Becoming a Data Analyst Using Python, from scratch

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the course

Lecture 2: Downloading the necessary program

Lecture 3: The Anaconda interface

Lecture 4: Jupyter lab – navigating the user interface and executing code

Lecture 5: Back to the Anaconda interface – viewing libraries

Chapter 2: A First Look at the Pandas Library

Lecture 1: First look at the Pandas library

Lecture 2: First look at the data

Lecture 3: Adjusting the name of the data set

Chapter 3: Looking More Closely at the Data

Lecture 1: Using the info() function

Lecture 2: Counting the null values

Lecture 3: Zooming in to a specific row

Lecture 4: Zooming into a specific column

Lecture 5: Zooming in to a certain row and column

Lecture 6: Removing rows and columns

Chapter 4: Using Conditions to Perform Operations

Lecture 1: One condition – part 1

Lecture 2: One condition – part 2

Lecture 3: Two conditions – the AND operator

Lecture 4: Two conditions – the OR operator

Lecture 5: Less than or equal to and greater than or equal to

Lecture 6: Not equal to

Lecture 7: The isin() method

Lecture 8: The isna() method

Chapter 5: Summary Statistics for Numeric Variables

Lecture 1: Introduction to summary statistics

Lecture 2: Reading the new data set

Lecture 3: Numeric variables – central tendency part 1

Lecture 4: Numeric variables – central tendency part 2

Lecture 5: Numeric variables – central tendency part 3

Lecture 6: Visualizing the central tendency – Boxplots

Lecture 7: Visualizing the central tendency – Histograms

Lecture 8: The describe() function

Chapter 6: Summary Statistics for Non-Numeric Variables

Lecture 1: Unique values and counts

Lecture 2: Unique values and counts – two variables at the same time

Lecture 3: The groupby() function

Lecture 4: The describe() function

Lecture 5: Visualizing statistics for non-numeric variables – pie charts part 1

Lecture 6: Visualizing statistics for non-numeric variables – pie charts part 2

Lecture 7: Visualizing statistics for non-numeric variables – bar graphs

Chapter 7: Summary Statistics for Groups: How Numeric Values Differ Between Groups

Lecture 1: Summary statistics for groups

Lecture 2: Visualizing summary statistics for groups – part 1

Lecture 3: Visualizing summary statistics for groups – part 2

Lecture 4: Visualizing summary statistics for groups – part 3

Lecture 5: The unstack() function

Lecture 6: The crosstab() function

Lecture 7: Example

Lecture 8: Another example

Chapter 8: Relationships Between Numeric Variables

Lecture 1: Correlation

Lecture 2: Visualising the correlation between numeric variables

Lecture 3: Visualising the correlation between numeric variables – using Seaborn

Lecture 4: Scatter plots

Lecture 5: Scatter plots using Seaborn

Lecture 6: More plots from Seaborn – part 1

Lecture 7: More plots from Seaborn – part 2

Lecture 8: Geographic Maps: Another Library

Chapter 9: Example

Lecture 1: Reading the data

Lecture 2: Taking a general look at the data

Lecture 3: Diving further into the data

Lecture 4: Analyzing price in more detail

Lecture 5: Producing scatter plots

Lecture 6: Calculating the correlation

Lecture 7: Plotting the geographic locations of listings

Chapter 10: Data Manipulation

Lecture 1: Introduction

Lecture 2: What do we do with null values?

Lecture 3: What do we do with outliers?

Lecture 4: A column that should be numeric is stored as text

Lecture 5: There are extra spaces in the values of one of the columns

Lecture 6: A column contains a symbol that I would like to remove

Chapter 11: Case Study

Lecture 1: Reading the data set and converting some columns

Lecture 2: Taking a general look at the data

Lecture 3: Analyzing the data – part 1

Lecture 4: Analyzing the data – part 2

Lecture 5: Analyzing the data – part 3

Lecture 6: Conclusion

Chapter 12: Conclusion

Lecture 1: Conclusion