The Data Analyst Course Complete Data Analyst Bootcamp

Free Download The Data Analyst Course Complete Data Analyst Bootcamp
Last updated 7/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.90 GB | Duration: 20h 57m
Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization

What you’ll learn
The course provides the complete preparation you need to become a data analyst
Fill up your resume with in-demand data skills: Python programming, NumPy, pandas, data preparation – data collection, data cleaning, data preprocessing, data visualization; data analysis, data analytics
Acquire a big picture understanding of the data analyst role
Learn beginner and advanced Python
Study mathematics for Python
We will teach you NumPy and pandas, basics and advanced
Be able to work with text files
Understand different data types and their memory usage
Learn how to obtain interesting, real-time information from an API with a simple script
Clean data with pandas Series and DataFrames
Complete a data cleaning exercise on absenteeism rate
Expand your knowledge of NumPy – statistics and preprocessing
Go through a complete loan data case study and apply your NumPy skills
Master data visualization
Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts
Engage with coding exercises that will prepare you for the job
Practice with real-world data
Solve a final capstone project
Requirements
No prior experience is required. We will start from the very basics
You’ll need to install Anaconda. We will show you how to do that step by step
Description
The problemMost data analyst, data science, and coding courses miss a critical practical step. They don’t teach you how to work with raw data, how to clean, and preprocess it. This creates a sizeable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.The bootcamps we have seen online and even live classes neglect this aspect and show you how to work with ‘clean’ data. But this isn’t doing you a favour. In reality, it will set you back both when you are applying for jobs, and when you’re on the job.The solutionOur goal is to provide you with complete preparation. And this course will turn you into a job-ready data analyst. To take you there, we will cover the following fundamental topics extensively.Theory about the field of data analyticsBasic PythonAdvanced PythonNumPyPandasWorking with text filesData collectionData cleaningData preprocessingData visualizationFinal practical exampleEach of these subjects builds on the previous ones. And this is precisely what makes our curriculum so valuable. Everything is shown in the right order and we guarantee that you are not going to get lost along the way, as we have provided all necessary steps in video (not a single one skipped). In other words, we are not going to teach you how to analyse data before you know how to gather and clean it.So, to prepare you for the entry-level job that leads to a data science position – data analyst – we created The Data Analyst Course.This is a rather unique training program because it teaches the fundamentals you need on the job. A frequently neglected aspect of vital importance.Moreover, our focus is to teach topics that flow smoothly and complement each other. The course provides complete preparation for someone who wants to become a data analyst at a fraction of the cost of traditional programs (not to mention the amount of time you will save). We believe that this resource will significantly boost your chances of landing a job, as it will prepare you for practical tasks and concepts that are frequently included in interviews.The topics we will cover1. Theory about the field of data analytics2. Basic Python3. Advanced Python4. NumPy5. Pandas6. Working with text files7. Data collection8. Data cleaning9. Data preprocessing10. Data visualization11. Final practical example1. Theory about the field of data analyticsHere we will focus on the big picture. But don’t imagine long boring pages with terms you’ll have to check up in a dictionary every minute. Instead, this is where we want to define who a data analyst is, what they do, and how they create value for an organization.Why learn it?You need a general understanding to appreciate how every part of the course fits in with the rest of the content. As they say, if you know where you are going, chances are that you will eventually get there. And since data analyst and other data jobs are relatively new and constantly evolving, we want to provide you with a good grasp of the data analyst role specifically. Then, in the following chapters, we will teach you the actual tools you need to become a data analyst.2. Basic PythonThis course is centred around Python. So, we’ll start from the very basics. Don’t be afraid if you do not have prior programming experience.Why learn it?You need to learn a programming language to take full advantage of the data-rich world we live in. Unless you are equipped with such a skill, you will always be dependent on other people’s ability to extract and manipulate data, and you want to be independent while doing analysis, right? Also, you don’t necessarily need to learn many programming languages at once. It is enough to be very skilled at just one, and we’ve naturally chosen Python which has established itself as the number one language for data analysis and data science (thanks to its rich libraries and versatility).3. Advanced PythonWe will introduce advanced Python topics such as working with text data and using tools such as list comprehensions and anonymous functions.Why learn it?These lessons will turn you into a proficient Python user who is independent on the job. You will be able to use Python’s core strengths to your advantage. So, here it is not just about the topics, it is also about the depth in which we explore the most relevant Python tools.4. NumPyNumPy is Python’s fundamental package for scientific computing. It has established itself as the go-to tool when you need to compute mathematical and statical operations.Why learn it?A large portion of a data analyst’s work is dedicated to preprocessing datasets. Unquestionably, this involves tons of mathematical and statistical techniques that NumPy is renowned for. In addition, the package introduces multi-dimensional array structures and provides a plethora of built-in functions and methods to use while working with them. In other words, NumPy can be described as a computationally stable state-of-the-art Python instrument that provides flexibility and can take your analysis to the next level.5. PandasThe pandas library is one of the most popular Python tools that facilitate data manipulation and analysis. It is very valuable because you can use it to manipulate all sorts of information – numerical tables and time series data, as well as text.Why learn it?Pandas is the other main tool an analyst needs to clean and preprocess the data they are working with. Its data manipulation features are second to none in Python because of the diversity and richness it provides in terms of methods and functions. The combined ability to work with both NumPy and pandas is extremely powerful as the two libraries complement each other. You need to be capable to operate with both to produce a complete and consistent analysis independently.6. Working with text filesExchanging information with text files is practically how we exchange information today. In this part of the course, we will use the Python, pandas, and NumPy tools learned earlier to give you the essentials you need when importing or saving data.Why learn it?In many courses, you are just given a dataset to practice your analytical and programming skills. However, we don’t want to close our eyes to reality, where converting a raw dataset from an external file into a workable Python format can be a massive challenge.7. Data collectionIn the real world, you don’t always have the data readily available for you. In this part of the course, you will learn how to retrieve data from an API.Why learn it?You need to know how to source your data, right? To be a well-rounded analyst you must be able to collect data from outside sources. This is rarely a one-click process. This section aims at providing you with all the necessary tools to do that on your own.8. Data cleaningThe next logical step is to clean your data. This is where you will apply the pandas skills acquired earlier in practice. All lessons throughout the course have a real-world perspective.Why learn it?A large part of a data analyst’s job in the real world involves cleaning data and preparing it for the actual analysis. You can’t expect that you’ll deal with flawless data sources, right? So, it will be up to you to overcome this stage and clean your data.9. Data preprocessingEven when your dataset is clean and in an understandable shape, it isn’t quite ready to be processed for visualizations and analysis just yet. There is a crucial step in between, and that’s data preprocessing.Why learn it?Data preprocessing is where a data analyst can demonstrate how good or great they are at their job. This stage of the work requires the ability to choose the right statistical tool that will improve the quality of your dataset and the knowledge to implement it with advanced pandas and NumPy techniques. Only when you’ve completed this step can you say that your dataset is preprocessed and ready for the next part, which is data visualization.10. Data visualizationData visualization is the face of data. Many people look at the data and see nothing. The reason for that is that they are not creating good visualizations. Or even worse – they are creating nice graphs but cannot interpret them accurately.Why learn it?This part of the course will teach you how to use your data to produce meaningful insights. At the end of the day, data charts are what conveys the most information in the shortest amount of time. And nothing speaks better than a well crafted and meaningful data visualization.11. Practical exampleThe course contains plenty of exercises and practical cases. In the end, we have included a comprehensive practical example that will show you how everything you have learned along the way comes nicely together. This is where you will be able to appreciate how far you have come in your journey to becoming a data analyst and starting your data career.What you getA program worth $1,250Active Q&A supportAll the knowledge to become a data analystA community of aspiring data analystsA certificate of completionAccess to frequent future updatesReal-world trainingGet ready to become a data analyst from scratchWhy wait? Every day is a missed opportunity.Click the “Buy Now” button and become a part of our data analyst program today.
Overview
Section 1: Introduction to the Course
Lecture 1 A Practical Example – What Will You Learn in This Course?
Lecture 2 What Does the Course Cover?
Lecture 3 Download All Resources
Lecture 4 FAQ
Section 2: Introduction to Data Analytics
Lecture 5 Introduction to the World of Business and Data
Lecture 6 Relevant Terms Explained
Lecture 7 Data Analyst Compared to Other Data Jobs
Lecture 8 Data Analyst Job Description
Lecture 9 Why Python
Section 3: Setting up the Environment
Lecture 10 Introduction
Lecture 11 Programming Explained in a Few Minutes
Lecture 12 Jupyter – Introduction
Lecture 13 Jupyter – Installing Anaconda
Lecture 14 Jupyter – Intro to Using Jupyter
Lecture 15 Jupyter – Working with Notebook Files
Lecture 16 Jupyter – Using Shortcuts
Lecture 17 Jupyter – Handling Error Messages
Lecture 18 Jupyter – Restarting the Kernel
Section 4: Python Basics
Lecture 19 Python Variables
Lecture 20 Types of Data – Numbers and Boolean Values
Lecture 21 Types of Data – Strings
Lecture 22 Basic Python Syntax – Arithmetic Operators
Lecture 23 Basic Python Syntax – The Double Equality Sign
Lecture 24 Basic Python Syntax – Reassign Values
Lecture 25 Basic Python Syntax – Add Comments
Lecture 26 Basic Python Syntax – Line Continuation
Lecture 27 Basic Python Syntax – Indexing Elements
Lecture 28 Basic Python Syntax – Indentation
Lecture 29 Operators – Comparison Operators
Lecture 30 Operators – Logical and Identity Operators
Lecture 31 Conditional Statements – The IF Statement
Lecture 32 Conditional Statements – The ELSE Statement
Lecture 33 Conditional Statements – The ELIF Statement
Lecture 34 Conditional Statements – A Note on Boolean Values
Lecture 35 Functions – Defining a Function in Python
Lecture 36 Functions – Creating a Function with a Parameter
Lecture 37 Functions – Another Way to Define a Function
Lecture 38 Functions – Using a Function in Another Function
Lecture 39 Functions – Combining Conditional Statements and Functions
Lecture 40 Functions – Creating Functions That Contain a Few Arguments
Lecture 41 Functions – Notable Built-in Functions in Python
Lecture 42 Sequences – Lists
Lecture 43 Sequences – Using Methods
Lecture 44 Sequences – List Slicing
Lecture 45 Sequences – Tuples
Lecture 46 Sequences – Dictionaries
Lecture 47 Iteration – For Loops
Lecture 48 Iteration – While Loops and Incrementing
Lecture 49 Iteration – Create Lists with the range() Function
Lecture 50 Iteration – Use Conditional Statements and Loops Together
Lecture 51 Iteration – Conditional Statements, Functions, and Loops
Lecture 52 Iteration – Iterating over Dictionaries
Section 5: Fundamentals for Coding in Python
Lecture 53 Object-Oriented Programming (OOP)
Lecture 54 Modules, Packages, and the Python Standard Library
Lecture 55 Importing Modules
Lecture 56 Introduction to Using NumPy and pandas
Lecture 57 What is Software Documentation?
Lecture 58 The Python Documentation
Section 6: Mathematics for Python
Lecture 59 What Is а Matrix?
Lecture 60 Scalars and Vectors
Lecture 61 Linear Algebra and Geometry
Lecture 62 Arrays in Python
Lecture 63 What Is a Tensor?
Lecture 64 Adding and Subtracting Matrices
Lecture 65 Errors When Adding Matrices
Lecture 66 Transpose
Lecture 67 Dot Product of Vectors
Lecture 68 Dot Product of Matrices
Lecture 69 Why is Linear Algebra Useful
Section 7: NumPy Basics
Lecture 70 The NumPy Package and Why We Use It
Lecture 71 Installing/Upgrading NumPy
Lecture 72 Ndarray
Lecture 73 The NumPy Documentation
Lecture 74 NumPy Basics – Exercise
Section 8: Pandas – Basics
Lecture 75 Introduction to the pandas Library
Lecture 76 Installing and Running pandas
Lecture 77 A Note on Completing the Upcoming Coding Exercises
Lecture 78 Introduction to pandas Series
Lecture 79 Working with Attributes in Python
Lecture 80 Using an Index in pandas
Lecture 81 Label-based vs Position-based Indexing
Lecture 82 More on Working with Indices in Python
Lecture 83 Using Methods in Python – Part I
Lecture 84 Using Methods in Python – Part II
Lecture 85 Parameters vs Arguments
Lecture 86 The pandas Documentation
Lecture 87 Introduction to pandas DataFrames
Lecture 88 Creating DataFrames from Scratch – Part I
Lecture 89 Creating DataFrames from Scratch – Part II
Lecture 90 Additional Notes on Using DataFrames
Lecture 91 pandas Basics – Conclusion
Section 9: Working with Text Files
Lecture 92 Working with Files in Python – An Introduction
Lecture 93 File vs File Object, Read vs Parse
Lecture 94 Structured vs Semi-Structured and Unstructured Data
Lecture 95 Data Connectivity through Text Files
Lecture 96 Principles of Importing Data in Python
Lecture 97 More on Text Files (*.txt vs *.csv)
Lecture 98 Fixed-width Files
Lecture 99 Common Naming Conventions Used in Programming
Lecture 100 Importing Text Files in Python ( open() )
Lecture 101 Importing Text Files in Python ( with open() )
Lecture 102 Importing *.csv Files with pandas – Part I
Lecture 103 Importing *.csv Files with pandas – Part II
Lecture 104 Importing *.csv Files with pandas – Part III
Lecture 105 Importing Data with the "index_col" Parameter
Lecture 106 Importing Data with NumPy – .loadtxt() vs genfromtxt()
Lecture 107 Importing Data with NumPy – Partial Cleaning While Importing
Lecture 108 Importing Data with NumPy – Exercise
Lecture 109 Importing *.json Files
Lecture 110 Prelude to Working with Excel Files in Python
Lecture 111 Working with Excel Data (the *.xlsx Format)
Lecture 112 An Important Exercise on Importing Data in Python
Lecture 113 Importing Data with the pandas’ "Squeeze" Method
Lecture 114 A Note on Importing Files in Jupyter
Lecture 115 Saving Your Data with pandas
Lecture 116 Saving Your Data with NumPy – np.save()
Lecture 117 Saving Your Data with NumPy – np.savez()
Lecture 118 Saving Your Data with NumPy – np.savetxt()
Lecture 119 Saving Your Data with NumPy – Exercise
Lecture 120 Working with Text Files – Conclusion
Section 10: Working with Text Data
Lecture 121 Working with Text Data and Argument Specifiers
Lecture 122 Manipulating Python Strings
Lecture 123 Using Various Python String Methods – Part I
Lecture 124 Using Various Python String Methods – Part II
Lecture 125 String Accessors
Lecture 126 Using the .format() Method
Section 11: Must-Know Python Tools
Lecture 127 Iterating Over Range Objects
Lecture 128 Nested For Loops – Introduction
Lecture 129 Triple Nested For Loops
Lecture 130 List Comprehensions
Lecture 131 Anonymous (Lambda) Functions
Section 12: Data Gathering/Data Collection
Lecture 132 What is data gathering/data collection?
Section 13: APIs (POST requests are not needed for this course)
Lecture 133 Overview of APIs
Lecture 134 GET and POST Requests
Lecture 135 Data Exchange Format for APIs: JSON
Lecture 136 Introducing the Exchange Rates API
Lecture 137 Including Parameters in a GET Request
Lecture 138 More Functionalities of the Exchange Rates API
Lecture 139 Coding a Simple Currency Conversion Calculator
Lecture 140 iTunes API
Lecture 141 iTunes API: Homework
Lecture 142 iTunes API: Structuring and Exporting the Data
Lecture 143 Pagination: GitHub API
Lecture 144 APIs: Exercise
Section 14: Data Cleaning and Data Preprocessing
Lecture 145 Data Cleaning and Data Preprocessing
Section 15: pandas Series
Lecture 146 .unique(), .nunique()
Lecture 147 Converting Series into Arrays
Lecture 148 .sort_values()
Lecture 149 Attribute and Method Chaining
Lecture 150 .sort_index()
Section 16: pandas DataFrames
Lecture 151 A Revision to pandas DataFrames
Lecture 152 Common Attributes for Working with DataFrames
Lecture 153 Data Selection in pandas DataFrames
Lecture 154 Data Selection – Indexing with .iloc[]
Lecture 155 Data Selection – Indexing with .loc[]
Lecture 156 A Few Comments on Using .loc[] and .iloc[]
Section 17: NumPy Fundamentals
Lecture 157 Indexing in NumPy
Lecture 158 Assigning Values in NumPy
Lecture 159 Elementwise Properties of Arrays
Lecture 160 Types of Data Supported by NumPy
Lecture 161 Characteristics of NumPy Functions Part 1
Lecture 162 Characteristics of NumPy Functions Part 2
Lecture 163 NumPy Fundamentals – Exercise
Section 18: NumPy DataTypes
Lecture 164 ndarrays
Lecture 165 Arrays vs Lists
Lecture 166 Strings vs Object vs Number
Lecture 167 NumPy DataTypes – Exercise
Section 19: Working with Arrays
Lecture 168 Basic Slicing in NumPy
Lecture 169 Stepwise Slicing in NumPy
Lecture 170 Conditional Slicing in NumPy
Lecture 171 Dimensions and the Squeeze Function
Lecture 172 Working with Arrays – Exercise
Section 20: Generating Data with NumPy
Lecture 173 Arrays of 0s and 1s
Lecture 174 "_like" functions in NumPy
Lecture 175 A Non-Random Sequence of Numbers
Lecture 176 Random Generators and Seeds
Lecture 177 Basic Random Functions in NumPy
Lecture 178 Probability Distributions in NumPy
Lecture 179 Applications of Random Data in NumPy
Lecture 180 Generating Data with NumPy – Exercise
Section 21: Statistics with NumPy
Lecture 181 Using Statistical Functions in NumPy
Lecture 182 Minimal and Maximal Values in NumPy
Lecture 183 Statistical Order Functions in NumPy
Lecture 184 Averages and Variance in NumPy
Lecture 185 Covariance and Correlation in NumPy
Lecture 186 Histograms in NumPy (Part 1)
Lecture 187 Histograms in NumPy (Part 2)
Lecture 188 NAN Equivalent Functions in NumPy
Lecture 189 Statistics with NumPy – Exercise
Section 22: NumPy – Preprocessing
Lecture 190 Checking for Missing Values in Ndarrays
Lecture 191 Substituting Missing Values in Ndarrays
Lecture 192 Reshaping Ndarrays
Lecture 193 Removing Values from Ndarrays
Lecture 194 Sorting Ndarrays
Lecture 195 Argument Sort in NumPy
Lecture 196 Argument Where in NumPy
Lecture 197 Shuffling Ndarrays
Lecture 198 Casting Ndarrays
Lecture 199 Striping Values from Ndarrays
Lecture 200 Stacking Ndarrays
Lecture 201 Concatenating Ndarrays
Lecture 202 Finding Unique Values in Ndarrays
Section 23: A Loan Data Example with NumPy
Lecture 203 Setting Up: Introduction to the Practical Example
Lecture 204 Setting Up: Importing the Data Set
Lecture 205 Setting Up: Checking for Incomplete Data
Lecture 206 Setting Up: Splitting the Dataset
Lecture 207 Setting Up: Creating Checkpoints
Lecture 208 Manipulating Text Data: Issue Date
Lecture 209 Manipulating Text Data: Loan Status and Term
Lecture 210 Manipulating Text Data: Grade and Sub Grade
Lecture 211 Manipulating Text Data: Verification Status & URL
Lecture 212 Manipulating Text Data: State Address
Lecture 213 Manipulating Text Data: Converting Strings and Creating a Checkpoint
Lecture 214 Manipulating Numeric Data: Substitute Filler Values
Lecture 215 Manipulating Numeric Data: Currency Change – The Exchange Rate
Lecture 216 Manipulating Numeric Data: Currency Change – From USD to EUR
Lecture 217 Completing the Dataset
Section 24: The "Absenteeism" Exercise – Introduction
Lecture 218 An Introduction to the "Absenteeism" Exercise
Lecture 219 The "Absenteeism" Exercise from a Business Perspective
Lecture 220 The Dataset
Section 25: Solution to the "Absenteeism" Exercise
Lecture 221 How to Complete the Absenteeism Exercise
Lecture 222 Eyeball Your Data First
Lecture 223 Note: Programming vs the Rest of the World
Lecture 224 Using a Statistical Approach to Solve Our Exercise
Lecture 225 Dropping the ‘ID’ Column
Lecture 226 Analysis of the ‘Reason for Absence’ Column
Lecture 227 Splitting the Reasons for Absence into Multiple Dummy Variables
Lecture 228 Working with Dummy Variables – A Statistical Perspective
Lecture 229 Grouping the Reason for Absence Columns
Lecture 230 Concatenating Columns in a pandas DataFrame
Lecture 231 Reordering Columns in a DataFrame
Lecture 232 Creating Checkpoints
Lecture 233 Working on the ‘Date’ Column
Lecture 234 Extracting the Month Value from the ‘Date’ Column
Lecture 235 Creating the ‘Day of the Week’ Column
Lecture 236 Understanding the Meaning of 5 More Columns
Lecture 237 Modifying the ‘Education’ Column
Lecture 238 Final Remarks on the Absenteeism Exercise
Section 26: Data Visualization
Lecture 239 What Is Data Visualization and Why Is It Important?
Lecture 240 Why Learn Data Visualization?
Lecture 241 Choosing the Right Visualization – What Are Some Popular Approaches and Framewor
Lecture 242 Introduction into Colors and Color Theory
Lecture 243 Bar Chart – Introduction – General Theory and Getting to Know the Dataset
Lecture 244 Bar Chart – How to Create a Bar Chart Using Python
Lecture 245 Bar Chart – Interpreting the Bar Graph. How to Make a Good Bar Graph
Lecture 246 Pie Chart – Introduction – General Theory and Dataset
Lecture 247 Pie Chart – How to Create a Pie Chart Using Python
Lecture 248 Pie Chart – Interpreting the Pie Chart
Lecture 249 Pie Chart – Why You Should Never Create a Pie Graph
Lecture 250 Stacked Area Chart – Introduction – General Theory. Getting to Know the Dataset
Lecture 251 Stacked Area Chart – How to Create a Stacked Area Chart Using Python
Lecture 252 Stacked Area Chart – Interpreting the Stacked Area Graph
Lecture 253 Stacked Area Chart – How to Make a Good Stacked Area Chart
Lecture 254 Line Chart – Introduction – General Theory. Getting to Know the Dataset
Lecture 255 Line Chart – How to Create a Line Chart in Python
Lecture 256 Line Chart – Interpretation
Lecture 257 Line Chart – How to Make a Good Line Chart
Lecture 258 Histogram – Introduction – General Theory. Getting to Know the Dataset
Lecture 259 Histogram – How to Create a Histogram Using Python
Lecture 260 Histogram – Interpreting the Histogram
Lecture 261 Histogram – Choosing the Number of Bins in a Histogram
Lecture 262 Histogram – How to Make a Good Histogram
Lecture 263 Scatter Plot – Introduction – General Theory. Getting to Know the Dataset
Lecture 264 Scatter Plot – How to Create a Scatter Plot Using Python
Lecture 265 Scatter Plot – Interpreting the Scatter Plot
Lecture 266 Scatter Plot – How to Make a Good Scatter Plot
Lecture 267 Regression Plot – Introduction – General Theory. Getting to Know the Dataset
Lecture 268 Regression Plot – How to Create a Regression Scatter Plot Using Python
Lecture 269 Regression Plot – Interpreting the Regression Scatter Plot
Lecture 270 Regression Plot – How to Make a Good Regression Plot
Lecture 271 Bar and Line Chart – Introduction – General Theory. Getting to Know the Dataset
Lecture 272 Bar and Line Chart – How to Create a Combination Bar and Line Graph Using Python
Lecture 273 Bar and Line Chart – Interpreting the Combination Bar and Line Graph
Lecture 274 Bar and Line Chart – How to Make a Good Bar and Line Graph
Lecture 275 Data Visualization – Exercise
Section 27: Conclusion
Lecture 276 Conclusion
Lecture 277 Bonus
You should take this course if you want to become a Data Analyst and Data Scientist,This course is for you if you want a great career,The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
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