Topic Name

Subtopic Name

Learning Outcome(Slide2)

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Topic Name-Recall(Slide3)

Hook/Story/Analogy(Slide 4)

Transition from Analogy to Technical Concept(Slide 5)

What is Time Series Data?

Definition :

Time Series = A sequence of data points indexed by time.

Examples:

Stock market prices

Weather measurements

Website traffic

Sales over months

Python Libraries for Time Series

Pandas

Data handling

NumPy

Numerical computation

Statsmodels

Statistical tests & models

Matplotlib / Seaborn

Visualization

Essential libraries for time series analysis in Python

Loading Time Series Data

Reading time-based datasets

Key step:

Convert date column into datetime index

Example concept:

parse_dates

index_col

This allows time-based operations

Fixing Time Frequency

Why frequency matters

Time series must have consistent intervals

Example frequencies:

Using:

asfreq()

Time Series Visualization

Plotting reveals:

Trends

Seasonal patterns

Anomalies

Basic plot:

df.plot()

Resampling Data

Resampling = Changing time granularity

Two types:

Downsampling

Upsampling

Monthly

Yearly

Monthly

Daily

Handling Missing Data

Real datasets often have missing time points

Common methods:

Forward Fill (ffill)

Propagate last valid observation forward

Backward Fill (bfill)

Propagate next valid observation backward

Interpolation

Estimate values between known points

Rolling Statistics

Used to smooth noisy data

Example:

12-month rolling average

Helps reveal long-term trend

Time-based Filtering

We can easily filter time ranges

Examples:

Example concept:

Removing Trends (Differencing)

Many time series contain trend

Formula concept:

Current value - Previous value

Summary

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Which platform is mainly used for professional networking and B2B marketing ?

Which platform is mainly used for professional networking and B2B marketing ?