Course curriculum

    1. 👋 Introduction

    2. 🚀 Stages

    3. 🎯 Expectations

    4. 🔗 Biweekly Sessions & Support Link

    1. 🗺️ Book your One on one

    1. Course Outline

    2. Python Overview

    3. What is Google Colaboratory?

    4. Working in Colab: Simple Math

    5. Primitive Data Types

    6. Declaring Variables

    7. Variables and Primitive Data Types (Practice)

    8. Functional Programming and OOP

    9. Lists and Tuples

    10. Lists and Tuples (Practice)

    11. Dictionaries

    12. Dictionaries (Practice)

    13. For Loops

    14. For Loops Assignment (Core)

    15. Conditionals

    16. Conditionals Exercise (Practice)

    17. Intro to Custom Functions

    18. Debugging

    19. Debugging Exercise (Practice)

    20. Custom Functions

    21. Measures of Central Tendency

    22. Writing a Function to Calculate the Mean

    23. Intro to Python Libraries

    24. Functions for Measures of Central Tendency

    25. Central Tendency Functions (Core)

    26. Using Markdown Effectively

    27. Project 1 - Part 1 (Core)

    28. Interview Questions (Optional)

    29. Sets (Optional)

    30. While Loops (Optional)

    1. Intro to Pandas DataFrames

    2. Working with DataFrames

    3. Saving Files to Google Drive

    4. Loading Data with Pandas

    5. Loading Data (Practice):

    6. Saving a dataframe as a .csv

    7. Slicing

    8. Slicing (Practice)

    9. Filtering

    10. Filtering (Practice)

    11. Pandas Groupby

    12. Groupby (Core)

    13. 🌟 STAGE 2: Application Workshop

    14. 📝CV

    15. 🛠️CV Resources

    16. 🚀CV Do’s & Don’ts

    17. 📝Finalize your CV

    18. ✅ CV Checklist

    19. 🎓Education Section "CV"

    20. 📝Tailor Your CV

    21. 📝 [Submission] CV

    22. CRISP-DM Workflow

    23. Project Overview

    24. Phase 2) Data Understanding

    25. Initial Inspection

    26. Duplicates

    27. Missing Values

    28. Handling Null Values

    29. Consistency

    30. Combine or Separate Features

    31. Data Cleaning (Core)

    32. Project 1 - Part 2 (Core)

    33. Additional Pandas Resources

    34. Pandas Interview Questions (Optional)

    1. Phase 2.4: Explore Each Feature

    2. Why Visualize Your Data?

    3. Creating Matplotlib Plots

    4. Univariate Visualizations

    5. Recreate Interest vs Principal Graph (Practice)

    6. Histograms

    7. Boxplots

    8. Histograms & Boxplots Exercise (Practice)

    9. Univariate Categorical Plots

    10. Univariate Plots (core)

    11. Overview - Multivariate Visualizations

    12. Bar Plots

    13. Correlation and Heat Maps

    14. Regression Plots

    15. Multivariate Categorical Plots

    16. Multivariate Plots Practice:

    17. Data Visualization (Core)

    18. Customizing Plots

    19. Making a README

    20. Project 1 - Part 3 (Core)

    21. Matplotlib Styles

    22. Saving Plots to Files

    23. Data Visualization Interview Questions

    1. 1.0 Cover Letter

    2. 2.0 LinkedIn

    3. 2.1 Building your LinkedIn Profile

    4. 2.2 Professional profile photo

    5. 3.0 Coveto

    6. 4.0 GitHub

    7. 🌟 [Submission] Stage 3

    8. 🌐 Networking Skills

About this course

  • Free
  • 133 lessons
  • 0 hours of video content

Discover your potential, starting today