Python and Analytics for Data Science and Machine Learning
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- Curriculum
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This course is meant for beginners and intermediates who wants to expert on Python programming concepts and Data Science libraries for analysis, machine Learning models etc.
They can be students, professionals, Data Scientist, Business Analyst, Data Engineer, Machine Learning Engineer, Project Manager, Leads, business reports etc.
The course have been divided into 6 parts – Chapters, Quizzes, Classroom Hands-on Exercises, Homework Hands-on Exercises, Case Studies and Projects.
Practice and Hands-on concepts through Classroom, Homework Assignments, Case Studies and Projects
This Course is ideal for anyone who is starting their Data Science Journey and building ML models and Analytics in future.
This course covers all the important Python Fundamentals and Data Science Concepts requires to succeed in Academics and Corporate Industry.
Opportunity to Apply Data Science Concepts in 3 Real World Case Studies and 2 Real World Projects.
The 3 Case Studies are on Loan Risk Analysis, Churn Prediction and Customer Segmentation.
The 2 Projects are on Titanic Dataset and NYC Taxi Trip Duration.
The recommended approach for this course – Follow the chapters in their order, Do Yourself all the Hands-on Exercises. Finally, Consistency, discipline and practice is paramount.
This course will not teach you how to build and develop ML models. But, make you expert at python programming language which is needed to build ML models.
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7Introduction to VariablesVideo lesson
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8Variables, Datatypes and ExpressionsVideo lesson
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9Variables and DatatypesQuiz
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10Classroom 1: Hands-on Exercise on Variables and ExpressionsVideo lesson
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11Homework: Hands-on Exercise on Variables and ExpressionsText lesson
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12Introduction to Strings and FunctionsVideo lesson
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13Strings and its FunctionsQuiz
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14Classroom 2: Hands-on Exercise on StringsVideo lesson
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15Homework: Strings and its FunctionsText lesson
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16Introduction to ListVideo lesson
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17List and its FunctionsVideo lesson
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18List and its FunctionsQuiz
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19Introduction to Tuples and DictionaryVideo lesson
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20Tuples and DictionaryQuiz
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21Classroom 3: Hands-on Exercise on List and TuplesVideo lesson
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22Classroom 4: Hands-on Exercise on DictionaryVideo lesson
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23If-Else Conditions and Logical OperatorsVideo lesson
This course will cover
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24Conditions and Logical OperatorsQuiz
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25Classroom 5: Hands-on Exercise on ConditionsVideo lesson
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26Loops (For and While)Video lesson
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27List ComprehensionVideo lesson
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28Loops and List ComprehensionQuiz
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29Classroom 6: Hands-on Exercise on LoopsVideo lesson
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35Introduction to Numpy PackageVideo lesson
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36Introduction to NumpyQuiz
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37Part 1: Numpy FunctionsVideo lesson
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38Part 1: Numpy FunctionsQuiz
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39Part 2: Numpy FunctionsVideo lesson
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40Part 2: Numpy FunctionsQuiz
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41Part 3: Numpy FunctionsVideo lesson
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42Part 3: Numpy FunctionsQuiz
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43Classroom 8: Hands-on Exercise on Numpy PackageVideo lesson
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44Introduction to Pandas SeriesVideo lesson
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45Pandas SeriesQuiz
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46Introduction to Pandas DataFrameVideo lesson
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47Create Pandas DataFrameQuiz
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48Access Pandas DataFrameVideo lesson
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49Access Pandas DataFrameQuiz
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50Part 1: Pandas FunctionsVideo lesson
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51Part 2: Pandas FunctionsVideo lesson
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52Pandas PackageQuiz
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53Part 1: Classroom 9: Hands-on Exercise on Numpy and PandasVideo lesson
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54Part 2: Classroom 9: Hands-on Exercise on Numpy and PandasVideo lesson
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55Homewrok 4: Numpy and Pandas LibrariesText lesson
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56Case Study 2: Churn PredictionVideo lesson
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57Introduction to VisualizationVideo lesson
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58Visualization : Matplotlib and Seaborn PackagesVideo lesson
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59Data VisualizationQuiz
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60Part 1: Classroom 10: Hands-on Exercise on VisualizationVideo lesson
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61Part 2: Classroom 10: Hands-on Exercise on VisualizationVideo lesson
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62Homework 5: Data VisualizationText lesson
This is the 5th Homework Assignment and will cover Data Visualization.
Please download the homework5.pdf assignment which has 9 questions in it.
You will be using the data "homework5_bigmart.xlsx" and "product_data.csv" in order to complete this assignment.
Please find the homework questions and data in the resource section.

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