Data Science & Machine Learning: Professional Career Programme

Oak Academy

Reed Courses Certificate of Completion

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• Installations 57:06

  • 1: Installing Anaconda Distribution for Windows 10:35
  • 2: Installing Anaconda Distribution for Linux 14:43
  • 3: Installing Anaconda Distribution for MacOs 06:17
  • 4: Reviewing The Jupyter Notebook 12:54
  • 5: Reviewing The Jupyter Lab 11:37
  • 6: Project Files 01:00

• First Step to Coding 31:48

  • 7: Python Introduction 05:32
  • 8: First Step to Coding 07:05
  • 9: Using Quotation Marks in Python Coding 08:20
  • 10: How Should the Coding Form and Style Be (Pep8) 09:51
  • 11: Quiz 01:00

• Basic Operations with Python 59:15

  • 12: Introduction to Basic Data Structures in Python 08:17
  • 13: Performing Assignment to Variables 10:10
  • 14: Performing Complex Assignment to Variables 04:57
  • 15: Type Conversion 09:04
  • 16: Arithmetic Operations in Python 09:53
  • 17: Examining the Print Function in Depth 07:29
  • 18: Escape Sequence Operations 08:25
  • 19: Quiz 01:00

• Boolean Data Type in Python Programming Language 19:13

  • 20: Boolean Logic Expressions 05:03
  • 21: Oreder Of Operations In Boolean Operators 01:13
  • 22: Boolean Operators - Practice with Python 11:57
  • 23: Quiz 01:00

• String Data Type in Python Programming Language 1:30:13

  • 24: Examining Strings Specifically 08:31
  • 25: Accessing Length Information (Len Method) 02:42
  • 26: Search Method In Strings Startswith(), Endswith() 11:25
  • 27: Character Change Method In Strings Replace() 05:06
  • 28: Spelling Substitution Methods in String 05:08
  • 29: Character Clipping Methods in String 06:35
  • 30: Indexing and Slicing Character String 08:03
  • 31: Complex Indexing and Slicing Operations 10:48
  • 32: String Formatting with Arithmetic Operations 06:22
  • 33: String Formatting With % Operator 10:24
  • 34: String Formatting With String.Format Method 08:17
  • 35: String Formatting With f-string Method 05:52
  • 36: Quiz 01:00

• List Data Structure in Python Programming Language 44:40

  • 37: Creation of List 11:07
  • 38: Reaching List Elements – Indexing and Slicing 08:08
  • 39: Adding & Modifying & Deleting Elements of List 07:47
  • 40: Adding and Deleting by Methods 05:31
  • 41: Adding and Deleting by Index 04:59
  • 42: Other List Methods 06:08
  • 43: Quiz 01:00

• Tuple Data Structure in Python Programming Language 16:18

  • 44: Creation of Tuple 09:53
  • 45: Reaching Tuple Elements Indexing And Slicing 04:25
  • 46: Quiz 02:00

• Dictionary Data Structure in Python Programming Language 26:31

  • 47: Creation of Dictionary 06:02
  • 48: Reaching Dictionary Elements 08:01
  • 49: Adding & Changing & Deleting Elements in Dictionary 03:41
  • 50: Dictionary Methods 07:47
  • 51: Quiz 01:00

• Set Data Structure in Python Programming Language 28:51

  • 52: Creation of Set 08:08
  • 53: Adding & Removing Elements Methods in Sets 04:45
  • 54: Difference Operation Methods In Sets 05:18
  • 55: Intersection & Union Methods In Sets 02:33
  • 56: Asking Questions to Sets with Methods 06:07
  • 57: Quiz 02:00

• Conditional Expressions in Python Programming Language 53:31

  • 58: Comparison Operators 06:18
  • 59: Structure of “if” Statements 08:30
  • 60: Structure of “if-else” Statements 04:36
  • 61: Structure of “if-elif-else” Statements 09:22
  • 62: Structure of Nested “if-elif-else” Statements 10:01
  • 63: Coordinated Programming with “IF” and “INPUT” 07:30
  • 64: Ternary Condition 05:14
  • 65: Quiz 02:00

• For Loop in Python Programming Language 42:16

  • 66: For Loop in Python 07:17
  • 67: For Loops in Python(Reinforcing the Topic) 07:07
  • 68: Using Conditional Expressions and For Loop Together 10:01
  • 69: Continue Command 03:23
  • 70: Break Command 04:39
  • 71: List Comprehension 07:49
  • 72: Quiz 02:00

• While Loop in Python Programming Language 21:58

  • 73: While Loop in Python 05:39
  • 74: While Loops in Python Reinforcing the Topic 14:19
  • 75: Quiz 02:00

• Functions in Python Programming Language 43:46

  • 76: Getting know to the Functions 08:33
  • 77: How to Write the Function 06:59
  • 78: Return Expression in Functions 05:12
  • 79: Writing Functions with Multiple Argument 05:02
  • 80: Writing Docstring in Functions 05:02
  • 81: Using Functions and Conditional Expressions Together 10:58
  • 82: Quiz 02:00

• Arguments And Parameters in Python Programming Language 26:11

  • 83: Arguments and Parameters 11:16
  • 84: High Level Operations with Arguments 12:55
  • 85: Quiz 02:00

• Most Used Functions in Python Programming Language 45:21

  • 86: all(), any() Functions 05:52
  • 87: map() Function 04:58
  • 88: filter() Function 04:43
  • 89: zip() Function 04:22
  • 90: enumerate() Function 03:31
  • 91: max(), min() Functions 02:09
  • 92: sum() Function 01:45
  • 93: round() Function 04:15
  • 94: Lambda Function 11:46
  • 95: Quiz 02:00

• Class Structure in Python Programming Language 50:33

  • 96: Local and Global Variables 04:08
  • 97: Features of Classes 08:10
  • 98: nstantiation of Classes 06:58
  • 99: Attribute of Instantiation 09:32
  • 100: Write Function in the Class 07:10
  • 101: Inheritance Structure 11:35
  • 102: Quiz 03:00

• NumPy Library Introduction 24:29

  • 103: Introduction to NumPy Library 06:25
  • 104: The Power of NumPy 16:04
  • 105: Quiz 02:00

• Creating NumPy Array in Python 43:49

  • 106: Creating NumPy Array with The Array() Function 08:17
  • 107: Creating NumPy Array with Zeros() Function 05:06
  • 108: Creating NumPy Array with Ones() Function 03:06
  • 109: Creating NumPy Array with Full() Function 02:50
  • 110: Creating NumPy Array with Arange() Function 02:56
  • 111: Creating NumPy Array with Eye() Function 03:08
  • 112: Creating NumPy Array with Linspace() Function 01:32
  • 113: Creating NumPy Array with Random() Function 08:29
  • 114: Properties of NumPy Array 05:25
  • 115: Quiz 03:00

• Functions in the NumPy Library 44:33

  • 116: Reshaping a NumPy Array: Reshape() Function 05:57
  • 117: Identifying the Largest Element of a Numpy Array 03:45
  • 118: Detecting Least Element of Numpy Array: Min(), Ar 02:35
  • 119: Concatenating Numpy Arrays: Concatenate() Function 09:40
  • 120: Splitting One-Dimensional Numpy Arrays: The Split 05:46
  • 121: Splitting Two-Dimensional Numpy Arrays: Split(), 09:33
  • 122: Sorting Numpy Arrays: Sort() Function 04:17
  • 123: Quiz 03:00

• Indexing, Slicing, and Assigning NumPy Arrays 1:08:03

  • 124: Indexing Numpy Arrays 07:39
  • 125: Slicing One-Dimensional Numpy Arrays 06:08
  • 126: Slicing Two-Dimensional Numpy Arrays 09:30
  • 127: Assigning Value to One-Dimensional Arrays 05:03
  • 128: Assigning Value to Two-Dimensional Array 09:58
  • 129: Fancy Indexing of One-Dimensional Arrrays 06:10
  • 130: Fancy Indexing of Two-Dimensional Arrrays 12:32
  • 131: Combining Fancy Index with Normal Indexing 03:26
  • 132: Combining Fancy Index with Normal Slicing 04:37
  • 133: Quiz 03:00

• Operations in Numpy Library 37:57

  • 134: Operations with Comparison Operators 06:10
  • 135: Arithmetic Operations in Numpy 15:11
  • 136: Statistical Operations in Numpy 06:36
  • 137: Solving Second-Degree Equations with NumPy 07:00
  • 138: Quiz 03:00

• Pandas Library 06:39

  • 139: Introduction to Pandas Library 06:39

• Series Structures in the Pandas Library 51:44

  • 140: Creating a Pandas Series with a List 10:22
  • 141: Creating a Pandas Series with a Dictionary 04:54
  • 142: Creating Pandas Series with NumPy Array 03:11
  • 143: Object Types in Series 05:15
  • 144: Examining the Primary Features of the Pandas Seri 04:55
  • 145: Most Applied Methods on Pandas Series 12:54
  • 146: Indexing and Slicing Pandas Series 07:13
  • 147: Quiz 03:00

• DataFrame Structures in Pandas Library 22:10

  • 148: Creating Pandas DataFrame with List 05:33
  • 149: Creating Pandas DataFrame with NumPy Array 03:03
  • 150: Creating Pandas DataFrame with Dictionary 04:02
  • 151: Examining the Properties of Pandas DataFrames 06:32
  • 152: Quiz 03:00

• Element Selection Operations in DataFrame Structures 49:02

  • 153: Element Selection Operations in Pandas DataFrames: Lesson 1 07:42
  • 154: Element Selection Operations in Pandas DataFrames: Lesson 2 06:05
  • 155: Top Level Element Selection in Pandas DataFrames:Lesson 1 08:43
  • 156: Top Level Element Selection in Pandas DataFrames:Lesson 2 07:33
  • 157: Top Level Element Selection in Pandas DataFrames:Lesson 3 05:35
  • 158: Element Selection with Conditional Operations in 11:24
  • 159: Quiz 02:00

• Structural Operations on Pandas DataFrame 55:55

  • 160: Adding Columns to Pandas Data Frames 08:17
  • 161: Removing Rows and Columns from Pandas Data frames 04:00
  • 162: Null Values in Pandas Dataframes 14:42
  • 163: Dropping Null Values: Dropna() Function 07:15
  • 164: Filling Null Values: Fillna() Function 11:37
  • 165: Setting Index in Pandas DataFrames 07:04
  • 166: Quiz 03:00

• Multi-Indexed DataFrame Structures 23:34

  • 167: Multi-Index and Index Hierarchy in Pandas DataFrames 09:17
  • 168: Element Selection in Multi-Indexed DataFrames 05:13
  • 169: Selecting Elements Using the xs() Function in Multi-Indexed DataFrames 07:04
  • 170: Quiz 02:00

• Structural Concatenation Operations in Pandas DataFrame 1:01:06

  • 171: Concatenating Pandas Dataframes: Concat Function 12:41
  • 172: Merge Pandas Dataframes: Merge() Function: Lesson 1 10:45
  • 173: Merge Pandas Dataframes: Merge() Function: Lesson 2 05:38
  • 174: Merge Pandas Dataframes: Merge() Function: Lesson 3 09:45
  • 175: Merge Pandas Dataframes: Merge() Function: Lesson 4 07:35
  • 176: Joining Pandas Dataframes: Join() Function 11:42
  • 177: Quiz 03:00

• Functions That Can Be Applied on a DataFrame 1:43:44

  • 178: Loading a Dataset from the Seaborn Library 06:41
  • 179: Examining the Data Set 1 07:29
  • 180: Aggregation Functions in Pandas DataFrames 21:45
  • 181: Examining the Data Set 2 10:38
  • 182: Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes 18:15
  • 183: Advanced Aggregation Functions: Aggregate() Function 07:40
  • 184: Advanced Aggregation Functions: Filter() Function 06:31
  • 185: Advanced Aggregation Functions: Transform() Function 11:38
  • 186: Advanced Aggregation Functions: Apply() Function 10:07
  • 187: Quiz 03:00

• Pivot Tables in Pandas Library 21:50

  • 188: Examining the Data Set 3 08:14
  • 189: Pivot Tables in Pandas Library 10:36
  • 190: Quiz 03:00

• File Operations in Pandas Library 37:05

  • 191: Accessing and Making Files Available 05:12
  • 192: Data Entry with Csv and Txt Files 13:35
  • 193: Data Entry with Excel Files 04:25
  • 194: Outputting as an CSV Extension 07:09
  • 195: Outputting as an Excel File 03:44
  • 196: Quiz 03:00

• Data Visualization - Matplotlib - Seaborn - Geoplotlib 3:59:11

  • 197: What is Data Visualization 07:53
  • 198: What is Matplotlib 03:02
  • 199: Using Pyplot 07:30
  • 200: Using Pyplot - Pylab - Matplotlib 07:19
  • 201: Figure Subplot Multiplot Axes 17:28
  • 202: Figure Customization 14:48
  • 203: Plot Customization 06:45
  • 204: Grid, Spines, Ticks 07:06
  • 205: Basic Plots in Matplotlib I 26:47
  • 206: Basic Plots in Matplotlib II 13:28
  • 207: What is Seaborn 04:09
  • 208: Controlling Figure Aesthetics 10:21
  • 209: Example 09:08
  • 210: Color Palette 13:00
  • 211: Basic Plots in Seabornlib 19:57
  • 212: Multi-Plots in Seaborn 09:19
  • 213: Regression Plots and Squarify 14:22
  • 214: What is Geoplotlib 08:43
  • 215: Example 08:17
  • 216: Example - II 16:09
  • 217: Example - III 09:40
  • 218: Quiz 04:00

• First Contact with Machine Learning 10:24

  • 219: What is Machine Learning? 03:53
  • 220: Machine Learning Terminology 02:31
  • 221: Quiz 04:00

• Evalution Metrics in Machine Learning 53:31

  • 222: Classification vs Regression in Machine Learning 03:24
  • 223: Machine Learning Model Performance Evaluation: Classification Error Metrics 18:02
  • 224: Evaluating Performance: Regression Error Metrics in Python 09:52
  • 225: Machine Learning With Python 18:13
  • 226: Quiz 04:00

• Supervised Learning with Machine Learning 08:07

  • 227: What is Supervised Learning in Machine Learning? 05:07
  • 228: Quiz 03:00

• Linear Regression Algorithm in Machine Learning 1:24:33

  • 229: Linear Regression Algorithm Theory in Machine Learning 07:48
  • 230: Linear Regression Algorithm With Python Part 1 14:58
  • 231: Linear Regression Algorithm with Python Part 2 23:39
  • 232: Linear Regression Algorithm with Python Part 3 15:46
  • 233: Linear Regression Algorithm With Python Lesson 4 19:22
  • 234: Quiz 03:00

• Bias Variance Trade-Off in Machine Learning 13:47

  • 235: What is Bias Variance Trade-Off? 10:47
  • 236: Quiz 03:00

• Bias Variance Trade-Off in Machine Learning 1:03:04

  • 237: What is Logistic Regression Algorithm in Machine Learning? 04:39
  • 238: Logistic Regression Algorithm with Python Part 1 13:45
  • 239: Logistic Regression Algorithm with Python Part 2 18:17
  • 240: Logistic Regression Algorithm with Python Part 3 07:54
  • 241: Logistic Regression Algorithm with Python Part 4 09:18
  • 242: Logistic Regression Algorithm with Python Part 5 08:11
  • 243: Quiz 01:00

• K-fold Cross-Validation in Machine Learning 12:51

  • 244: K-Fold Cross-Validation Theory 04:18
  • 245: K-Fold Cross-Validation with Python 06:33
  • 246: Quiz 02:00

• K Nearest Neighbors Algorithm in Machine Learning 35:50

  • 247: K Nearest Neighbors Algorithm Theory 06:33
  • 248: K Nearest Neighbors Algorithm with Python Part 1 07:23
  • 249: K Nearest Neighbors Algorithm with Python Part 2 12:07
  • 250: K Nearest Neighbors Algorithm with Python Part 3 07:47
  • 251: Quiz 02:00

• Hyperparameter Optimization 18:21

  • 252: Hyperparameter Optimization Theory 06:24
  • 253: Hyperparameter Optimization with Python 09:57
  • 254: Quiz 02:00

• Decision Tree Algorithm in Machine Learning 45:34

  • 255: Decision Tree Algorithm Theory 09:18
  • 256: Decision Tree Algorithm with Python Part 1 07:06
  • 257: Decision Tree Algorithm with Python Part 2 08:36
  • 258: Decision Tree Algorithm with Python Part 3 03:27
  • 259: Decision Tree Algorithm with Python Part 4 09:09
  • 260: Decision Tree Algorithm with Python Part 5 05:58
  • 261: Quiz 02:00

• Random Forest Algorithm in Machine Learning 21:56

  • 262: Random Forest Algorithm Theory 05:47
  • 263: Random Forest Algorithm with Pyhon Part 1 05:54
  • 264: Random Forest Algorithm with Pyhon Part 2 08:15
  • 265: Quiz 02:00

• Support Vector Machine Algorithm in Machine Learning 39:20

  • 266: Support Vector Machine Algorithm Theory 05:09
  • 267: Support Vector Machine Algorithm with Python Part 1 05:31
  • 268: Support Vector Machine Algorithm with Python Part 2 08:15
  • 269: Support Vector Machine Algorithm with Python Part 3 10:43
  • 270: Support Vector Machine Algorithm with Python Part 4 08:42
  • 271: Quiz 01:00

• Unsupervised Learning 04:31

  • 272: Unsupervised Learning Overview 03:31
  • 273: Quiz 01:00

• K Means Clustering Algorithm in Machine Learning 34:06

  • 274: K Means Clustering Algorithm Theory 04:10
  • 275: K Means Clustering Algorithm with Python Part 1 07:06
  • 276: K Means Clustering Algorithm with Python Part 2 06:50
  • 277: K Means Clustering Algorithm with Python Part 3 06:51
  • 278: K Means Clustering Algorithm with Python Part 4 07:09
  • 279: Quiz 02:00

• Hierarchical Clustering Algorithm in Machine Learning 20:25

  • 280: Hierarchical Clustering Algorithm Theory 04:40
  • 281: Hierarchical Clustering Algorithm with Python Part 1 07:51
  • 282: Hierarchical Clustering Algorithm with Python Part 2 05:54
  • 283: Quiz 02:00

• Principal Component Analysis (PCA) in Machine Learning 25:31

  • 284: Principal Component Analysis (PCA) Theory 08:48
  • 285: Principal Component Analysis (PCA) with Python Part 1 05:17
  • 286: Principal Component Analysis (PCA) with Python Part 2 01:56
  • 287: Principal Component Analysis (PCA) with Python Part 3 07:30
  • 288: Quiz 02:00

• Recommender System Algorithm in Machine Learning 11:22

  • 289: What is the Recommender System? Part 1 04:58
  • 290: What is the Recommender System? Part 2 04:24
  • 291: Quiz 02:00

• First Contact with Kaggle 40:49

  • 292: What is Kaggle? 15:57
  • 293: Registering on Kaggle and Member Login Procedures 06:07
  • 294: Getting to Know the Kaggle Homepage 17:45
  • 295: Quiz 01:00

• Competition Section on Kaggle 45:10

  • 296: Competitions on Kaggle: Lesson 1 22:45
  • 297: Competitions on Kaggle: Lesson 2 21:25
  • 298: Quiz 01:00

• Dataset Section on Kaggle 17:00

  • 299: Datasets on Kaggle 16:00
  • 300: Quiz 01:00

• Code Section on Kaggle 48:24

  • 301: Examining the Code Section in Kaggle Lesson 1 12:40
  • 302: Examining the Code Section in Kaggle Lesson 2 14:49
  • 303: Examining the Code Section in Kaggle Lesson 3 19:55
  • 304: Quiz 01:00

• What is Discussion on Kaggle? 05:40

  • 305: What is Discussion on Kaggle? 05:40

• Other Most Used Options on Kaggle 28:10

  • 306: Courses in Kaggle 06:48
  • 307: Ranking Among Users on Kaggle 15:33
  • 308: Blog and Documentation Sections 04:49
  • 309: Quiz 01:00

• Details on Kaggle 32:55

  • 310: User Page Review on Kaggle 10:38
  • 311: Treasure in The Kaggle 07:42
  • 312: Publishing Notebooks on Kaggle 05:11
  • 313: What Should Be Done to Achieve Success in Kaggle? 08:24
  • 314: Quiz 01:00

• Introduction to Machine Learning with Real Hearth Attack Prediction Project 57:35

  • 315: First Step to the Hearth Attack Prediction Project 15:16
  • 316: Notebook Design to be Used in the Project 14:16
  • 317: Examining the Project Topic 10:01
  • 318: Recognizing Variables In Dataset 17:02
  • 319: Quiz 01:00

• First Organization 23:50

  • 320: Required Python Libraries 08:40
  • 321: Loading the Statistics Dataset in Data Science 01:48
  • 322: Initial analysis on the Dataset 12:22
  • 323: Quiz 01:00

• Preparation For Exploratory Data Analysis (EDA) in Data Science 42:40

  • 324: Examining Missing Values 10:05
  • 325: Examining Unique Values 09:11
  • 326: Separating variables (Numeric or Categorical) 03:12
  • 327: Examining Statistics of Variables 18:12
  • 328: Quiz 02:00

• Exploratory Data Analysis (EDA) - Uni-variate Analysis 1:00:10

  • 329: Numeric Variables (Analysis with Distplot): Lesson 1 14:29
  • 330: Numeric Variables (Analysis with Distplot): Lesson 2 03:57
  • 331: Categoric Variables (Analysis with Pie Chart): Lesson 1 13:55
  • 332: Categoric Variables (Analysis with Pie Chart): Lesson 2 15:40
  • 333: Examining the Missing Data According to the Analysis Result 10:09
  • 334: Quiz 02:00

• Exploratory Data Analysis (EDA) - Bi-variate Analysis 1:57:29

  • 335: Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 1 08:33
  • 336: Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 2 07:31
  • 337: Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 1 03:58
  • 338: Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 2 12:57
  • 339: Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1 04:56
  • 340: Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2 06:55
  • 341: Feature Scaling with the Robust Scaler Method 09:00
  • 342: Creating a New DataFrame with the Melt() Function 11:22
  • 343: Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 1 06:26
  • 344: Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 2 11:10
  • 345: Numerical - Categorical Variables (Analysis with Box Plot): Lesson 1 07:19
  • 346: Numerical - Categorical Variables (Analysis with Box Plot): Lesson 2 07:45
  • 347: Relationships between variables (Analysis with Heatmap): Lesson 1 06:05
  • 348: Relationships between variables (Analysis with Heatmap): Lesson 2 12:32
  • 349: Quiz 01:00

• Preparation for Modelling in Machine Learning 1:15:16

  • 350: Dropping Columns with Low Correlation) 03:47
  • 351: Visualizing Outliers 08:31
  • 352: Dealing With Outliers – Trtbps Variable Lesson 1 09:58
  • 353: Dealing With Outliers – Trtbps Variable Lesson 2 10:53
  • 354: Dealing With Outliers – Thalach Variable 08:22
  • 355: Dealing With Outliers – Oldpeak Variable 07:50
  • 356: Determining Distributions of Numeric Variables 05:02
  • 357: Transformation Operations on Unsymmetrical Data 04:56
  • 358: Applying One Hot Encoding Method to Categorical Variables 05:24
  • 359: Feature Scaling with the RobustScaler Method for Machine Learning Algorithms 02:29
  • 360: Separating Data into Test and Training Set 07:04
  • 361: Quiz 01:00

• Modelling for Machine Learning 1:02:03

  • 362: Logistic Regression 06:54
  • 363: Cross Validation 05:41
  • 364: Roc Curve and Area Under Curve (AUC) 08:17
  • 365: Hyperparameter Optimization (with GridSearchCV) 12:54
  • 366: Decision Tree Algorithm 05:05
  • 367: Support Vector Machine Algorithm 05:02
  • 368: Random Forest Algorithm 06:17
  • 369: Hyperparameter Optimization(with GridSearchCV) 10:53
  • 370: Quiz 01:00

• Conclusion 03:32

  • 371: Project Conclusion and Sharing 03:32

• Exploring Global Conflict: Dataset and Variables 25:42

  • 372: Project Introduction 07:22
  • 373: Loading the Dataset 11:52
  • 374: Understanding the Variables in the Dataset 05:28
  • 375: Quiz 01:00

• Preparing the Conflict Dataset for Analysis 38:56

  • 376: Exploring the Characteristics of Our Conflict Dataset 08:23
  • 377: Missing and Unique Value Analysis 07:35
  • 378: Renaming Variables 02:58
  • 379: Ensuring Data Consistency- Lesson 1 06:35
  • 380: Ensuring Data Consistency- Lesson 2 07:25
  • 381: Ensuring Data Consistency- Lesson 3 05:00
  • 382: Quiz 01:00

• Level 1 – Foundations of Exploratory Data Analysis 59:56

  • 383: EDA - Distribution Analysis of Conflict - Lesson 1 08:38
  • 384: EDA - Distribution Analysis of Conflict - Lesson 2 06:23
  • 385: EDA - Distribution Analysis of Conflict - Lesson 3 05:56
  • 386: Exploring Annual Trends Lesson 1 09:05
  • 387: Exploring Annual Trends Lesson 2 09:35
  • 388: Analyzing Conflict Data by Region Lesson 1 10:19
  • 389: Analyzing Conflict Data by Region Lesson 2 09:00
  • 390: Quiz 01:00

• Level 2 – Intermediate EDA: Revealing Relationships 54:52

  • 391: Visualizing Conflict Data Correlations Lesson 1 05:03
  • 392: Visualizing Conflict Data Correlations Lesson 2 06:58
  • 393: Bar Charts for Regional Death Counts - Lesson 1 09:40
  • 394: Bar Charts for Regional Death Counts - Lesson 2 13:31
  • 395: Identifying Top Events with Highest Fatalities 07:45
  • 396: Stacked Bar Charts for Conflict-related Deaths Over the Years – Lesson 1 05:59
  • 397: Stacked Bar Charts for Conflict-related Deaths Over the Years – Lesson 2 04:56
  • 398: Quiz 01:00

• Level 3 – Advanced EDA: Clustering and Temporal Patterns 1:08:38

  • 399: Exploring Trends with Bubble Charts Lesson 1 05:35
  • 400: Exploring Trends with Bubble Charts Lesson 2 04:07
  • 401: Filtering Regional Death Trends- A Temporal Analysis Lesson 1 08:48
  • 402: Filtering Regional Death Trends- A Temporal Analysis Lesson 2 08:05
  • 403: Mastering Elbow Method and Silhouette Scores Lesson 1 14:02
  • 404: Mastering Elbow Method and Silhouette Scores Lesson 2 05:54
  • 405: Mastering Elbow Method and Silhouette Scores Lesson 3 03:52
  • 406: Regional Conflict Clustering Lesson 1 09:59
  • 407: Regional Conflict Clustering Lesson 2 07:16
  • 408: Quiz 01:00

• Level 4 – Mastering EDA: From Interactivity to Statistical Precision 1:08:17

  • 409: Advanced Data Visualization with Plotly Animating Conflict Trends Over Time - 1 07:03
  • 410: Advanced Data Visualization with Plotly Animating Conflict Trends Over Time - 2 15:32
  • 411: Advanced Data Visualization with Plotly Animating Conflict Trends Over Time - 3 02:53
  • 412: Heatmap Visualization of Regional Conflict Metrics Lesson 1 06:20
  • 413: Heatmap Visualization of Regional Conflict Metrics Lesson 2 05:35
  • 414: In-Depth Statistical Analysis of Numerical Data in Conflict Studies 13:37
  • 415: Testing for Normality in Conflict Data Using the Shapiro-Wilk Test 07:41
  • 416: Advanced Outlier Detection Using Z-Scores in Conflict Data Analysis 08:36
  • 417: Quiz 01:00

• Advanced Pattern Discovery and Dimensionality Reduction 58:33

  • 418: Discovering Data Patterns via Clustering After Outlier Removal – Lesson 1 06:09
  • 419: Discovering Data Patterns via Clustering After Outlier Removal – Lesson 2 11:30
  • 420: Discovering Data Patterns via Clustering After Outlier Removal – Lesson 3 12:01
  • 421: Visualizing High-Dimensional Data with PCA – Lesson 1 10:37
  • 422: Visualizing High-Dimensional Data with PCA – Lesson 2 05:25
  • 423: Visualizing High-Dimensional Data with PCA – Lesson 3 11:51
  • 424: Quiz 01:00

• Temporal and Spatial Conflict Risk Analysis 1:01:40

  • 425: Conflict Risk Mapping Over Time Lesson 1 08:02
  • 426: Conflict Risk Mapping Over Time Lesson 2 07:53
  • 427: Conflict Risk Mapping Over Time Lesson 3 05:55
  • 428: Interpreting Year-on-Year Growth in Violence Types Lesson 1 07:31
  • 429: Interpreting Year-on-Year Growth in Violence Types Lesson 2 06:49
  • 430: Interpreting Year-on-Year Growth in Violence Types Lesson 3 06:28
  • 431: Cumulative Conflict Trends- Tracking the Deadly Rise Over Time Lesson 1 09:28
  • 432: Cumulative Conflict Trends- Tracking the Deadly Rise Over Time Lesson 2 03:43
  • 433: Cumulative Conflict Trends- Tracking the Deadly Rise Over Time Lesson 3 03:51
  • 434: Quiz 02:00

• Preparing Data & Building and Evaluating ML Models 46:50

  • 435: Feature Selection & Target Engineering for Machine Learning 17:02
  • 436: Importing Machine Learning Libraries 07:53
  • 437: Building Machine Learning Models 04:44
  • 438: Machine Learning Modeling 10:44
  • 439: Evaluating the Model Results Table 04:27
  • 440: Quiz 02:00

• CatBoost Modeling and Interpretation 36:30

  • 441: Let’s Develop the CatBoost Model – Hyperparameter Optimization Lesson 1 07:56
  • 442: Let’s Develop the CatBoost Model – Hyperparameter Optimization Lesson 2 08:49
  • 443: CatBoost – Feature Importance Lesson 1 06:50
  • 444: CatBoost – Feature Importance Lesson 2 03:32
  • 445: CatBoost – Feature Importance Lesson 3 07:23
  • 446: Quiz 02:00

• Gradient Boosting Modeling and Interpretation 24:19

  • 447: Let’s Develop the Gradient Boosting Model – Hyperparameter Optimization 11:47
  • 448: Gradient Boosting Algorithm – Feature Importance Lesson 1 06:27
  • 449: Gradient Boosting Algorithm – Feature Importance Lesson 2 04:05
  • 450: Quiz 02:00

• Final Model Selection and Deployment 26:25

  • 451: Let's Choose the Best Model 05:31
  • 452: Model Prediction Control 06:33
  • 453: Model Deployment 12:21
  • 454: Quiz 02:00

• Introduction & Setup 27:14

  • 455: Course Overview & Learning Path 02:46
  • 456: Exam Guide Breakdown 03:39
  • 457: What is Databricks & Why Data Engineering 03:54
  • 458: Creating Your Free Databricks Environment 03:49
  • 459: Navigating the Databricks User Interface 11:06
  • 460: Quiz 02:00

• Databricks Building Blocks 1:23:19

  • 461: How Databricks Fits Together Lesson 1 04:48
  • 462: How Databricks Fits Together Lesson 2 08:53
  • 463: File and Notebook Management in Databricks 06:27
  • 464: Databricks Compute Options Lesson 1 07:01
  • 465: Databricks Compute Options Lesson 2 10:10
  • 466: Databricks Cluster Settings 07:18
  • 467: Databricks – Your Digital Notebook and Laboratory Lesson 1 04:05
  • 468: Databricks – Your Digital Notebook and Laboratory Lesson 2 07:40
  • 469: Databricks – Your Digital Notebook and Laboratory Lesson 3 05:58
  • 470: Essential Notebook Commands in Databricks 11:16
  • 471: Smart Shortcuts in Databricks 08:43
  • 472: Quiz 01:00

• Lakehouse Architecture Fundamentals 26:35

  • 473: What is Lakehouse? – The Unified Data Platform 07:02
  • 474: Understanding the Medallion Layers (Bronze, Silver, Gold) 09:43
  • 475: ACID Transactions & Transaction Logs 07:50
  • 476: Quiz 02:00

• Data Governance & Unity Catalog 1:23:09

  • 477: From DBFS to Unity Catalog: The Evolution of Data Governance 10:08
  • 478: Understanding Unity Catalog Layers 08:29
  • 479: Managed vs External Tables in Unity Catalog 13:26
  • 480: Creating a Unity Catalog 11:31
  • 481: Creating Managed Tables – Lesson 1 12:59
  • 482: Creating Managed Tables – Lesson 2 06:38
  • 483: Creating Volumes – Lesson 1 11:47
  • 484: Creating Volumes – Lesson 2 06:11
  • 485: Quiz 02:00

• Getting Started with ETL Apache Spark 17:51

  • 486: Getting Started with ETL and Apache Spark 07:32
  • 487: Understanding the Data Model 08:19
  • 488: Quiz 02:00

• Data Engineering with Apache Spark – Bronze Layer 1:44:06

  • 489: Your First ETL Steps (Extract) with Apache Spark – Lesson 1 18:40
  • 490: Your First ETL Steps (Extract) with Apache Spark – Lesson 2 09:26
  • 491: Your First ETL Steps (Extract) with Apache Spark – Lesson 3 06:02
  • 492: Exploring All Bronze DataFrames with PySpark 10:47
  • 493: External Tables: Using External Data Without Bringing It into Databricks 09:11
  • 494: Detecting Duplicate Keys in the Bronze Layer 08:36
  • 495: Missing Value Profiling in the Bronze Layer 25:06
  • 496: Final Checks before Moving to Silver Layer – Lesson 1 08:25
  • 497: Final Checks before Moving to Silver Layer – Lesson 2 05:53
  • 498: Quiz 02:00

• Data Engineering with Apache Spark – Silver Layer 4:58:23

  • 499: Cleaning and Normalizing Customers Table – Lesson 1 05:42
  • 500: Cleaning and Normalizing Customers Table – Lesson 2 17:34
  • 501: Olist Sellers: Transforming Bronze to Silver – Lesson 1 11:52
  • 502: Olist Sellers: Transforming Bronze to Silver – Lesson 2 14:17
  • 503: Cleaning and Enriching the Products Table — Lesson 1 08:46
  • 504: Cleaning and Enriching the Products Table — Lesson 2 06:49
  • 505: Cleaning and Enriching the Products Table — Lesson 3 10:43
  • 506: Cleaning and Enriching the Products Table — Lesson 4 13:07
  • 507: Cleaning and Enriching the Products Table — Lesson 5 11:00
  • 508: Time, Quality, and Missing Data Management in Orders Table – Lesson 1 10:05
  • 509: Time, Quality, and Missing Data Management in Orders Table – Lesson 2 09:12
  • 510: Time, Quality, and Missing Data Management in Orders Table – Lesson 3 10:48
  • 511: Time, Quality, and Missing Data Management in Orders Table – Lesson 4 09:36
  • 512: Time, Quality, and Missing Data Management in Orders Table – Lesson 5 14:18
  • 513: Order_Items Data Transformation and Quality Checks – Lesson 1 14:38
  • 514: Order_Items Data Transformation and Quality Checks – Lesson 2 13:05
  • 515: Order_Items Data Transformation and Quality Checks – Lesson 3 08:37
  • 516: Payments Data Validation and Transformation – Lesson 1 07:36
  • 517: Payments Data Validation and Transformation – Lesson 2 07:48
  • 518: Payments Data Validation and Transformation – Lesson 3 08:57
  • 519: Payments Data Validation and Transformation – Lesson 4 04:00
  • 520: Building the Silver Version of order_reviews – Lesson 1 08:48
  • 521: Building the Silver Version of order_reviews – Lesson 2 10:37
  • 522: Building the Silver Version of order_reviews – Lesson 3 14:10
  • 523: Geolocation Data Cleaning and Deduplication – Lesson 1 08:42
  • 524: Geolocation Data Cleaning and Deduplication – Lesson 2 09:18
  • 525: Geolocation Data Cleaning and Deduplication – Lesson 3 11:40
  • 526: Geolocation Data Cleaning and Deduplication – Lesson 4 08:54
  • 527: Clean Reference Tables in the Silver Layer 05:44
  • 528: Quiz 02:00

• Data Engineering with Apache Spark – Gold Layer 2:39:38

  • 529: Customer Distribution Analysis – Lesson 1 12:15
  • 530: Customer Distribution Analysis – Lesson 2 20:26
  • 531: Seller Metrics and Pareto Visualization – Lesson 1 06:29
  • 532: Seller Metrics and Pareto Visualization – Lesson 2 18:21
  • 533: Analyzing Product Categories by Weight, Volume and Density – Lesson 1 09:31
  • 534: Analyzing Product Categories by Weight, Volume and Density – Lesson 2 08:51
  • 535: Analyzing Product Categories by Weight, Volume and Density – Lesson 3 10:12
  • 536: Gold Layer – Each Table Tells Its Own Story 13:25
  • 537: Unified Order Gold Analytics – Lesson 1 08:24
  • 538: Unified Order Gold Analytics – Lesson 2 06:30
  • 539: Unified Order Gold Analytics – Lesson 3 11:58
  • 540: Unified Order Gold Analytics – Lesson 4 14:38
  • 541: Unified Order Gold Analytics – Lesson 5 08:39
  • 542: Designing Analytical Joins in the Gold Layer 07:59
  • 543: Quiz 02:00

• Identify basic concepts of data schemas and dimensions 1:01:59

  • 544: Data Schemas 03:54
  • 545: Relational Databases Schemas 07:36
  • 546: Non-Relational Databases 06:26
  • 547: Comparing Databases 05:07
  • 548: Data Processing(OLTP & OLAP) 06:34
  • 549: Data Warehouse 06:45
  • 550: Data Mart 03:38
  • 551: Schema Concepts(Snowflake & Star) 07:40
  • 552: Data Lake 07:44
  • 553: Slowly Changing Dimensions 05:35
  • 554: Quiz 01:00

• Compare and contrast different data types 19:07

  • 555: Quantitative Data 03:57
  • 556: Qualitative Data 06:17
  • 557: Data Types 05:22
  • 558: Can We Convert Data Types 02:31
  • 559: Quiz 01:00

• Compare and contrast common data structures and file formats 26:30

  • 560: Data Structures 07:00
  • 561: Data File Formats-Text/Flat File 04:05
  • 562: Review Data Languages: Lesson 1 07:48
  • 563: Review Data Languages: Lesson 2 06:37
  • 564: Quiz 01:00

• Explain data acquisition concepts 56:31

  • 565: Explain Data Acquisition Concepts 05:06
  • 566: Extracting Data 04:29
  • 567: Transforming Data 05:40
  • 568: Loading Data(Full Load & Delta Load) 06:13
  • 569: Application programming interfaces (APIs) 05:49
  • 570: Web Scraping 05:38
  • 571: Machine Data 03:09
  • 572: Public Data 05:39
  • 573: Survey Data 07:00
  • 574: Sampling & Observation 06:48
  • 575: Quiz 01:00

• Cleansing and Profiling Datasets 49:41

  • 576: Cleansing and Profiling Datasets 04:15
  • 577: Data Profiling Steps 04:24
  • 578: Tools that Simplify The Data Profiling Process 03:31
  • 579: Redundant Data 03:06
  • 580: Dublicate Data 03:45
  • 581: Missing Values 09:08
  • 582: Invalid Data 04:56
  • 583: Non-parametric Data 03:25
  • 584: Data Outliers 06:51
  • 585: Specification Mismatch 04:20
  • 586: Quiz 02:00

• Data Manipulation Techniques. 37:19

  • 587: Data Manipulation Techniques 03:24
  • 588: Recording Data 10:24
  • 589: Derived Variables 04:01
  • 590: Data Merge 02:21
  • 591: Data Blending 02:14
  • 592: Concatenation 03:03
  • 593: Data Append 01:39
  • 594: Value Imputation 05:08
  • 595: Reduction/Aggregation 04:05
  • 596: Quiz 01:00

• Common Techniques for Data Manipulation 53:39

  • 597: Filtering 02:49
  • 598: Data Sorting 02:31
  • 599: Date Functions 02:44
  • 600: Logical Functions 10:05
  • 601: Aggregate Functions 06:53
  • 602: System Functions 01:05
  • 603: Query Optimization 04:00
  • 604: Parameterization 04:37
  • 605: Indexing 04:47
  • 606: Temporary Table in The Query Set 04:03
  • 607: Subset of Records 04:59
  • 608: Execution Plan 04:06
  • 609: Quiz 01:00

• Summarize types of analysis and key analysis techniques. 35:56

  • 610: Data Analysis 07:50
  • 611: Exploratory Data Analysis(EDA) 04:29
  • 612: Sentiment Analysis 03:34
  • 613: Perfomance Analysis 04:10
  • 614: Diagnostic Analysis 04:35
  • 615: Gap Analysis 03:31
  • 616: Trend Analysis 03:29
  • 617: Link Anlysis 03:18
  • 618: Quiz 01:00

• Descriptive Statistical Methods 37:33

  • 619: Descriptive Statistical Methods 04:36
  • 620: Measures of Central Tendency(Mean, Median, Mode) 05:39
  • 621: Why is Central Tendency Important? 06:19
  • 622: Measures of Dispersion 10:59
  • 623: Frequencies/Percentages 03:39
  • 624: Percent change / Percent Difference 02:26
  • 625: Confidence intervals 02:55
  • 626: Quiz 01:00

• Inferential Statistical Methods 35:22

  • 627: Inferential Statistical Methods 04:03
  • 628: T-tests 07:48
  • 629: Z-Score 03:17
  • 630: P-Values 03:57
  • 631: Chi-squared 02:50
  • 632: Hypothesis Testing 05:30
  • 633: Linear Regression 03:17
  • 634: Correlation 03:40
  • 635: Quiz 01:00

• Data Analytics Tools 1:26:56

  • 636: Data Analytics Tools 01:26
  • 637: Data Transformation Tools(Excel-Tableau) 08:36
  • 638: Data Transformation Tools(Power BI ve Rapid Miner) 05:06
  • 639: Data Visualization Tools(Tableau, Power BI) 09:11
  • 640: Data Visualization Tools(Qlik, AWS QuickSight, ArcGIS) 08:33
  • 641: Statistical Tools 03:47
  • 642: Statistical Tools(SAS, IBMSPSS) 06:16
  • 643: Statistical Tools(Stata, Minitab) 04:34
  • 644: Reporting Tools 03:12
  • 645: Reporting Tools(SSRS, Crystal Reports ve Power BI) 08:40
  • 646: Platform Tools 02:52
  • 647: Platform Tools(Business Objects, MicroStrategy) 07:16
  • 648: Platform Tools(Oracles Apex, Dataroma) 04:50
  • 649: Platform Tools(IBM Cognos, Rapid Miner) 05:19
  • 650: Platform Tools(Oracle Analytics, Domo ve Microsoft Power platform) 06:18
  • 651: Quiz 01:00

• Translate business requirements to form a report 46:23

  • 652: Creating Reports 04:21
  • 653: Data Content 03:09
  • 654: Data Filtering 12:33
  • 655: Data Sorting 10:56
  • 656: Views 02:45
  • 657: Data Range 02:44
  • 658: Frequency 03:16
  • 659: Audience for Report 05:39
  • 660: Quiz 01:00

• Use Appropriate Design Components For Reports And Dashboards. 25:44

  • 661: Creating Dashboards 02:27
  • 662: Report Cover Page 03:10
  • 663: Design Elements: Lesson 1 03:49
  • 664: Design Elements: Lesson 2 07:24
  • 665: Documentation Elements 07:54
  • 666: Quiz 01:00

• Use Appropriate Methods For Dashboard Development 46:27

  • 667: Dashboard Considerations 02:23
  • 668: Data sources and attributes 03:14
  • 669: Continuous live Data Feed vs. Static Data 03:40
  • 670: Development Process 02:35
  • 671: Mockup wireframe 04:54
  • 672: Approval Granted 03:01
  • 673: Develop Dashboard 02:32
  • 674: Deploy to Production 03:04
  • 675: Subscription and Scheduled Delivery 06:06
  • 676: Interactive 06:26
  • 677: Static & Web interface 03:02
  • 678: Dashboard Optimization & Access Permissions 03:30
  • 679: Quiz 02:00

• Apply the appropriate type of visualization. 30:02

  • 680: Visualization Type 02:59
  • 681: Line Chart 02:30
  • 682: Pie Chart – Bubble Chart 03:26
  • 683: Scatter Plot-Bar Chart 03:41
  • 684: Histogram-Waterfall 03:28
  • 685: Heat map - Geographic map 03:50
  • 686: Tree map - Stacked chart 04:14
  • 687: Infographic - Word cloud 04:54
  • 688: Quiz 01:00

• Compare and contrast types of reports 22:51

  • 689: Compare and Contrast Types of Reports 03:58
  • 690: Static vs. Dynamic Reports 03:33
  • 691: Ad-hoc/one-time report 03:30
  • 692: Self-service on demand 03:23
  • 693: Recurring Reports 03:24
  • 694: Tactical Research Report 03:03
  • 695: Quiz 02:00

• Data Governance, Quality, and Controls 10:55

  • 696: Data Governance, Quality, and Controls 02:54
  • 697: Data Lifecycle 03:34
  • 698: Data Roles 03:27
  • 699: Quiz 01:00

• Summarize important data governance concepts. 58:51

  • 700: Access Requirements 06:19
  • 701: Security Requirements 07:16
  • 702: Storage Environment Requirements 04:41
  • 703: Use Requirements 04:50
  • 704: Data Process 03:04
  • 705: Data Retention 02:10
  • 706: Entity Relationship Requirements 05:55
  • 707: Data Classification 11:38
  • 708: Jurisdiction Requirements 06:08
  • 709: Data Breach Reporting 05:50
  • 710: Quiz 01:00

• Apply data quality control concepts. 1:05:11

  • 711: Data Quality Control Concepts 04:38
  • 712: Circumstances to check for quality: Lesson 1 07:26
  • 713: Circumstances to check for quality: Lesson 2 04:07
  • 714: Circumstances to check for quality: Lesson 3 06:20
  • 715: Automated Validation 07:03
  • 716: Data Quality Dimensions 06:50
  • 717: Data quality Rule and Metrics 09:59
  • 718: Cross validation & Sample Spot Check 06:16
  • 719: Reasonable expectations & Data Profiling 05:53
  • 720: Data audits & Peer Review 05:39
  • 721: Quiz 01:00

• Explain Master Data Management (MDM) concepts. 26:32

  • 722: Explain Master Data Management (MDM) Concepts. 03:46
  • 723: Processes 11:36
  • 724: Circumstances for MDM 09:10
  • 725: Quiz 02:00

The Data Science & Machine Learning Professional Career Programme delivers an in-depth and systematic education in modern data science practices. It is designed to take learners from foundational concepts to advanced, job-ready skills through a carefully structured curriculum that reflects real industry expectations.

The programme begins by establishing a strong foundation in Python programming and data analysis. Learners develop the ability to work confidently with real-world datasets, including handling missing data, cleaning inconsistent values, transforming data, and performing exploratory data analysis. Statistical thinking is introduced early to ensure learners can reason about data, patterns, variability, and uncertainty in a professional and analytical manner.

As the programme progresses, learners move into the core of machine learning. They study and apply supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. Rather than focusing on abstract formulas alone, the programme emphasizes applied understanding — learners build models, evaluate their performance, and refine them based on real-world constraints.

A key strength of this programme is its focus on end-to-end machine learning workflows. Learners gain experience designing full pipelines that include data preparation, feature engineering, model training, validation, performance evaluation, and result interpretation. They learn how to select appropriate algorithms for different problem types and how to avoid common pitfalls such as overfitting, data leakage, and misleading evaluation metrics.

Throughout the programme, learners work extensively with real datasets and applied projects inspired by real business and technical scenarios. These projects are designed to simulate the tasks data scientists encounter in professional roles, such as predictive modeling, pattern discovery, and insight generation. This approach ensures learners develop practical problem-solving skills rather than relying on simplified academic examples.

In addition to technical modeling skills, the programme emphasizes the ability to communicate insights effectively. Learners practice explaining data-driven findings, model results, and analytical decisions in a clear and professional manner — a critical skill for working with stakeholders, teams, and decision-makers.

The programme is structured to progressively increase complexity, allowing learners to build confidence while developing depth. Each phase builds on the previous one, reinforcing knowledge through repetition, application, and increasingly challenging tasks. By the end of the programme, learners are capable of independently approaching data science problems, designing solutions, and delivering results at a professional level.

This is not simply a training course — it is a career preparation programme. Graduates complete the programme with a strong portfolio of data science and machine learning projects, a clear understanding of industry-standard workflows, and the confidence to pursue junior to mid-level data science roles.

This programme is designed for individuals who are serious about building a professional career in data science and machine learning.

• Aspiring data scientists who want a clear, structured path into the field

• Career changers aiming to transition into data-driven or AI-related roles

• Software developers or engineers who want to specialize in data science and machine learning

• Analysts seeking to move beyond reporting into predictive modeling and advanced analytics

• STEM graduates who want practical, industry-aligned data science training

• Professionals who want to work with real datasets and real machine learning workflows

• Learners who are committed to developing professional-level technical skills

This programme is not designed for casual learners or those looking for a quick overview of data science concepts.

• Basic computer literacy and comfort working with technology

• Logical and analytical thinking skills

• Willingness to work with data, code, and problem-solving tasks

• Commitment to hands-on practice and project-based learning

• No prior data science or machine learning experience required

• Prior exposure to programming, mathematics, or statistics is helpful but not mandatory

Graduates of this programme are prepared to pursue roles such as:

• Data Scientist

• Junior to Mid-Level Machine Learning Engineer

• Data Analyst with Machine Learning specialization

• Applied Machine Learning Practitioner

• AI / Data Science Associate

• Predictive Analytics Specialist

• Data Science Consultant (Junior Level)

Currently there are no Q&As for this course. Be the first to ask a question.