This course offers students an opportunity to learn fundamentals of computation required to understand and analyze real world data. The course helps students to work with modern data structures, apply data cleaning and data wrangling operations. The course covers conceptual and practical applications of probability and distribution, cluster analysis, text analysis and time series analysis.
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Il y a 4 modules dans ce cours
In this module, we will focus on Python programming fundamentals. The aim is to help understand Python's basic syntax, data types, and operators, enabling the creation of simple programs. Additionally, we will cover the use of if statements, loops, and proper indentation to control program flow, fostering a foundational understanding of essential control structures in Python programming.
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5 vidéos7 lectures1 devoir1 devoir de programmation1 sujet de discussion
In this module, we will dive into the diverse landscape of Python data structures, including lists, dictionaries, sets, tuples, and arrays. By exploring real-world use cases, you will uncover the unique strengths and weaknesses of each data structure. You will gain insights into recognizing and understanding the characteristics of these structures, empowering you to make informed choices when tackling programming challenges. Through hands-on practice, you will develop the skills to select and apply the most suitable data structure to efficiently solve a wide range of problems, enhancing your proficiency in Python programming.
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2 vidéos3 lectures1 quiz1 devoir de programmation
In this module we will introduce DataFrames, a pivotal tool in data manipulation and analysis. You will grasp the fundamental concepts of DataFrames, learning how to create, manipulate, and access data efficiently. You will gain essential skills for basic data exploration–including summarizing data, indexing, and slicing, enabling them to extract meaningful insights. Furthermore, this module equips learners with the expertise to clean and preprocess data, covering handling missing values, filtering data, merging/joining datasets, and transforming data for analysis readiness. By the end of this module, you will harness DataFrames for advanced data analysis, mastering group-wise operations, aggregation, and statistical analysis.
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3 vidéos4 lectures1 devoir1 devoir de programmation
This module will equip you with a comprehensive toolkit for proficient data exploration and analysis. It covers the essential techniques and tools for effectively summarizing data sets, encompassing statistical summaries, data visualization, and data cleaning methods. You will learn how to identify and assess missing data, outliers, and anomalies, vital tasks during the initial exploratory phase of data analysis. Furthermore, you will develop the ability to uncover patterns, relationships, and trends within the data using various visualizations, including scatter plots, histograms, and correlation matrices, enabling them to extract valuable insights and make informed decisions from their data.
Inclus
2 vidéos2 lectures1 devoir1 devoir de programmation
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Recommandé si vous êtes intéressé(e) par Data Analysis
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Google
University of Colorado Boulder
University of Illinois Urbana-Champaign
Johns Hopkins University
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Ce site cours fait partie du (des) programme(s) diplômant(s) suivant(s) proposé(s) par Northeastern University . Si vous êtes admis et que vous vous inscrivez, les cours que vous avez suivis peuvent compter pour l'apprentissage de votre diplôme et vos progrès peuvent être transférés avec vous.¹
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