FinTech 10-1 Time Series

What were the objectives of Time Series Day 1?

• Manipulate time series data sets in pandas

• Perform auto-correlation on time series data

• Identify the components of a time series

• Time series analysis in Scikit-learn

• Analyze long term trends with different averages and filters

Which are applications of machine learning?

• Determining loan eligibility

• Fraud and money laundering

• Designing a cryptocurrency

• Customer segmentation and clustering

• Disease diagnosis and prognosis

Which are Python libraries that can be used for machine learning?

• Scikit-Learn

• TensorFlow

• NumPy

• SciPy

• Pandas

Which are types of machine learning?

• Supervised learning

• Unsupervised learning

• Independent learning

• Reinforcement learning

Which are type of machine learning is regression?

• Supervised learning

• Unsupervised learning

• Independent learning

• Reinforcement learning

Which are type of machine learning is clustering?

• Supervised learning

• Unsupervised learning

• Independent learning

• Reinforcement learning

Which steps should you take to prepare a time series dataframe?

• Read in the csv using pandas

• Format the x-axis as dates

• Set the index to the datetime column

• Set parse_dates = True

Analogy - groupby : typical dataframes :: ______ : time series dataframes

• groupby()

• mean()

• resample()

• set_index()

How was Time Series Day 1 for you?

Any suggestions for improvement?