Section 1 : Introduction to the Course

lecture 1 A Practical Example - What Will You Learn in Th 4:47
lecture 2 About Certification Pdf
lecture 3 Download All Resources Text
lecture 4 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 2 : Introduction to Data Analytics

lecture 5 Introduction to the World of Business and Data 2:26
lecture 6 Relevant Terms Explained 5:46
lecture 7 Data Analyst Compared to Other Data Jobs 2:28
lecture 8 Data Analyst Job Description 5:43
lecture 9 Why Python 5:8

Section 3 : Setting up the Environment

lecture 10 Introduction 1:24
lecture 11 Programming Explained in a Few Minutes 5:4
lecture 12 Jupyter - Introduction 3:29
lecture 13 Jupyter - Installing Anaconda 4:0
lecture 14 Jupyter - Intro to Using Jupyter 3:11
lecture 15 Jupyter - Working with Notebook Files 6:9
lecture 16 Jupyter - Using Shortcuts 3:7
lecture 17 Jupyter - Handling Error Messages 5:53
lecture 18 Jupyter - Restarting the Kernel 2:18

Section 4 : Python Basics

lecture 19 Python Variables 3:37
lecture 20 Types of Data - Numbers and Boolean Values 3:6
lecture 21 Types of Data - Strings 5:40
lecture 22 Basic Python Syntax - Arithmetic Operators 3:23
lecture 23 Basic Python Syntax - The Double Equality Sign 1:34
lecture 24 Basic Python Syntax - Reassign Values 1:8
lecture 25 Basic Python Syntax - Add Comments 1:34
lecture 26 Basic Python Syntax - Line Continuation 0:50
lecture 27 Basic Python Syntax - Indexing Elements 1:18
lecture 28 Basic Python Syntax - Indentation 1:45
lecture 29 Operators - Comparison Operators 2:10
lecture 30 Operators - Logical and Identity Operators 5:36
lecture 31 Conditional Statements - The IF Statement 3:2
lecture 32 Conditional Statements - The ELSE Statement 2:45
lecture 33 Conditional Statements - The ELIF Statement 5:34
lecture 34 Conditional Statements - A Note on Boolean Val 2:14
lecture 35 Functions - Defining a Function in Python 2:2
lecture 36 Functions - Creating a Function with a Paramet 3:50
lecture 37 Functions - Another Way to Define a Function 2:36
lecture 38 Functions - Using a Function in Another Functi 1:49
lecture 39 Functions - Combining Conditional Statements a 3:7
lecture 40 Functions - Creating Functions That Contain a 1:17
lecture 41 Functions - Notable Built-in Functions in Pyth 3:56
lecture 42 Sequences - Lists 4:2
lecture 43 Sequences - Using Methods 3:19
lecture 44 Sequences - List Slicing 4:31
lecture 45 Sequences - Tuples 3:11
lecture 46 Sequences - Dictionaries 4:4
lecture 47 Iteration - For Loops 2:57
lecture 48 Iteration - While Loops and Incrementing 2:26
lecture 49 Iteration - Create Lists with the range() Func 3:50
lecture 50 Iteration - Use Conditional Statements and Loo 3:12
lecture 51 Iteration - Conditional Statements, Functions, 2:27
lecture 52 Iteration - Iterating over Dictionaries 3:8

Section 5 : Fundamentals for Coding in Python

lecture 53 Object-Oriented Programming (OOP) 5:0
lecture 54 Modules, Packages, and the Python Standard Lib 4:24
lecture 55 Importing Modules 3:25
lecture 56 Introduction to Using NumPy and pandas 9:9
lecture 57 What is Software Documentation 3:58
lecture 58 The Python Documentation 6:23

Section 6 : Mathematics for Python

lecture 59 What Is ? Matrix 3:37
lecture 60 Scalars and Vectors 2:59
lecture 61 Linear Algebra and Geometry 3:6
lecture 62 Arrays in Python 5:9
lecture 63 What Is a Tensor 3:0
lecture 64 Adding and Subtracting Matrices 3:36
lecture 65 Errors When Adding Matrices 2:1
lecture 66 Transpose 5:13
lecture 67 Dot Product of Vectors 3:48
lecture 68 Dot Product of Matrices 8:23
lecture 69 Why is Linear Algebra Useful 10:10

Section 7 : NumPy Basics

lecture 70 The NumPy Package and Why We Use It 4:3
lecture 71 InstallingUpgrading NumPy 2:2
lecture 72 Ndarray 3:6
lecture 73 The NumPy Documentation 4:43
lecture 74 NumPy Basics - Exercise Text

Section 8 : Pandas - Basics

lecture 75 Introduction to the pandas Library 5:41
lecture 76 Installing and Running pandas 5:57
lecture 77 Introduction to pandas Series 8:41
lecture 78 Working with Attributes in Python 5:22
lecture 79 Using an Index in pandas 4:1
lecture 80 Label-based vs Position-based Indexing 4:32
lecture 81 More on Working with Indices in Python 5:37
lecture 82 Using Methods in Python - Part I 4:55
lecture 83 Using Methods in Python - Part II 2:36
lecture 84 Parameters vs Arguments 4:35
lecture 85 the pandas Documentation 9:55
lecture 86 Introduction to pandas DataFrames 5:23
lecture 87 Creating DataFrames from Scratch - Part I 5:56
lecture 88 Creating DataFrames from Scratch - Part II 5:3
lecture 89 Additional Notes on Using DataFrames 1:58
lecture 90 pandas Basics - Conclusion Text

Section 9 : Working with Text Files

lecture 91 Working with Files in Python - An Introduction 3:47
lecture 92 File vs File Object, Read vs Parse 2:52
lecture 93 Structured vs Semi-Structured and Unstructured 3:10
lecture 94 Data Connectivity through Text Files 3:7
lecture 95 Principles of Importing Data in Python 4:50
lecture 96 More on Text Files (.txt vs .csv) 4:33
lecture 97 Fixed-width Files 1:26
lecture 98 Common Naming Conventions Used in Programming 3:50
lecture 99 Importing Text Files in Python ( open() ) 9:1
lecture 100 Importing Text Files in Python ( with open() 4:53
lecture 101 Importing .csv Files with pandas - Part I 5:35
lecture 102 Importing .csv Files with pandas - Part II 2:37
lecture 103 Importing .csv Files with pandas - Part III 5:58
lecture 104 Importing Data with the index_col Parameter 2:36
lecture 105 Importing Data with NumPy - .loadtxt 10:44
lecture 106 Importing Data with NumPy - Partial Cleaning 7:21
lecture 107 Importing Data with NumPy - Exercise Text
lecture 108 Importing .json Files 5:15
lecture 109 Prelude to Working with Excel Files in Python 3:41
lecture 110 Working with Excel Data (the .xlsx Format) 1:56
lecture 111 An Important Exercise on Importing Data in 5:44
lecture 112 Importing Data with the pandas' Squeeze Param 2:37
lecture 113 A Note on Importing Files in Jupyter 3:10
lecture 114 Saving Your Data with pandas 3:12
lecture 115 Saving Your Data with NumPy - np.save() 5:23
lecture 116 Saving Your Data with NumPy - np.savez() 5:12
lecture 117 Saving Your Data with NumPy - np.savetxt() 3:58
lecture 118 Saving Your Data with NumPy - Exercise Text
lecture 119 Working with Text Files - Conclusion 0:42

Section 10 : Working with Text Data

lecture 120 Using the .format() Method 9:3

Section 11 : Must-Know Python Tools

lecture 121 Iterating Over Range Objects 4:17
lecture 122 Nested For Loops - Introduction 6:0
lecture 123 Triple Nested For Loops 5:37
lecture 124 List Comprehensions 8:30
lecture 125 Anonymous (Lambda) Functions 7:0

Section 12 : Data GatheringData Collection

lecture 126 What is data gatheringdata collection 6:32

Section 13 : APIs (POST requests are not needed for this course)

lecture 127 Overview of APIs 3:10
lecture 128 GET and POST Requests 2:36
lecture 129 Data Exchange Format for APIs JSON 2:24
lecture 130 Introducing the Exchange Rates API 4:57
lecture 131 Including Parameters in a GET Request 3:18
lecture 132 More Functionalities of the Exchange Rates 4:40
lecture 133 Coding a Simple Currency Conversion Calculato 4:52
lecture 134 iTunes API 4:41
lecture 135 iTunes API Homework Text
lecture 136 iTunes API Structuring and Exporting the Data 2:10
lecture 137 Pagination GitHub API
lecture 138 APIs Exercise Text

Section 14 : Data Cleaning and Data Preprocessing

lecture 139 Data Cleaning and Data Preprocessing 5:27

Section 15 : pandas Series

lecture 140 unique(), .nunique() 3:49
lecture 141 Converting Series into Arrays 5:29
lecture 142 .sort_values() 3:58
lecture 143 Attribute and Method Chaining 4:21
lecture 144 .sort_index() 3:59

Section 16 : pandas DataFrames

lecture 145 A Revision to pandas DataFrames 5:6
lecture 146 Common Attributes for Working with DataFrames 4:16
lecture 147 Data Selection in pandas DataFrames 6:56
lecture 148 Data Selection - Indexing with .iloc[] 5:57
lecture 149 Data Selection - Indexing with .loc[] 4:2
lecture 150 A Few Comments on Using .loc[] and .iloc[] 11:40

Section 17 : NumPy Fundamentals

lecture 151 Indexing in NumPy 5:52
lecture 152 Assigning Values in NumPy 4:16
lecture 153 Elementwise Properties of Arrays 4:29
lecture 154 Types of Data Supported by NumPy 5:57
lecture 155 Characteristics of NumPy Functions Part 1 4:43
lecture 156 Characteristics of NumPy Functions Part 2 3:31
lecture 157 NumPy Fundamentals - Exercise Text

Section 18 : NumPy DataTypes

lecture 158 ndarrays 9:52
lecture 159 Arrays vs Lists 6:55
lecture 160 Strings vs Object vs Number 7:15
lecture 161 NumPy DataTypes - Exercise Text

Section 19 : Working with Arrays

lecture 162 Basic Slicing in NumPy 10:4
lecture 163 Stepwise Slicing in NumPy 4:58
lecture 164 Conditional Slicing in NumPy 4:51
lecture 165 Dimensions and the Squeeze Function 6:52
lecture 166 Working with Arrays - Exercise Text

Section 20 : Generating Data with NumPy

lecture 167 Arrays of 0s and 1s 5:33
lecture 168 _like functions in NumPy 3:13
lecture 169 A Non-Random Sequence of Numbers 5:2
lecture 170 Random Generators and Seeds 5:21
lecture 171 Basic Random Functions in NumPy 3:57
lecture 172 Probability Distributions in NumPy
lecture 173 Applications of Random Data in NumPy 4:9
lecture 174 Generating Data with NumPy - Exercise Text

Section 21 : Statistics with NumPy

lecture 175 Using Statistical Functions in NumPy 7:45
lecture 176 Minimal and Maximal Values in NumPy 6:2
lecture 177 Statistical Order Functions in NumPy 6:26
lecture 178 Averages and Variance in NumPy 4:17
lecture 179 Covariance and Correlation in NumPy 2:59
lecture 180 Histograms in NumPy (Part 1) 7:36
lecture 181 Histograms in NumPy (Part 2) 4:15
lecture 182 NAN Equivalent Functions in NumPy
lecture 183 Statistics with NumPy - Exercise Text

Section 22 : NumPy - Preprocessing

lecture 184 Checking for Missing Values in Ndarrays 9:24
lecture 185 Substituting Missing Values in Ndarrays 8:30
lecture 186 Reshaping Ndarrays 6:31
lecture 187 Removing Values from Ndarrays 4:21
lecture 188 Sorting Ndarrays 9:45
lecture 189 Argument Sort in NumPy 5:49
lecture 190 Argument Where in NumPy 11:13
lecture 191 Shuffling Ndarrays 6:52
lecture 192 Casting Ndarrays 6:14
lecture 193 Striping Values from Ndarrays 4:44
lecture 194 Stacking Ndarrays 10:31
lecture 195 Concatenating Ndarrays 6:28
lecture 196 Finding Unique Values in Ndarrays 5:4

Section 23 : A Loan Data Example with NumPy

lecture 197 Setting Up Introduction to the Practical Exam 4:50
lecture 198 Setting Up Importing the Data Set 4:10
lecture 199 Setting Up Checking for Incomplete Data 4:35
lecture 200 Setting Up Splitting the Dataset 5:28
lecture 201 Setting Up Creating Checkpoints 2:50
lecture 202 Manipulating Text Data Issue Date 5:27
lecture 203 Manipulating Text Data Loan Status and Term 7:8
lecture 204 Manipulating Text Data Grade and Sub Grade 8:55
lecture 205 Manipulating Text Data Verification Status 5:20
lecture 206 Manipulating Text Data State Address 6:2
lecture 207 Manipulating Text Data Converting Strings and 3:29
lecture 208 Manipulating Numeric Data Substitute Filler 7:52
lecture 209 Manipulating Numeric Data Currency Change 6:32
lecture 210 Manipulating Numeric Data Currency Change 8:22
lecture 211 Completing the Dataset 6:46

Section 24 : The Absenteeism Exercise - Introduction

lecture 212 An Introduction to the Absenteeism Exercise 1:12
lecture 213 The Absenteeism Exercise from a Business Pers 2:19
lecture 214 The Dataset 1:34

Section 25 : Solution to the Absenteeism Exercise

lecture 215 How to Complete the Absenteeism Exercise 1:58
lecture 216 Eyeball Your Data First 5:54
lecture 217 Note Programming vs the Rest of the World 3:28
lecture 218 Using a Statistical Approach to Solve Our Exe 2:18
lecture 219 Dropping the 'ID' Column 6:27
lecture 220 Analysis of the 'Reason for Absence' Column 5:4
lecture 221 Splitting the Reasons for Absence into Multip 8:38
lecture 222 Working with Dummy Variables - A Statistical 1:28
lecture 223 Grouping the Reason for Absence Columns 8:35
lecture 224 Concatenating Columns in a pandas DataFrame 4:35
lecture 225 Reordering Columns in a DataFrame 1:43
lecture 226 Working on the 'Date' Column 7:49
lecture 227 Extracting the Month Value from the 'Date' 7:0
lecture 228 Creating the 'Day of the Week' Column 3:36
lecture 229 Understanding the Meaning of 5 More Columns 3:18
lecture 230 Modifying the 'Education' Column 4:38
lecture 231 Final Remarks on the Absenteeism Exercise 1:41

Section 26 : Data Visualization

lecture 232 What Is Data Visualization and Why Is It Impo 4:31
lecture 233 Why Learn Data Visualization 6:8
lecture 234 Choosing the Right Visualization – What Are S 6:58
lecture 235 Introduction into Colors and Color Theory 8:56
lecture 236 Bar Chart - Introduction - General Theory and 1:30
lecture 237 Bar Chart - How to Create a Bar Chart Using 11:28
lecture 238 Bar Chart – Interpreting the Bar Graph. How 2:50
lecture 239 Pie Chart - Introduction - General Theory and 4:4
lecture 240 Pie Chart - How to Create a Pie Chart Using 6:39
lecture 241 Pie Chart – Interpreting the Pie Chart 1:32
lecture 242 Pie Chart - Why You Should Never Create a Pie 7:33
lecture 243 Stacked Area Chart - Introduction - General T 3:16
lecture 244 Stacked Area Chart - How to Create a Stacked 7:48
lecture 245 Stacked Area Chart - Interpreting the Stacked 2:30
lecture 246 Stacked Area Chart - How to Make a Good Stack
lecture 247 Line Chart - Introduction - General Theory 2:4
lecture 248 Line Chart - How to Create a Line Chart in Py 8:6
lecture 249 Line Chart - Interpretation 3:11
lecture 250 Line Chart - How to Make a Good Line Chart 6:30
lecture 251 Histogram - Introduction - General Theory. Ge
lecture 252 Histogram - How to Create a Histogram Using 5:43
lecture 253 Histogram – Interpreting the Histogram 2:12
lecture 254 Histogram – Choosing the Number of Bins in a 5:28
lecture 255 Histogram - How to Make a Good Histogram 4:43
lecture 256 Scatter Plot - Introduction - General Theory 2:29
lecture 257 Scatter Plot - How to Create a Scatter Plot 8:39
lecture 258 Scatter Plot – Interpreting the Scatter Plot 2:42
lecture 259 Scatter Plot - How to Make a Good Scatter Plo 2:57
lecture 260 Regression Plot - Introduction - General Theo 3:3
lecture 261 Regression Plot - How to Create a Regression 7:9
lecture 262 Regression Plot – Interpreting the Regression 4:36
lecture 263 Regression Plot - How to Make a Good Regressi 3:14
lecture 264 Bar and Line Chart - Introduction - General T 3:10
lecture 265 Bar and Line Chart - How to Create a Combinat 7:40
lecture 266 Bar and Line Chart – Interpreting the Combina 2:36
lecture 267 Bar and Line Chart – How to Make a Good Bar a 4:4
lecture 268 Data Visualization - Exercise Text