Python Data Analysis and Data Engineering

What you'll learn

• Master Data Analysis With Python

• Master Beginner To Advance Level Data Analytics Techniques

• Learn The Latest Data Analytics Skills And Techniques In 2021

• Master How To Deal With Messy Data(outliers, missing values, data imbalance, data leakage etc.)

• Know How To Deal With Complex Data Cleaning Issues In Python

• Learn Automated Modern Tools And Libraries For Professional Data Cleaning And Analysis

• Get The Skill Needed To Be Part Of The Top 10% Data Analytics and Data Science

• Learn The Best Ways To Prepare Your Data To Build Machine Learning Models

• Master Different Techniques Of Dealing With Raw Data

• Master The Art Of Visualisation And Data Story Telling

• Perform Industry Level Data Engineering

Reed courses certificate of completion

Digital certificate - Included

• Section 1: Course Curriculum 06:24

  • 1: 1. Curriculum Preview 06:24

• Section 2: Welcome 00:31

  • 2: 2. Welcome Preview 00:31

• Section 3: All Course Resources 01:00

  • 3: 3. All Course Materials 01:00

• Section 4: Introduction to Data Analysis 11:03

  • 4: 4. Introduction 11:03

• Section 5: Introduction To Jupyter Notebook 15:26

  • 5: 5. Getting Started With Anaconda 04:11
  • 6: 6. Introduction To Jupyter Notebook: Code Vs Markdown Vs Raw 05:00
  • 7: 7. Introduction To Jupyter Notebook: Working With Text 02:51
  • 8: 8. Introduction To Jupyter Notebook: Working With Code Saving Exporting Notebook 03:24

• Section 6: Introduction To Google Colab 12:37

  • 9: 9. Introduction To Google Colab: Overview 04:48
  • 10: 10. Introduction To Google Colab: Working With Text 04:49
  • 11: 11. Introduction To Google Colab: Saving & Exporting Notebook 03:00

• Section 7: Data Analysis Tools 04:06

  • 12: 12. Introduction to Data Analysis Tools 04:06

• Section 8: BONUS: Python Crash Course 6:52:35

  • 13: 13. IMPORTANT 01:00
  • 14: 14. Lecture Resources 01:00
  • 15: 15. Introduction to Python 10:36
  • 16: 16. Data Types In Python 18:04
  • 17: 17. List In Python 16:55
  • 18: 18. Operations On Python List-Split,Count,Index, Reverse,Append,Remove,Pop,Sort 10:55
  • 19: 19. Range in Python 09:16
  • 20: 20. List Comprehension In Python 06:16
  • 21: 21. Dataframe In Python 16:00
  • 22: 22. SET In Python 16:00
  • 23: 23. Dictionary In Python 07:11
  • 24: 24. Functions In Python 29:38
  • 25: 25. Conditional Statements In Python 22:27
  • 26: 26. Introduction To Numpy 15:40
  • 27: 27. Operations On Numpy 29:02
  • 28: 28. Indexing And Slicing 1 24:28
  • 29: 29. Indexing And Slicing 2 21:16
  • 30: Assignment 1: Numpy Assignment 01:00
  • 31: 30. Pandas Dataframe 1: Series 30:27
  • 32: 31. Iloc and Loc For Pandas Dataframe 07:48
  • 33: 32. Pandas Dataframe 13:28
  • 34: 33.Operations On Pandas Dataframe: Slicing Columns, Adding/Drop Columns and Rows 18:49
  • 35: 34.Operations On Pandas DataFrame: Setting Column Index,Rename DataFrame Columns 10:02
  • 36: 35. Reading Dataset In Pamdas 08:32
  • 37: 36. Working With Dataset:Checking for Missing/Null Values,Head of dataset 24:10
  • 38: 37. Working With Dataset: Fillna, Sorting,Value_Counts,Concatenation,Join&Merge 23:50
  • 39: 38. Join And Merge In Pandas 17:45
  • 40: Assignment 2: Pandas Assignment 01:00

• Section 9: Levels Of Data Analysis Techniques 01:00

  • 41: Introduction 01:00

• Section 10: Univariate Data Analysis 20:39

  • 42: 40. Lesson 1: What Is A Univariate Data Analysis? 03:35
  • 43: 41. Lesson 2: Dataset Overview 04:31
  • 44: 42. Lesson 3: Identify Data Types & Missing Values 04:19
  • 45: 43. Lesson 4: Feature Distribution(Unique & Countplot) 04:11
  • 46: 44. Lesson 5: Feature Distribution(Displot) 04:03

• Section 11: Bivariate Data Analysis 04:56

  • 47: 45. Bivariate Data Analysis 04:56

• Section 12: Multivariate Data Analysis 09:54

  • 48: 46. Multivariate Data Analysis 03:10
  • 49: 47. Multivariate Data Analysis: Barplot 01:44
  • 50: 48. Multivariate Data Analysis: Pairplot 05:00

• Section 13: Data Visualisation: Introduction To Data Visualisation Libraries 28:46

  • 51: 49. Introduction 04:59
  • 52: 50. Matplotlib 02:54
  • 53: 51. Seaborn 02:59
  • 54: 52. Plotly 03:33
  • 55: 53. Plotly: Display Output In Google Colab 02:51
  • 56: 54. Bokeh 03:38
  • 57: 55. Altair 03:57
  • 58: 56. ggplot 03:55

• Section 14: Plot Different Types of Data Visualisations using Various Libraries 2:09:27

  • 59: 57. Bar Graph: Dataset Overview 02:56
  • 60: 58. Plotting Bar Graphs 04:09
  • 61: 59. Horizontal Bar Graphs 01:25
  • 62: 60. Stacked Bar Graphs 02:22
  • 63: 61. Group Bar Graphs 01:08
  • 64: 62. Histogram 02:44
  • 65: 63. Marginal Histogram 01:42
  • 66: 64. Facet Histogram 02:04
  • 67: 65. Distplot 03:34
  • 68: 66. Bivariate and Multivariate Distplot 03:10
  • 69: 67. Hexplot 01:56
  • 70: 68. Scatter Plot 02:54
  • 71: 69. Relplot 02:37
  • 72: 70. LM Plot 01:41
  • 73: 71. Tree Plot 04:18
  • 74: 72. Pie Chart 02:35
  • 75: 73. Heat Map 04:22
  • 76: 74. Scatter Matrix 02:22
  • 77: 75. Sunburst 02:55
  • 78: 76. Sunburst Interactive Plotting 1 05:00
  • 79: 77. Sunburst Interactive Plotting 2 02:01
  • 80: 78. Funnel Plot 02:53
  • 81: 79. Bubble Plot 04:46
  • 82: 80. Box Plot 04:38
  • 83: 81. Violin Plot 02:05
  • 84: 82. Wind Rose Chart 02:40
  • 85: 83. Word Cloud: Overview Of Dataset 02:14
  • 86: 84. Creating WordCloud 04:21
  • 87: 85. Folium: Overview Of Dataset 02:06
  • 88: 86. Folium Plot 05:00
  • 89: 87. Time Series Plot 03:48
  • 90: 88. Multi-Variable Time Series Plot 04:13
  • 91: 89. 3D Plots With Matplotlib 03:12
  • 92: 90. 3D Plots With Plotly 04:06
  • 93: 91. 3D Plots With Plotly Output 02:09
  • 94: 92 - Animated plot with plotly 03:13
  • 95: 93. Animated Plot With Matplotlib: COVID 19 Dataset 02:29
  • 96: 94. Animated Plot With Matplotlib/ Code Walk Through 1 02:03
  • 97: 95. Animated Plot With Matplotlib/ Code Walk Through 2 03:57
  • 98: 96. Animated Plot With Matplotlib: Code Walk Through 3 02:04
  • 99: 97. Animated Plot With Matplotlib: Code Walk Through 4 04:46
  • 100: 98. Animated Plot With Matplotlib: Final Output 04:49

• Section 15: Feature Engineering 10:27

  • 101: 99. Introduction To Feature Engineering 04:14
  • 102: 100. What Is Feature Engineering? 04:25
  • 103: 101. What We Will Learn 01:48

• Section 16: How To Deal With Data Types In Python 27:06

  • 104: 102. Lesson 1: Integer & Floating Point Numbers 04:21
  • 105: 103. Lesson 2: Complex Numbers & Strings 03:50
  • 106: 104. Lesson 3: LIST 02:38
  • 107: 105. Lesson 4: Tuple & List Mutability 04:59
  • 108: 106. Lesson 5: Tuple Immutability 03:26
  • 109: 107. Lesson 6: Set 02:53
  • 110: 108. Lesson 7: Dictionary 04:59

• Section 17: Working With Data Types In Python 37:13

  • 111: 109. Lesson 1: Continuous Vs Discrete 03:58
  • 112: 110. Lesson 2: Dataset Intro 02:53
  • 113: 111. Lesson 3: Describing The Dataset 03:56
  • 114: 112. Lesson 4: Rounding/Bucketing/Binning 03:03
  • 115: 113. Lesson 5: Rounding(Hands-On Demonstration) 04:34
  • 116: 114. Lesson 6: Rounding Continuation 00:45
  • 117: 115. Lesson 7: Counts 04:16
  • 118: 116. Lesson 8: Binarization 05:00
  • 119: 117. Lesson 9: Binning / Quantisation/ Grouping 03:57
  • 120: 118. Lesson 10: Bining / Quantisation/ group (Continuation) 04:51

• Section 18: How To Deal With Date And Time In Python 1:12:39

  • 121: 119. Introduction 04:23
  • 122: 120. How To Extract Day, Month and Year from a given Time 02:15
  • 123: 121. How to Extract Hours, Minutes, Seconds and Micro-seconds from a given Time 02:03
  • 124: 122. How To Update current Date 01:44
  • 125: 123. Working With TimeDelta in Python 06:12
  • 126: 124. How To Extract Week-Day From A Given Date 1 04:06
  • 127: 125. How To Extract Week-Day From A Given Date 2 03:07
  • 128: 126. How To How To Generate Calendar 02:07
  • 129: 127. How To Format Date and Time in Python 01:41
  • 130: 128. Date and Time Formatting Using STRFTIME and STRPTIME 04:48
  • 131: 129. How To Extract The Year, Month, Day Time Using STRFTIME 01:51
  • 132: 130. How To Work With Timestamp Preview 04:22
  • 133: 131. How To Convert Strings To DateTime Using STRPTIME 01:20
  • 134: 132. How To Handle Different Time Zones 06:58
  • 135: 133. DataFrame: Get Year, Month and Day from a DataFrame 1 02:39
  • 136: 134. DataFrame: Get Year, Month and Day from a DataFrame 2 Preview 04:55
  • 137: 135. DataFrame: How to Get The Week and Leap Year from DataFrame 04:57
  • 138: 136. DataFrame: How to Get Age from Date 02:48
  • 139: 137. Operations on Date and Time with Dataset 1 03:37
  • 140: 138. Operations on Date and Time with Dataset 2 04:05
  • 141: 139. Operations on Date and Time with Dataset 3 02:41

• Section 19: How To Deal With Missing Values 54:16

  • 142: 140. Lesson 1: What are Missing Values? 03:47
  • 143: 141. Lesson 2: Overview Of Dataset 04:57
  • 144: 142. Lesson 3: Counting And Replacing Missing Values 04:36
  • 145: 143. Lesson 4: Replacing Missing Values With NaN 02:58
  • 146: 144. Lesson 5: Visualising The Missing Using MissingNO (Matrix) 03:59
  • 147: 145. Lesson 6: Visualising The Missing Using MissingNO (Bar Plot) 02:48
  • 148: 146. Lesson 7: Linear Discriminant Analysis 04:39
  • 149: 147. Lesson 8: Dropping Missing Values Using Dropna 04:30
  • 150: 148. Lesson 9: Linear Discriminant Analysis With No Missing Value 01:03
  • 151: 149. Lesson 10: Missing Value Imputation 01:09
  • 152: 150. Lesson 11: Feature Distribution 04:04
  • 153: 151. Lesson 12: Outlier Effect 05:00
  • 154: 152. Lesson 13: Impute Missing Values With The Right Statistics 04:13
  • 155: 153. Lesson 14: Simple Imputer 02:29
  • 156: 154. Lesson 14: Testing Machine Learning Model on Clean Dataset 04:04

• Section 20: How To Deal With Outliers 43:10

  • 157: 155. Introduction 04:15
  • 158: 156. Overview Of Dataset 04:56
  • 159: 157. Detect Outliers With Boxplot 02:52
  • 160: 158. Detect Outliers With Scatterplot 02:25
  • 161: 159. Detect Outliers With Zscore 04:47
  • 162: 160. Detect Outliers Using Inter-quartile Range 03:27
  • 163: 161. Detect Outliers Using Inter-quartile Range And Boxplot 04:53
  • 164: 162. Remove Outliers Using Inter-quartile Range And Boxplot 01:54
  • 165: 163. Outliers Detection And Removal Using Custom Function 04:24
  • 166: 164. Automatic Outliers Detection With One-Class Classification(OCC) 04:18
  • 167: 165. Automatic Outliers Detection: Local Outlier Factor 04:59

• Section 21: Automated Exploratory Data Analysis (EDA) 01:00

  • 168: 166. Introduction 01:00

• Section 22: Pandas Profiling 18:59

  • 169: 167. Introduction 04:12
  • 170: 168. Installation & Dataset Overview 04:24
  • 171: 169. Generate Profile Report 1 05:00
  • 172: 170. Generate Profile Report 2 03:10
  • 173: 171. Generate Profile Widgets 02:13

• Section 23: Autoviz 10:39

  • 174: 172. Autoviz Part 1 03:59
  • 175: 173. Autoviz Part 2 03:54
  • 176: 174. Autoviz Part 3 02:46

• Section 24: Sweetviz 13:31

  • 177: 175. Loading Dataset 02:10
  • 178: 176. Generate Sweetviz Report 04:59
  • 179: 177. Sweetviz Bivariate Variable Report 1 02:51
  • 180: 178. Sweetviz Bivariate Variable Report 2 03:31

• Section 25: Dora 15:14

  • 181: 179. Introduction To DORA 04:26
  • 182: 180. One-Hot Encoding 04:30
  • 183: 181. One-Hot Encoding 2 03:20
  • 184: 182. Feature Transformation 02:58

• Section 26: How To Deal With Categorical Variables 22:18

  • 185: 183. Introduction 00:35
  • 186: 184. Difference between Features and Observations 02:18
  • 187: 185. What are Categorical Variables? 02:21
  • 188: 186. Norminal Vs Ordinal Categorical Variables 01:25
  • 189: 187. Problems With Categorical Variables 02:32
  • 190: 188. Overview Of Dataset 04:39
  • 191: 189. Description Of Dataset 02:29
  • 192: 190. Examples Of Ordinal Categorical Variables 1 02:22
  • 193: 191. Examples Of Ordinal Categorical Variables 2 03:37

• Section 27: Feature Encoding 01:00

  • 194: 192. Introduction 01:00

• Section 28: Manual Encoding Of Categorical Variables 02:42

  • 195: 193. Manual Encoding: Ordinal Categorical Variables 02:42

• Section 29: Label Encoding 23:22

  • 196: 194. Label Encoding: Ordinal Categorical Variables 02:48
  • 197: 195. Identify Labels 03:56
  • 198: 196. Label Encoding: Norminal Categorical Variables 02:20
  • 199: 197. Label Encoding Variable 1 03:50
  • 200: 198. High Cardinality 04:20
  • 201: 199. Label Encoding High Cardinality Variables 02:26
  • 202: 200. Problem With Label Encoding Preview 03:42

• Section 30: One-Hot Encoding 09:55

  • 203: 201. Introduction 02:38
  • 204: 202. One-Hot Encoding Categorical Variables 03:51
  • 205: 203. DataFrame With One-Hot Encoded Values 03:26

• Section 31: Get Dummies 03:43

  • 206: 204. Get Dummies 03:43

• Section 32: Curse Of Dimensionality 01:30

  • 207: 205. Curse Of Dimensionality 01:30

• Section 33: Feature Harsher(The Hashing Trick) 04:59

  • 208: 206. Feature Harsher or The Hashing Trick 04:59

• Section 34: Variable/Feature Scaling 01:21

  • 209: 207. Feature Scaling Introduction 01:21

• Section 35: What Is Normalisation? 04:15

  • 210: 208. What Is Normalisation? 04:15

• Section 36: Standardisation 04:02

  • 211: 209. What is Standardisation 04:02

• Section 37: Standard Scaler 17:09

  • 212: 210. Generating Random Values 03:55
  • 213: 211. Scaling Data Points 03:35
  • 214: 212. Visualising Scaled Data Points 03:54
  • 215: 213. Standard Scaler On A Dataset 04:31
  • 216: 214. Visualise Scaled Dataset 01:14

• Section 38: MinMax Scaler 04:56

  • 217: 215. MinMax Scaler 04:56

• Section 39: Robust Scaler 04:59

  • 218: 216. Robust Scaler 04:59

• Section 40: Normaliser 03:49

  • 219: 217. Normaliser 03:49

• Section 41: Variable/Feature Transformation 04:53

  • 220: 218. What is Feature Transformation 03:53
  • 221: 219. What We Will Learn 01:00

• Section 42: Log Transformation 16:36

  • 222: 220. Introduction 02:14
  • 223: 221. Overview Of Dataset 04:31
  • 224: 222. Feature Distribution 04:13
  • 225: 223. Code Walkthrough 03:30
  • 226: 224. Log Transformation Output 02:08

• Section 43: Power Transformation: Box Cox Transformer 09:34

  • 227: 225. Box Cox: Finding The Optimal Lambda 04:15
  • 228: 226. Transforming Data With Optimal Lambda 03:24
  • 229: 227. Distribution of Transformed Data 01:55

• Section 44: Power Transformation: Yeo Johnson Transformer 04:30

  • 230: 228. Yeo Johnson Transformation 04:30

• Section 45: Polynomial Transformation 01:31

  • 231: 229. Polynomial Transformation 01:31

• Section 46: Automated Feature Engineering Tools 01:00

  • 232: 230. Introduction 01:00

• Section 47: Feature Selector 44:07

  • 233: 231. Introduction To Feature Selector 04:40
  • 234: 232. Auto Missing Value Detection 04:50
  • 235: 233. Auto Detect Highly Correlated Values 03:54
  • 236: 234. Plotting Highly Correlated Heatmap 03:59
  • 237: 235. Zero Importance Features 04:54
  • 238: 236. Identify and Plot Feature Importance 04:36
  • 239: 237. Low Importance Features 04:30
  • 240: 238. Display Low Importance Features 01:35
  • 241: 239. Remove Features 03:25
  • 242: 240. One-Hot Encoding Values 03:02
  • 243: 241. Code Summary 04:42

• Section 48: RFECV 24:31

  • 244: 242. Introduction and Quick Dataset Overview 02:44
  • 245: 243. Creating RFECV Estimator 04:36
  • 246: 244. Optimal Number Of Features 01:36
  • 247: 245. Optimal Features Curve 03:14
  • 248: 246. Explaining The Optimal Features Curve 02:19
  • 249: 247. Identify Which Features Are Important 04:22
  • 250: 248. Feature Importance Values 00:48
  • 251: 249. Plotting And Visualising Feature Importance 04:52

• Section 49: Feature Tools 38:56

  • 252: 250. Introduction 01:02
  • 253: 251. Dataset Overview 04:08
  • 254: 252. Entity Vs Entityset 04:43
  • 255: 253. Creating Entityset In FeatureTools 03:26
  • 256: 254. Creating Entitysets In FeatureTools 2 03:35
  • 257: 255. Entityset Output 02:06
  • 258: 256. Introduction To Relationship 03:20
  • 259: 257. Creating Relationship 04:59
  • 260: 258. Creating New Features Using Feature Primitives 04:13
  • 261: 259. Creating New Features Using Feature Primitives(Output) 2 02:26
  • 262: 260. Deep Feature Synthesis 04:58

• Section 50: AutoFeat 07:57

  • 263: 261. AutoFeat Introduction 03:05
  • 264: 262. AutoFeat Feature Selector 04:52

• Section 51: How To Deal With Imbalance Data 1:19:16

  • 265: 263. Introduction 03:48
  • 266: 264. Problems With Data Imbalance 02:04
  • 267: 265. Overview Of Dataset 04:34
  • 268: 266. Visualising Imbalance Dataset 1 04:44
  • 269: 267. Visualising Imbalance Dataset 2 00:59
  • 270: 268. Dropping Null Values 01:29
  • 271: 269. Dummy Classifier on Imbalance Dataset 05:00
  • 272: 270. Dummy Classifier on Imbalance Dataset 2 03:16
  • 273: 271. Output of Dummy Classifier on Imbalance Dataset 02:03
  • 274: 272. Logistic Regression on Imbalance Dataset 02:21
  • 275: 273. Performance Metrics 03:38
  • 276: 274. Performance Metrics: Confusing Matrix 1 02:40
  • 277: 275. Performance Metrics: Confusing Matrix 2 04:04
  • 278: 276. Performance Metrics: F1 Score and Recall 03:50
  • 279: 277. Changing The Algorithm 03:07
  • 280: 278. Performance Metrics On New Algorithm 02:35
  • 281: 279. Resampling Techniques 00:53
  • 282: 280. OverSampliing or Upsamplig Techniques 02:11
  • 283: 281. OverSampliing/Upsamplig: Code Walkthrough 04:41
  • 284: 282. OverSampling/Upsampling: Code Output 00:47
  • 285: 283. RandomOverSampler Technique 03:51
  • 286: 284. Logistic Regression On Oversampled Balanced Dataset 04:49
  • 287: 285. DownSampling or Undersampling Technique 03:09
  • 288: 286. Logistic Regression On DownSampled Balanced dataset 01:43
  • 289: 287. Generative Synthetic Sampling(SMOTE) 03:16
  • 290: 288. Logistic Regression On SMOTE 03:44

• Section 52: How To Deal With Data Leakage 1:02:11

  • 291: 289. Introduction To Data Leakage 03:41
  • 292: 290. Why Data Leakage? 05:00
  • 293: 291. How To Know If You Have Data Leakage 01:22
  • 294: 292. Causes Of Data Leakage 03:59
  • 295: 293. Dataset Overview 04:16
  • 296: 294. Leaky Preditors 03:35
  • 297: 295. Leaky Preditors (Label Encoder) 02:43
  • 298: 296. Leaky Predictors (Correlation) 02:41
  • 299: 297. Data Preprocessing Activities(Comparing Two Approaches) 03:25
  • 300: 298. Data Preprocessing Activities(Normalisation) 04:08
  • 301: 299. Data Preprocessing Activities: Approach 1 04:55
  • 302: 300. Data Preprocessing Activities/ Approach 1 (MinMax Scaler) 04:52
  • 303: 301. Data Preprocessing Activities: Approach 1 (Model Building) 02:00
  • 304: 302. Data Preprocessing Activities: Approach 1 (Evaluate The Model) 04:42
  • 305: 303. Data Preprocessing Activities: Approach 2 02:24
  • 306: 304. Data Preprocessing Activities: Cross Validation 02:20
  • 307: 305. Data Preprocessing Activities: Approach 1 02:42
  • 308: 306. Cross Validation: Approach 2 (Continuation) 03:26

• Section 53: Hands-On PROJECT 01:00

  • 309: 307. Introduction 01:00

• Section 54: Web Scraping 07:15

  • 310: 308. Web Scraping Introduction Preview 01:31
  • 311: 309. Library: Requests Preview 01:55
  • 312: 310. Library: BeautifulSoup Preview 01:01
  • 313: 311. Library: Selenium 01:12
  • 314: 312. Library: Scrapy 01:36

• Section 55: Wikipedia Web Scraping 30:58

  • 315: 313. Web Scraping On Wikipedia 30:58

• Section 56: Online BookStore Web Scrapping 55:40

  • 316: 314. PART 1: BookStore Web Scrapping 06:05
  • 317: 315. PART 2 BookStore Web Scrapping 22:52
  • 318: 316. PART 3: BookStore Web Scrapping 10:12
  • 319: 317. PART 4: BookStore Web Scrapping 16:31

• Section 57: Web Scraping: Building Amazon Auto Scraper 43:32

  • 320: 318. Part 1: Building Amazon Web Scraper 00:54
  • 321: 319. PART 2 Building Amazon Auto Scraper 14:45
  • 322: 320. Amazon Auto Scraper 27:53

Interested in the field of Data Analytics, Business Analytics, Data Science or Machine Learning?

Do you want to know the best ways to clean data and derive useful insights from it?

Do you want to save time and easily perform Exploratory Data Analysis(EDA)?

Then this course is for you!!

According to Forbes: "60% of the Data Scientist's or Data Analyst's time is spent in cleaning and organising the data..."

In this course, you will not just get to know the industry level strategies but also I will practically demonstrate them for better understanding.

This course has been practically and carefully designed by industry experts to reflect the real-world scenario of working with messy data.

This course will help you learn complex Data Analytic techniques and concepts for easier understanding and data manipulations.

We will walk you through step-by-step on each topic explaining each line of code for your understanding.

This course has been structured in the following form:

• Introduction To Basic Concepts

• Introduction To Data Analysis Tools

• BONUS: Python Crush Course

• How To Properly Deal With Python Data Types

• How To Properly Deal With Date and Time In Python

• How To Properly Deal With Missing Values

• How To Properly Deal With Outliers

• How To Properly Deal With Data Imbalance

• How To Properly Deal With Data Leakage

• How To Properly Deal With Categorical Values

• Beginner To Advanced Data Visualisation

• Different Feature Engineering Techniques including:

  • Feature Encoding

  • Feature Scaling

  • Feature Transformation

  • Feature Normalisation

• Automated Feature EDA Tools

  • pandas-profiling

  • Dora

  • Autoviz

  • Sweetviz

• Automated Feature Engineering

  • RFECV

  • FeatureTools

  • FeatureSelector

  • Autofeat

• Web scraping

  • Wikipedia

  • online bookstore

  • Amazon .com

This course aims to help beginners, as well as an intermediate data analyst, students, business analyst, data science, and machine learning enthusiasts, master the foundations of confidently working with data in the real world.

• This course is from a beginner level to advance level, and therefore anyone interested in learning basic to complex Data Analytics techniques for Data Science and Machine Learning is strongly advised to enrol.
• Anyone preparing for a career in Data Analytics, Data Science, Business Analytics, Business Intelligence, Machine Learning will highly find this course very useful.
• Any student ready to learn how to deal with complex machine learning problems such as imbalance data, data leakage, basic to advanced Feature Engineering etc. is strongly recommended to enrol.
• Anyone who is looking for a career transition to Data Analytics, Data Science, Business Analytics, Business Intelligence, Machine Learning role and wants to understand the concepts very well from scratch is recommended to enrol.

• This course is a beginner to advance level course with a step-by-step walk through.

• If you are a complete beginner, you have all the lessons from introduction to python to dealing with complex data issues and building a web-scraper.

• If you already have the basics in Python, feel free to skip the Python Crush course at the BONUS session.

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