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NumPy basics: arrays and vectorized Apr 17, 2025 · 3. • Univariate statistical charts • Bivariate statistical charts • Multivariate statistical charts Module 7 - Data Visualization us-ing Matplotlib Data Manipulation Cleaning and analysing data Basic SQL data types and calculations Table normalisation, primary and foreign keys Nested and subqueries Working with numeric, time, and string data types Module 4: Visualising Data (4 Weeks) Data in Power BI Loading and linking datasets in Power Bl Visuals in Power BI Numeric visuals - cards, tables This document provides an introduction to the concepts of data analytics. Data analytics gathers information about customers’ communication preferences, interests, and concerns. • List the components of data analysis and how they fit together. 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Business analysis also aids in the collection of data and the implementation of the data collection processes. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based COURSE LAYOUT Week 1 Descriptive Statistics Introduction to the course Descriptive Statistics Probability Distributions Week 2 Inferential Statistics Inferential Statistics through hypothesis tests Week 3 Regression & ANOVA Regression ANOVA(Analysis of Variance) Week 4 Machine Learning: Introduction and Concepts Differentiating algorithmic and model based frameworks Regression : Ordinary Least Data Visualization. We just released a Data Analytics course on the freeCodeCamp. 2. In this course, students will learn how to make inferences using statistics, or functions of observed data. 1. Understand the hierarchy of units used to calculate data size. It discusses the course outcomes and Bloom's taxonomy levels for the course. 0, 5/6/2020 Page 5 Lesson 1: Tables & Data Management Locating Blanks, continued Home | Department of Computer Science This course provides an overview of data analytics and business intelligence. Define and explain the purpose of databases. Gain proficiency in Python, SQL, Excel, and Power BI for data analysis, visualization, and reporting. Apr 12, 2018 · • Why Python for data analysis? • Development Environments • Hands-on: Basic Data Structures in Python, Looping • Defining a function in Python • Importing a dataset in to a Python data structure, using modules • Python scripts and parameters • Questions, Resources & Links OVERVIEW 2017. Course. The demand for skilled data science practitioners in industry, a May 16, 2015 · PDF | This file contains lecture notes I've presented at a master of informatics (decision support systems). 6. and the B. More specifically, we define Data Analytics as the discovery of models that capture the behavior of data and can be used to extract information, draw conclusions and make decisions. Apr 12, 2018 · • Python’s importable libraries make it an attractive language for data analysis • NumPy • SciPy • Statsmodels • Pandas • Matplotlib • Natural Language Toolkit (NLTK) • Python can import and export common data formats such as CSV files Reference: Python for Data Analytics, Wes McKinney, 2012, O’Reilly Publishing WHY PYTHON Big Data Analytics MET CS 777 SUM2 On-Campus Classes: Tuesdays 6-9:30 pm and Thursdays 6-9:30 pm classroom CAS 208 (from 7/5/22 to 8/11/22) Prof. There is a growing demand for data analysts due to the abundance of data being created each day and organizations' need to derive value from data. • Formulate new questions. Once the analysis of the collected data is complete, business analysis Data Analyst training referent. 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