Women in Big Data

APRIL 17, 2024

Hypothesis Testing in Action: We learned how to formulate a null hypothesis (no difference exists) and an alternative hypothesis (a difference exists) and use statistical tests to evaluate their validity. It learns from historical data to make predictions about future events.

Exploratory Data Analysis 52

Exploratory Data Analysis EDA Machine Learning Machine Learning 52

Pickl AI

APRIL 3, 2025

Exploratory Data Analysis (EDA): Using statistical summaries and initial visualisations (yes, visualisation plays a role within analysis!) Hypothesis Testing: Formally testing assumptions or theories about the data using statistical methods to determine if observed patterns are statistically significant or likely due to chance.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Pickl AI

OCTOBER 14, 2024

Different approaches help organisations make sense of raw data, from simply summarising past events to predicting future outcomes. Exploratory Data Analysis (EDA) Exploratory Data Analysis (EDA) is an approach to analyse datasets to uncover patterns, anomalies, or relationships.

Data Analysis 52

Data Analysis Data Analysis EDA Data Mining 52

Pickl AI

NOVEMBER 4, 2024

Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesis testing, confidence intervals). Perform exploratory Data Analysis (EDA) using Pandas and visualise your findings with Matplotlib or Seaborn. These concepts help you analyse and interpret data effectively.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses. Inferential Statistics: A branch of statistics that makes inferences about a population based on a sample, allowing for hypothesis testing and confidence intervals.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52