Data Analysis with Python Course

What you will learn in 8 lessons

1. 1

   NumPy Arrays

   Start here

   Create, index and reshape NumPy arrays and replace loops with vectorized operations, broadcasting and aggregations.

   6 learning goals

   What you will learn

   1. 1.Create arrays and control their data types
   2. 2.Select and filter data in arrays
   3. 3.Reshape, transpose and join arrays
   4. 4.Replace loops with vectorized arithmetic, ufuncs and broadcasting
   5. 5.Summarize arrays with aggregations along axes
   6. 6.Generate random numbers and run simulations
2. 2

   Getting Started with pandas

   Create and inspect pandas Series and DataFrames, select and filter rows and columns, and compute new columns and summary statistics.

   6 learning goals

   What you will learn

   1. 1.Understand Series and DataFrames
   2. 2.Load a CSV file and check what you loaded
   3. 3.Select data by label and by position
   4. 4.Filter rows with conditions
   5. 5.Add and transform columns
   6. 6.Sort, rank and summarize data
3. 3

   Loading and Storing Data in pandas

   Read and write data in CSV, Excel, JSON, Parquet and SQL databases, call web APIs and handle encodings and messy file layouts.

   6 learning goals

   What you will learn

   1. 1.Load delimited text files into pandas
   2. 2.Read text correctly and efficiently
   3. 3.Work with Excel workbooks
   4. 4.Get data from JSON files and web APIs
   5. 5.Store data in the right file format
   6. 6.Read from and write to SQL databases
4. 4

   Wrangling Data with pandas

   Clean, transform, combine and reshape DataFrames with pandas, including missing data, string operations, merges and pivots.

   5 learning goals

   What you will learn

   1. 1.Find and handle missing values and duplicates
   2. 2.Transform values by replacing, mapping and binning
   3. 3.Clean text and categorical data
   4. 4.Combine DataFrames
   5. 5.Reshape data with hierarchical indexes, pivot and melt
5. 5

   Grouping and Aggregation with pandas

   Split data into groups and apply aggregations, transformations and custom functions, and build pivot tables and cross-tabulations.

   5 learning goals

   What you will learn

   1. 1.Split data into groups with groupby
   2. 2.Summarize each group with aggregations
   3. 3.Transform, filter and apply functions group by group
   4. 4.Bin numeric data and analyze groups by bins and quantiles
   5. 5.Summarize data in pivot tables and cross-tabulations
6. 6

   Time Series with pandas

   Parse dates and times, index data by time, resample and compute rolling statistics, and handle time zones and periods.

   6 learning goals

   What you will learn

   1. 1.Turn dates and times into data pandas can calculate with
   2. 2.Index, select and compare data by time
   3. 3.Generate dates and use frequencies, offsets and periods
   4. 4.Change the frequency of a time series with resampling
   5. 5.Smooth and summarize data with rolling and expanding windows
   6. 6.Handle time zones and daylight saving time
7. 7

   Matplotlib and Seaborn

   Create and customize line, bar, scatter, histogram, box and heatmap plots with Matplotlib, pandas and seaborn.

   5 learning goals

   What you will learn

   1. 1.Build plots with Matplotlib figures and axes
   2. 2.Plot data directly from pandas
   3. 3.Make statistical plots with seaborn
   4. 4.Visualize distributions and grids of values
   5. 5.Build multi-panel figures, annotate them and export them
8. 8

   Exploratory Data Analysis in Python

   Run a complete exploratory analysis in a notebook, profiling variables, examining distributions and relationships and reporting findings reproducibly.

   6 learning goals

   What you will learn

   1. 1.Plan an exploratory analysis in a notebook
   2. 2.Profile a dataset and assess its quality
   3. 3.Describe the distribution of each variable
   4. 4.Explore relationships between variables
   5. 5.Compare segments of the data
   6. 6.Report exploratory findings reproducibly