Data Science Foundations Academy

E-learning (Self-Paced): 1,200€ + IVA

REF: DSFA Catálogo: Data Science Área: Data Science Foundations

Duração icon


90 H

Próxima Data icon

Próxima Data:

1/2/2024 a 5/4/2024

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Initial missions will make you explore your first datasets using Pandas, the de facto Python library for data manipulation. Overcome common data quality issues by learning how to clean, wrangle and curate multiple data sets in a fast & efficient way.

Quickly characterise your dataset by getting your hands on descriptive statistics and visual inspection through basic stats and plots (eg. box-plot, histogram). Uncover trends, patterns, and relationships (eg. correlations, outliers) in your datasets that may not be readily apparent to business users.

In order to be successful in this field you will need to know the bare bones of Statistics and Probability. Learn how to use probability and statistics to answer questions and test hypotheses based on the data you have.

Overall, you will learn how to extract information from a dataset, answer questions , and help people to make informed decisions avoiding, at the same time, some of the data science pitfalls.

This academy will be held in partnership with Miles in the Sky

  • Área: Data Science

  • Certificação Associada: Data Science Foundational Exam (DSPA)

  • Destinatários: All participants who wanted to start their knowledge in Data Science Foundations.

Quero inscrever-me.

*Curso disponível em Live Training


Mission 1 - Data Cleaning

  • Description: In this mission, you will learn the fundamental skills of loading and cleaning real-world datasets, focusing on an online retail dataset. Data cleaning is a crucial step in data preprocessing for machine learning and analysis.
  • Learning Topics: Data loading, Pandas usage, handling data types, missing value detection and handling, duplicate entry identification, data inconsistency resolution.
  • Learning Goals: By the end of this mission, you will be proficient in cleaning and preparing datasets for further analysis, an essential skill for data scientists.

Mission 2 - Business Data Analysis

  • Description: This mission is about answering business-related questions using the online retail dataset, leveraging Pandas for data manipulation. Analyzing data for business insights is a key application of data science.
  • Learning Topics: Filtering and subsetting data, joining and merging datasets, performing group-by operations, sorting data, setting and resetting indexes.
  • Learning Goals: After this mission, you will have the skills to extract meaningful business insights and make data-driven decisions.

Mission 3 - Descriptive Statistics and Visualization

  • Description: In this mission, you will explore the world of descriptive statistics, focusing on the use of a house prices dataset. You will also learn the importance of data visualization to spot patterns.
  • Learning Topics: Calculating mean, median, mode, quantiles, data visualization, outlier detection, correlation analysis.
  • Learning Goals: Your ability to summarize data, visualize it effectively, and detect outliers will be enhanced, enabling you to make data-informed decisions.

Mission 4 - Probability and Statistics Concepts

  • Description: This mission introduces probability and statistics concepts, including probability basics, probability distributions, and their real-life applications.
  • Learning Topics: Probability basics, probability distributions, probability mass/density functions, cumulative distribution functions, expected value, variance, famous probability distributions.
  • Learning Goals: You'll have a solid understanding of the fundamental concepts in probability and statistics, which are essential for data modeling and analysis.

Mission 5 - Central Limit Theorem and Hypothesis Testing

  • Description: This mission delves into the Central Limit Theorem, confidence intervals, hypothesis testing, and A/B testing, key tools for making inferences and testing hypotheses.
  • Learning Topics: Central Limit Theorem, confidence intervals, hypothesis testing, A/B testing.
  • Learning Goals: You'll be equipped to make inferences about populations, test hypotheses, and conduct A/B tests with statistical confidence.

Mission 6 - Association Analysis and Bayesian Inference

  • Description: In this mission, you'll learn to measure the strength of associations between variables and explore Bayesian Inference, broadening your statistical reasoning skills.
  • Learning Topics: Contingency tables, marginal and conditional probabilities, Bayes' Theorem, Chi-Square Test, Cramer's V measure.
  • Learning Goals: You'll gain expertise in association analysis and Bayesian Inference, expanding your analytical toolkit for data-driven decision-making.

Mission 7 - Independent Data Analysis

  • Description: This mission challenges you to independently analyze a credit card score dataset, avoiding common data science pitfalls. You'll learn to formulate business questions and conduct exploratory data analysis (EDA).
  • Learning Topics: Avoiding data science pitfalls, formulating business questions, conducting EDA.
  • Learning Goals: By the end of this mission, you'll be capable of applying all your skills to analyze new data independently and provide valuable insights, a crucial skill for data scientists.
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