Hands-on core Python and Python libraries
Module description This module discuss the basics of Python programming language and explores how to set up Python environment to work with data. Demonstrate different parts of Python code such as keywords, variables, data types, statements, functions, loops, and libraries, and get familiarized with programming in python. This module offers a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools. The module focuses specifically on Python programming, libraries, and tools needed for data analysis. Essential Python libraries covered in this module are NumPy, pandas & matplotlib. Learning Outcomes L01. Learning python structure and how to write programs in it. L02.Basic concepts of Python, its syntax, functions, and conditional statements. L03. Operate Pandas to sort through and rearrange data, run analyses, and build data frames L04.Understand packages to enable them to write scripts for data manipulation and analysis. Content Covered Basic Python Programming Variable and data types Conditional statements Loops Functions Essential Python libraries for data Pandas Numpy Matplotlib
Mathematics and Statistics for Data Treatment
Module description Mathematics have a significant role in the foundation for programming and this module is designed to help students master the mathematical foundation required for writing programs and algorithms for Business Intelligence. A business intelligence system offers decision-makers information and know edge gleaned from data through mathematical models and algorithms. In certain circumstances, this activity may be reduced to calculations of totals and percentages, graphically represented by basic histograms, although more complex analyses need the development of comprehensive optimization. The module covers three main mathematical theories: Linear Algebra, Statistics, and Probability Theory Learning Outcomes L01. Acquire a fundamental understanding of the analytical techniques and software tools necessary to effectively generate useful information from structured and unstructured datasets of any size chart L02. Gain experience in using the tools and techniques of Business Intelligence to structure and complete projects focused on obtaining actionable insights from complex data. L03. Dive deeply into a chosen area of practice to fully prepare to use the knowledge gained in the program to add significant value in a professional setting L04. Be able to utilize knowledge and skills to continue learning and adapting to new data science technologies Content Covered Linear algebra Probability Statistics Data Cleaning Data Pre-processing Statistical tools CSV Excel.
Data visualization and Database management (SQL)
Module Description This Module covers fundamental topics related to the construction and usage of databases, database systems, and methodologies for data visualization. Organizations store data in two kinds of databases: operational and analytical. Operational database themes include database requirements, entity relationship modeling, relational modeling database constraints, update anomalies, normalization, Structures Query Language (SQL), and data quality. Once data is cleansed and saved, data visualization is utilized to best effectively convey the information contained in the data. The Modulecovers data visualization ideas borrowed from statistics, perception, graphic and informtion design, and data mining. Learners will study visual representation approaches that assist in comprehending complex data and concepts. Learning Outcomes L01.Acquire a fundamental understanding of the analytical techniques and software tools necessary to effectively generate useful information from structured and unstructured datasets of any size charts L02.Gain experience in using the tools and techniques of data science to structure and complete projects focused on obtaining actionable insights from complex data L03.Dive deeply into a chosen area of practice to fully prepare to use the knowledge gained in the program to add significant value in a professional setting L04.Be able to utilize knowledge and skills to continue learning and adapting to new data science technologies Content Covered Data visualization introduction Configuring Data Environment Types of charts Introduction to Database concepts Database Environment PostgreSQL Setup Joins and Sub Queries PostgreSQL Connectivity Relational Model Entity Relationship Model ORM Overview Basic SQL Tables DB creation Data modeling Constraints and Data Manipulation SQL CRUD operations Django’s Database CRUD Operations Exploratory Visualization
Business Intelligence with Data Visualization Tools
Module Description Business intelligence projects are helping various firms utilize and analyze the massive quantity of information accessible today. This Module aims to present a data visualization project tutorial for Information Systems (IS) education. The applied BI lesson was aimed at assisting students in understanding how to develop and analyze a heat map using SQL and Server Data Tools (SSDT). Learners comprehend how to make judgments based on significant volumes of data by presenting it in visual shape. This Module introduces Learners to the decision-making capacity generated from data visualization. Learn to shape and change data before the data analysis with Tableau Query Editor. Filter the information in reports by location and decide how these filters interconnect and interact with other images in the report. Learning Outcomes L01. Describe the ideas and elements of Business Intelligence (BI) and critically examine the usage of BI for assisting decision-making in a business L02. Learn to shape and transform your data before the data analysis using Tableau Query Editor. L03- Filter the information in your reports by location and control how these filters interconnect and interact with other visuals in your report. L04. Learn how to design a dashboard using a real dataset, several types of data visualization, PowerBI plots/charts, including Tableau's Data, Model, and Report views. Content Covered What Is Business Intelligence Applications and use cases of Business intelligence BI in Decision Making PowerBI Tableau Plots Charts Data, Model and Report Views Making Report Views Data Visualization Tableau Query Editor Dashboard design principles Dashboard interactivity Connected “drill-down” dashboards Advanced Tableau Large datasets Fiscal Year Calculations Parameters
Introduction to Data mining
Module Description Business intelligence encompasses tools and strategies for data collection, analysis, and visualization to aid executive decision-making in any business. Data mining covers statistical and machine-learning approaches to develop decision-making models from raw data.In this Module, we desire to discuss and classify data mining activities concerning inquiry goals and analysis approaches. Learners will also examine the relevant qualities of the input data. Finally, we shall discuss the data mining process and its articulation in several stages.Data mining methods in this module include decision trees, regression, artificial neural networks, cluster analysis, and many more. Text mining, web mining, and big data are also addressed in an accessible method. An introduction to data modeling is offered for individuals untrained in this field. Learning Outcomes L01. Discuss the fundamental data mining concepts such as classification, clustering, regression, and unsupervised learning. L02. Introduction to data mining algorithms L03. Understanding the data mining process and techniques L04. Engaging in meaningful discussions about pattern evaluation metrics and investigating techniques for mining various patterns, including sequential and sub-graph patterns. Content Covered Data understanding Decision trees Regression analysis Cluster Analysis Introduction to Data mining Artificial Neural Networks Association Rule Mining Data Mining in a Python-based environment What is a data warehouse How to find patterns? Affinity Analysis Product Recommendation Text mining Web mining Data Preparation Data Modeling Identifying Patterns Data warehousing
Machine Learning Algorithms
Module Description This module offers an introduction to both the theoretical and practical elements of the design and implementation of algorithms that allow computers to "learn" from examples (i.e., Machine Learning). The new paradigm will be established by providing machines with examples from which they can learn the relevant rules to accomplish a task, rather than programming machines by defining a set of instructions that specify precisely how they should perform a task. Learners receive an in-depth introduction to Supervised and Unsupervised Machine Learning topics. The Module will cover essential Machine Learning methods for classification, regression, clustering, dimensionality reduction, and data modeling along with the basics of HTML/CSS, and Git version control system (VCS) to Build and Deploy a model for WebView. Learning Outcomes L01. Learn about training data, and how to use a set of data to discover potentially predictive relationships. L02. Master machine learning techniques, including supervised and unsupervised learning and hands-on modeling, rounding out your artificial intelligence education. L03.Learn popular machine learning algorithms, Feature Selection, and the Mathematical intuition behind them. L04.Learn the basics of HTML/CSS, and Git version control system (VCS) to Build and Deploy a model for WebView Content Covered Introduction to machine learning Machine Learning Algorithms Feature Selection Git Version control system Supervised Learning Unsupervised learning ML Deployment.
