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Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization The problem Most 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 solution Our 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 analytics
- Basic Python
- Advanced Python
- NumPy
- Pandas
- Working with text files
- Data collection
- Data cleaning
- Data preprocessing
- Data visualization
- Final practical example

- 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

**+ Table of Contents**

**Introduction to the Course**

1 A Practical Example – What Will You Learn in This Course

2 What Does the Course Cover

3 Download All Resources

4 FAQ

**Introduction to Data Analytics**

5 Introduction to the World of Business and Data

6 Relevant Terms Explained

7 Data Analyst Compared to Other Data Jobs

8 Data Analyst Job Description

9 Why Python

**Setting up the Environment**

10 Introduction

11 Programming Explained in a Few Minutes

12 Jupyter – Introduction

13 Jupyter – Installing Anaconda

14 Jupyter – Intro to Using Jupyter

15 Jupyter – Working with Notebook Files

16 Jupyter – Using Shortcuts

17 Jupyter – Handling Error Messages

18 Jupyter – Restarting the Kernel

**Python Basics**

19 Python Variables

20 Types of Data – Numbers and Boolean Values

21 Types of Data – Strings

22 Basic Python Syntax – Arithmetic Operators

23 Basic Python Syntax – The Double Equality Sign

24 Basic Python Syntax – Reassign Values

25 Basic Python Syntax – Add Comments

26 Basic Python Syntax – Line Continuation

27 Basic Python Syntax – Indexing Elements

28 Basic Python Syntax – Indentation

29 Operators – Comparison Operators

30 Operators – Logical and Identity Operators

31 Conditional Statements – The IF Statement

32 Conditional Statements – The ELSE Statement

33 Conditional Statements – The ELIF Statement

34 Conditional Statements – A Note on Boolean Values

35 Functions – Defining a Function in Python

36 Functions – Creating a Function with a Parameter

37 Functions – Another Way to Define a Function

38 Functions – Using a Function in Another Function

39 Functions – Combining Conditional Statements and Functions

40 Functions – Creating Functions That Contain a Few Arguments

41 Functions – Notable Built-in Functions in Python

42 Sequences – Lists

43 Sequences – Using Methods

44 Sequences – List Slicing

45 Sequences – Tuples

46 Sequences – Dictionaries

47 Iteration – For Loops

48 Iteration – While Loops and Incrementing

49 Iteration – Create Lists with the range() Function

50 Iteration – Use Conditional Statements and Loops Together

51 Iteration – Conditional Statements, Functions, and Loops

52 Iteration – Iterating over Dictionaries

**Fundamentals for Coding in Python**

53 Object-Oriented Programming (OOP)

54 Modules, Packages, and the Python Standard Library

55 Importing Modules

56 Introduction to Using NumPy and pandas

57 What is Software Documentation

58 The Python Documentation

**Mathematics for Python**

59 What Is а Matrix

60 Scalars and Vectors

61 Linear Algebra and Geometry

62 Arrays in Python

63 What Is a Tensor

64 Adding and Subtracting Matrices

65 Errors When Adding Matrices

66 Transpose

67 Dot Product of Vectors

68 Dot Product of Matrices

69 Why is Linear Algebra Useful

**NumPy Basics**

70 The NumPy Package and Why We Use It

71 InstallingUpgrading NumPy

72 Ndarray

73 The NumPy Documentation

74 NumPy Basics – Exercise

**Pandas – Basics**

75 Introduction to the pandas Library

76 Installing and Running pandas

77 Introduction to pandas Series

78 Working with Attributes in Python

79 Using an Index in pandas

80 Label-based vs Position-based Indexing

81 More on Working with Indices in Python

82 Using Methods in Python – Part I

83 Using Methods in Python – Part II

84 Parameters vs Arguments

85 the pandas Documentation

86 Introduction to pandas DataFrames

87 Creating DataFrames from Scratch – Part I

88 Creating DataFrames from Scratch – Part II

89 Additional Notes on Using DataFrames

90 pandas Basics – Conclusion

**Working with Text Files**

91 Working with Files in Python – An Introduction

92 File vs File Object, Read vs Parse

93 Structured vs Semi-Structured and Unstructured Data

94 Data Connectivity through Text Files

95 Principles of Importing Data in Python

96 More on Text Files (.txt vs .csv)

97 Fixed-width Files

98 Common Naming Conventions Used in Programming

99 Importing Text Files in Python ( open() )

100 Importing Text Files in Python ( with open() )

101 Importing .csv Files with pandas – Part I

102 Importing .csv Files with pandas – Part II

103 Importing .csv Files with pandas – Part III

104 Importing Data with the index col Parameter

105 Importing Data with NumPy – .loadtxt() vs genfromtxt()

106 Importing Data with NumPy – Partial Cleaning While Importing

107 Importing Data with NumPy – Exercise

108 Importing .json Files

109 Prelude to Working with Excel Files in Python

110 Working with Excel Data (the .xlsx Format)

111 An Important Exercise on Importing Data in Python

112 Importing Data with the pandas’ Squeeze Parameter

113 A Note on Importing Files in Jupyter

114 Saving Your Data with pandas

115 Saving Your Data with NumPy – np.save()

116 Saving Your Data with NumPy – np.savez()

117 Saving Your Data with NumPy – np.savetxt()

118 Saving Your Data with NumPy – Exercise

119 Working with Text Files – Conclusion

**Working with Text Data**

120 Using the .format() Method

**Must-Know Python Tools**

121 Iterating Over Range Objects

122 Nested For Loops – Introduction

123 Triple Nested For Loops

124 List Comprehensions

125 Anonymous (Lambda) Functions

**Data GatheringData Collection**

126 What is data gatheringdata collection

**APIs (POST requests are not needed for this course)**

127 Overview of APIs

128 GET and POST Requests

129 Data Exchange Format for APIs JSON

130 Introducing the Exchange Rates API

131 Including Parameters in a GET Request

132 More Functionalities of the Exchange Rates API

133 Coding a Simple Currency Conversion Calculator

134 iTunes API

135 iTunes API Homework

136 iTunes API Structuring and Exporting the Data

137 Pagination GitHub API

138 APIs Exercise

**Data Cleaning and Data Preprocessing**

139 Data Cleaning and Data Preprocessing

**pandas Series**

140 unique(), .nunique()

141 Converting Series into Arrays

142 sort values()

143 Attribute and Method Chaining

144 sort index()

**NumPy Fundamentals**

145 Indexing in NumPy

146 Assigning Values in NumPy

147 Elementwise Properties of Arrays

148 Types of Data Supported by NumPy

149 Characteristics of NumPy Functions Part 1

150 Characteristics of NumPy Functions Part 2

151 NumPy Fundamentals – Exercise

**NumPy DataTypes**

152 ndarrays

153 Arrays vs Lists

154 Strings vs Object vs Number

155 NumPy DataTypes – Exercise

**Working with Arrays**

156 Basic Slicing in NumPy

157 Stepwise Slicing in NumPy

158 Conditional Slicing in NumPy

159 Dimensions and the Squeeze Function

160 Working with Arrays – Exercise

**Generating Data with NumPy**

161 Arrays of 0s and 1s

162 like functions in NumPy

163 A Non-Random Sequence of Numbers

164 Random Generators and Seeds

165 Basic Random Functions in NumPy

166 Probability Distributions in NumPy

167 Applications of Random Data in NumPy

168 Generating Data with NumPy – Exercise

**Statistics with NumPy**

169 Using Statistical Functions in NumPy

170 Minimal and Maximal Values in NumPy

171 Statistical Order Functions in NumPy

172 Averages and Variance in NumPy

173 Covariance and Correlation in NumPy

174 Histograms in NumPy (Part 1)

175 Histograms in NumPy (Part 2)

176 NAN Equivalent Functions in NumPy

177 Statistics with NumPy – Exercise

**NumPy – Preprocessing**

178 Checking for Missing Values in Ndarrays

179 Substituting Missing Values in Ndarrays

180 Reshaping Ndarrays

181 Removing Values from Ndarrays

182 Sorting Ndarrays

183 Argument Sort in NumPy

184 Argument Where in NumPy

185 Shuffling Ndarrays

186 Casting Ndarrays

187 Striping Values from Ndarrays

188 Stacking Ndarrays

189 Concatenating Ndarrays

190 Finding Unique Values in Ndarrays

**A Loan Data Example with NumPy**

191 Setting Up Introduction to the Practical Example

192 Setting Up Importing the Data Set

193 Setting Up Checking for Incomplete Data

194 Setting Up Splitting the Dataset

195 Setting Up Creating Checkpoints

196 Manipulating Text Data Issue Date

197 Manipulating Text Data Loan Status and Term

198 Manipulating Text Data Grade and Sub Grade

199 Manipulating Text Data Verification Status & URL

200 Manipulating Text Data State Address

201 Manipulating Text Data Converting Strings and Creating a Checkpoint

202 Manipulating Numeric Data Substitute Filler Values

203 Manipulating Numeric Data Currency Change – The Exchange Rate

204 Manipulating Numeric Data Currency Change – From USD to EUR

205 Completing the Dataset

**The Absenteeism Exercise – Introduction**

206 An Introduction to the Absenteeism Exercise

207 The Absenteeism Exercise from a Business Perspective

208 The Dataset

**Solution to the Absenteeism Exercise**

209 How to Complete the Absenteeism Exercise

210 Eyeball Your Data First

211 Note Programming vs the Rest of the World

212 Using a Statistical Approach to Solve Our Exercise

213 Dropping the ‘ID’ Column

214 Analysis of the ‘Reason for Absence’ Column

215 Splitting the Reasons for Absence into Multiple Dummy Variables

216 Working with Dummy Variables – A Statistical Perspective

217 Grouping the Reason for Absence Columns

218 Concatenating Columns in a pandas DataFrame

219 Reordering Columns in a DataFrame

220 Working on the ‘Date’ Column

221 Extracting the Month Value from the ‘Date’ Column

222 Creating the ‘Day of the Week’ Column

223 Understanding the Meaning of 5 More Columns

224 Modifying the ‘Education’ Column

225 Final Remarks on the Absenteeism Exercise

**Data Visualization**

226 What Is Data Visualization and Why Is It Important

227 Why Learn Data Visualization

228 Choosing the Right Visualization – What Are Some Popular Approaches and Framewor

229 Introduction into Colors and Color Theory

230 Bar Chart – Introduction – General Theory and Getting to Know the Dataset

231 Bar Chart – How to Create a Bar Chart Using Python

232 Bar Chart – Interpreting the Bar Graph. How to Make a Good Bar Graph

233 Pie Chart – Introduction – General Theory and Dataset

234 Pie Chart – How to Create a Pie Chart Using Python

235 Pie Chart – Interpreting the Pie Chart

236 Pie Chart – Why You Should Never Create a Pie Graph

237 Stacked Area Chart – Introduction – General Theory. Getting to Know the Dataset

238 Stacked Area Chart – How to Create a Stacked Area Chart Using Python

239 Stacked Area Chart – Interpreting the Stacked Area Graph

240 Stacked Area Chart – How to Make a Good Stacked Area Chart

241 Line Chart – Introduction – General Theory. Getting to Know the Dataset

242 Line Chart – How to Create a Line Chart in Python

243 Line Chart – Interpretation

244 Line Chart – How to Make a Good Line Chart

245 Histogram – Introduction – General Theory. Getting to Know the Dataset

246 Histogram – How to Create a Histogram Using Python

247 Histogram – Interpreting the Histogram

248 Histogram – Choosing the Number of Bins in a Histogram

249 Histogram – How to Make a Good Histogram

250 Scatter Plot – Introduction – General Theory. Getting to Know the Dataset

251 Scatter Plot – How to Create a Scatter Plot Using Python

252 Scatter Plot – Interpreting the Scatter Plot

253 Scatter Plot – How to Make a Good Scatter Plot

254 Regression Plot – Introduction – General Theory. Getting to Know the Dataset

255 Regression Plot – How to Create a Regression Scatter Plot Using Python

256 Regression Plot – Interpreting the Regression Scatter Plot

257 Regression Plot – How to Make a Good Regression Plot

258 Bar and Line Chart – Introduction – General Theory. Getting to Know the Dataset

259 Bar and Line Chart – How to Create a Combination Bar and Line Graph Using Python

260 Bar and Line Chart – Interpreting the Combination Bar and Line Graph

261 Bar and Line Chart – How to Make a Good Bar and Line Graph

262 Data Visualization – Exercise

**Conclusion**

263 Conclusion

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