What is included?

  • 5 Python Training Modules -- this includes 70+ training videos ranging from the basics to data visualization to data cleaning. ($297 Value)
  • Ultimate Vault of PDF Guides -- Summaries, cheat sheets, shortcuts, functions by library, etc. ($97 Value)

  • Access to The Q&A Lounge -- where we will be answering your questions and providing our favorite Python hacks ($97 Value)


Total Value: $491

Your Price: $57

(Limited-Time Offer)

Do I have to start on a certain day?

You can complete the course at your own pace and start whenever you'd like! You will have lifetime access to the videos and materials. The course is designed to be one module per week over several weeks. However, if you are the "Netflix binge-watching" type -- feel free to go forth and conquer!

Course curriculum

  1. 1

    • Welcome to the course! 🎉

      FREE PREVIEW

    • Introduction to Python 💡

    • Setting up Python 🔧

    • What is Jupyter?

    • Anaconda Installation Windows Mac and Ubuntu 🔧

      FREE PREVIEW

    • How to implement Python in Jupyter 🔧

    • Managing Directories in Jupyter Notebook 🗃️📁

    • Input/Output

    • Working with different datatypes

    • Variables

    • Arithmetic Operators 🔢

    • Comparison Operators

    • Logical Operators

    • Conditional statements

    • Loops ➰

    • Sequences Lists

    • Sequences Dictionaries

    • Sequences Tuples

    • Functions Built-in Functions

    • Functions User-defined Functions
  2. 2

    • Installing Libraries 🔧

    • Importing Libraries

    • Pandas Library for Data Science 👩🏻‍🔬👨‍🔬

    • NumPy Library for Data Science

    • Pandas vs NumPy 💡

    • Matplotlib Library for Data Science

    • Seaborn Library for Data Science
  3. 3

    • Introduction to NumPy arrays ☀️

    • Creating NumPy arrays

    • Indexing NumPy arrays

    • Array shape

    • Iterating Over NumPy Arrays
  4. 4

    • zeros()

    • ones()

    • full()

    • Adding a scalar to an array ➕

    • Subtracting a scalar from an array ➖

    • Multiplying an array by a scalar ✖️

    • Dividing an array by a scalar ➗

    • Raise to a power

    • Transpose

    • Element-wise addition

    • Element-wise Subtraction

    • Element wise multiplication

    • Element-wise division

    • Matrix multiplication

    • Statistics
  5. 5

    • What is a Python Pandas DataFrame? 💡

    • What is a Python Pandas Series? 💡

    • DataFrame vs Series

    • Creating a DataFrame using lists

    • Creating a DataFrame using a dictionary

    • Loading CSV data into python

      FREE PREVIEW

    • Changing the Index Column

    • Inplace

    • Examining the DataFrame - Head & Tail

    • Statistical summary of the DataFrame

    • Slicing rows using bracket operators

    • Indexing columns using bracket operators

    • Boolean list

    • Filtering Rows

    • Filtering rows using AND OR operators

    • Filtering data using loc()

    • Filtering data using iloc()

    • Adding and deleting rows and columns

    • Sorting Values

    • Exporting and saving pandas DataFrames

    • Concatenating DataFrames

    • Groupby()
  6. 6

    • Introduction to Data Cleaning

    • Quality of Data

    • Examples of Anomalies

    • Median-based Anomaly Detection

    • Mean-based anomaly detection

    • Z-score-based Anomaly Detection

    • Interquartile Range for Anomaly Detection

    • Dealing with missing values

    • Regular Expressions

    • Feature Scaling
  7. 7

    • Introduction

    • Setting Up Matplotlib

    • Plotting Line Plots using Matplotlib

    • Title, Labels & Legend

    • Plotting Histograms

    • Plotting Bar Charts

    • Plotting Pie Charts

    • Plotting Scatter Plots

    • Plotting Log Plots

    • Plotting Polar Plots

    • Handling Dates

    • Creating multiple subplots in one figure
  8. 8

    • Introduction

    • What is Exploratory Data Analysis?

    • Univariate Analysis

    • Univariate Analysis: Continuous Data

    • Univariate Analysis: Categorical Data

    • Bivariate analysis: Continuous & Continuous

    • Bivariate analysis: Categorical & Categorical

    • Bivariate analysis: Continuous & Categorical

    • Detecting Outliers

    • Categorical Variable Transformation
  9. 9

    • Introduction to Time Series

    • Getting stock data using yfinance

    • Converting a Dataset into Time Series

    • Working with Time Series

    • Time Series Data Visualization with Python
  10. 10

    • Built-in Functions

    • Pandas & NumPy

    • Matplotlib & Seaborn

    • Mini Projects
  11. 11

    • Q&A Lounge
  12. 12