Module 2: Python for Data Work

Module 2: Python for Data Work#

Duration: 12 hours
Goal: Build confidence working with real-world datasets — loading, cleaning, transforming, analysing, and visualising data using Python.

In Module 1, you learned how Python works.
In Module 2, Python starts to feel useful.

Real data is rarely neat. It arrives incomplete, inconsistent, and spread across files, spreadsheets, databases, and APIs. This module teaches you how to handle that reality calmly and systematically.

Section 2.1 — What Is Pandas and Why We Use It#

Pandas is the most widely used Python library for data analysis. It provides powerful, high-level abstractions for working with tabular data.

import pandas as pd

Section 2.2 — Loading Data from Files#

df = pd.read_csv("sales.csv")
df_excel = pd.read_excel("sales.xlsx")

Section 2.3 — First Look at a Dataset#

df.head()
df.info()
df.describe()

Section 2.4 — DataFrames and Series#

df["revenue"]
df[["revenue"]]

Section 2.5 — Filtering Data#

df[df["revenue"] > 10000]

Section 2.6 — Handling Missing Data#

df.isna().sum()
df.fillna(0)

Section 2.7 — Transforming Data#

df["profit"] = df["revenue"] - df["cost"]

Section 2.8 — Grouping and Aggregation#

df.groupby("region")["revenue"].sum()

Section 2.9 — NumPy for Numerical Work#

import numpy as np
np.mean(df["revenue"])

Section 2.10 — Statistics with SciPy#

from scipy import stats
stats.pearsonr(df["revenue"], df["cost"])

Section 2.11 — Visualisation with Matplotlib#

import matplotlib.pyplot as plt
plt.plot(df["date"], df["revenue"])
plt.show()

Section 2.12 — Visualisation with Seaborn#

import seaborn as sns
sns.histplot(df["revenue"])

Section 2.13 — Interactive Charts with Plotly#

import plotly.express as px
px.bar(df, x="region", y="revenue").show()

Section 2.14 — Automated Data Profiling#

from ydata_profiling import ProfileReport
ProfileReport(df)

Section 2.15 — Data from APIs and Databases#

import requests
import sqlite3

Section 2.16 — Acquiring Real Financial Data#

A critical skill for any data professional is knowing where to find authoritative data sources and how to download and load them.

Key Data Sources for Financial Analysis#

Market Data:

Economic Data:

Loading FRED Data:

# FRED CSVs use "." for missing values
df = pd.read_csv('fred_data.csv', parse_dates=['DATE'], na_values=['.'])
df = df.rename(columns={'DATE': 'Date', 'SERIES_CODE': 'Value'})

Module 2 Assessment#

The assessment requires you to download real financial data from authoritative sources:

  1. DJIA Index from Wall Street Journal (or Yahoo Finance)

  2. USD/GBP Exchange Rate from FRED (series: DEXUSUK)

  3. Federal Funds Rate from FRED (series: FEDFUNDS)

This is a crucial part of the learning — working with real data teaches you to handle real-world data quirks like missing values, different date formats, and varying column naming conventions.

Assessment Notebook: Module2_Assessment.ipynb

Module 2 Additional Resources#

Book:

Video:

End of Module 2#

You can now load, clean, analyse, and visualise real-world data using Python — including sourcing data from professional financial databases.

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