Python for Data Analytics

Curriculum

1. 01Python and Colab for AnalystsWhy analysts use Python, how a Colab notebook works, and the building blocks you'll use in every analysis: values, variables, types, maths and f-strings.25 min
2. 02Lists, Dictionaries, Loops and FunctionsThe core Python you need before pandas: store many values in lists and dictionaries, repeat work with loops, decide with if, and package logic in functions.30 min
3. 03DataFrames: Load and ExploreLoad CSV files into pandas DataFrames, check their size, columns and types, select columns, and get a first feel for the data with describe, value_counts and nunique.30 min
4. 04Filtering and SortingKeep only the rows you need with boolean conditions, combine conditions with & and |, use isin, between and text matching, and sort or pick the top rows.20 min
5. 05Calculated Columns and DatesAdd calculated columns to a whole table at once, convert text to real dates and pull out years, months and weekdays, and build categories with np.where and pd.cut.30 min
6. 06Cleaning Messy DataClean a real-world messy export in pandas, step by step: spaces and capitals, inconsistent categories, numbers stored as text, mixed date formats, phone numbers, missing values and duplicates.25 min
7. 07groupby and AggregationSummarise data by group with groupby: totals, averages and counts per product, month or department, several measures at once with named aggregation, and each group's share of the total.30 min
8. 08Merging TablesCombine tables on a shared key with merge, choose between inner and left joins, catch row explosions with validate, find records with no match, and stack tables with concat.20 min
9. 09Pivot Tables and TrendsBuild pivot tables and crosstabs in pandas, summarise by month, and measure change properly with growth rates, year-on-year comparisons and rolling averages.20 min
10. 10Charts with pandas and matplotlibTurn results into clear charts: line charts for trends, sorted bar charts for comparisons, readable axes in millions, titles that state the finding, and saving charts for reports.25 min
11. 11From Question to InsightRun a complete analysis in a notebook: turn a vague question into answerable ones, check the data, analyse, test the obvious explanation, and write findings a manager can act on, with honest caveats.30 min
12. 12Final ProjectPlan and start your final project, a customer health review for Kolanut Distribution. Find which customers are growing, which are slipping, and what's really behind the North West's fall.20 min
13. Final assessmentPass it (60% or more) to earn the Python for Data Analytics course completion badge.