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## Course Description

## Improve Your Python Skills

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib’s functions and get acquainted with data structures such as the dictionary and pandas DataFrame. This four-hour intermediate course will help you to build on your existing Python skills and explore new Python applications and functions that expand your repertoire and help you work more efficiently.## Learn to Use Python Dictionaries and pandas

Dictionaries offer an alternative to Python lists, while the pandas dataframe is the most popular way of working with tabular data. In the second chapter of this course, you’ll find out how you can create and manipulate datasets, and how to access them using these structures. Hands-on practice throughout the course will build your confidence in each area.## Explore Python Boolean Logic and Python Loops

In the second half of this course, you’ll look at logic, control flow, filtering and loops. These functions work to control decision-making in Python programs and help you to perform more operations with your data, including repeated statements. You’ll finish the course by applying all of your new skills by using hacker statistics to calculate your chances of winning a bet.Once you’ve completed all of the chapters, you’ll be ready to apply your new skills in your job, new career, or personal project, and be prepared to move onto more advanced Python learning.

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### Matplotlib

**Free**Data visualization is a key skill for aspiring data scientists. Matplotlib makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

Basic plots with Matplotlib50 xpLine plot (1)100 xpLine Plot (2): Interpretation50 xpLine plot (3)100 xpScatter Plot (1)100 xpScatter plot (2)100 xpHistogram50 xpBuild a histogram (1)100 xpBuild a histogram (2): bins100 xpBuild a histogram (3): compare100 xpChoose the right plot (1)50 xpChoose the right plot (2)50 xpCustomization50 xpLabels100 xpTicks100 xpSizes100 xpColors100 xpAdditional Customizations100 xpInterpretation50 xp - 2
### Dictionaries & Pandas

Learn about the dictionary, an alternative to the Python list, and the pandas DataFrame, the de facto standard to work with tabular data in Python. You will get hands-on practice with creating and manipulating datasets, and you’ll learn how to access the information you need from these data structures.

Dictionaries, Part 150 xpMotivation for dictionaries100 xpCreate dictionary100 xpAccess dictionary100 xpDictionaries, Part 250 xpDictionary Manipulation (1)100 xpDictionary Manipulation (2)100 xpDictionariception100 xpPandas, Part 150 xpDictionary to DataFrame (1)100 xpDictionary to DataFrame (2)100 xpCSV to DataFrame (1)100 xpCSV to DataFrame (2)100 xpPandas, Part 250 xpSquare Brackets (1)100 xpSquare Brackets (2)100 xploc and iloc (1)100 xploc and iloc (2)100 xploc and iloc (3)100 xp - 3
### Logic, Control Flow and Filtering

Boolean logic is the foundation of decision-making in Python programs. Learn about different comparison operators, how to combine them with Boolean operators, and how to use the Boolean outcomes in control structures. You'll also learn to filter data in pandas DataFrames using logic.

Comparison Operators50 xpEquality100 xpGreater and less than100 xpCompare arrays100 xpBoolean Operators50 xpand, or, not (1)100 xpand, or, not (2)50 xpBoolean operators with NumPy100 xpif, elif, else50 xpWarmup50 xpif100 xpAdd else100 xpCustomize further: elif100 xpFiltering pandas DataFrames50 xpDriving right (1)100 xpDriving right (2)100 xpCars per capita (1)100 xpCars per capita (2)100 xp - 4
### Loops

There are several techniques you can use to repeatedly execute Python code. While loops are like repeated if statements, the for loop iterates over all kinds of data structures. Learn all about them in this chapter.

while loop50 xpwhile: warming up50 xpBasic while loop100 xpAdd conditionals100 xpfor loop50 xpLoop over a list100 xpIndexes and values (1)100 xpIndexes and values (2)100 xpLoop over list of lists100 xpLoop Data Structures Part 150 xpLoop over dictionary100 xpLoop over NumPy array100 xpLoop Data Structures Part 250 xpLoop over DataFrame (1)100 xpLoop over DataFrame (2)100 xpAdd column (1)100 xpAdd column (2)100 xp - 5
### Case Study: Hacker Statistics

This chapter will allow you to apply all the concepts you've learned in this course. You will use hacker statistics to calculate your chances of winning a bet. Use random number generators, loops, and Matplotlib to gain a competitive edge!

Random Numbers50 xpRandom float100 xpRoll the dice100 xpDetermine your next move100 xpRandom Walk50 xpThe next step100 xpHow low can you go?100 xpVisualize the walk100 xpDistribution50 xpSimulate multiple walks100 xpVisualize all walks100 xpImplement clumsiness100 xpPlot the distribution100 xpCalculate the odds50 xp

Collaborators

Prerequisites

Introduction to PythonHugo Bowne-Anderson

See MoreData Scientist

Hugo is a data scientist, educator, writer and podcaster formerly at DataCamp. His main interests are promoting data & AI literacy, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. If you want to know what he likes to talk about, definitely check out DataFramed, the DataCamp podcast, which he hosted and produced.

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