:02:36 Duration
Lecture 36 Bar and Line Chart – How to Make a Good Bar a 00:04:04 Duration
Lecture 37 Data Visualization - Exercise