Business & Management Analytics

Master the essential skills and tools for Business Analytics to excel in data-driven decision-making.

What you'll learn

Understand and apply statistical foundations, including probability, hypothesis testing, and regression analysis.

Master Python programming for data manipulation, analysis, and visualization using key libraries.

Create and validate predictive models with techniques like regression, decision trees, and SVM.

Solve business problems using optimization models and tools like AIMMS, GAMS, and CPLEX.

Implement advanced machine learning techniques including neural networks and NLP.

Requirements

No prior programming or analytics experience needed. Basic understanding of mathematics is helpful but not required. A computer with internet access is necessary.

Description

Welcome to the "Business Analytics Complete Course"! This comprehensive course is designed to equip you with the knowledge and skills required to excel in the field of business analytics. Whether you are a beginner or looking to enhance your existing skills, this course covers a wide range of topics essential for anyone interested in data-driven decision-making.What You'll Learn:Mathematical and Statistical Foundations: Understand the core principles of statistics and mathematics that form the backbone of data analysis.Python Programming: Learn the basics of Python programming and how to use it for data analysis.Principles of Data Analytics: Gain insights into data collection, cleaning, preprocessing, and exploratory data analysis.Predictive Analytics and Modeling: Explore various predictive modeling techniques including linear regression, logistic regression, decision trees, and more.Optimization and Decision Models: Learn about linear programming, integer programming, nonlinear programming, and other optimization techniques.Machine Learning: Get hands-on experience with supervised and unsupervised learning algorithms, model evaluation, and neural networks.Advanced Machine Learning Techniques: Dive into support vector machines, reinforcement learning, natural language processing, and more.Network Analytics: Understand social network analysis, community detection, and graph algorithms.Data Structures and Algorithms: Build a strong foundation in data structures and algorithms crucial for efficient data processing.Database Technologies: Master SQL, NoSQL, and distributed systems for effective data management.Data Wrangling and Visualization: Learn data cleaning, transformation, integration, and visualization techniques using tools like Tableau and Power BI.Multi-Criteria Decision Making: Analyze decision-making processes using techniques like AHP and TOPSIS.Simulation Modeling: Explore discrete-event simulation, system dynamics, agent-based modeling, and Monte Carlo simulation.Stochastic Optimization: Learn about stochastic linear programming, chance-constrained programming, and other stochastic optimization methods.Web and Social Network Analytics: Analyze web and social media data for business insights.Performance Analytics with DEA: Measure efficiency and performance using Data Envelopment Analysis.Soft Computing Techniques: Apply fuzzy logic systems, genetic algorithms, and neural networks in soft computing.Customer Analytics: Manage and analyze customer data for better decision-making.Big Data Technologies: Understand big data frameworks like Hadoop and Spark.Practical Data Science Projects: Implement end-to-end data science projects, from data collection to model deployment.Communication and Data Storytelling: Effectively communicate data insights and build compelling data narratives.Who This Course Is For:Aspiring data analysts and business analystsProfessionals looking to transition into data-centric rolesStudents pursuing degrees in data science, business, or related fieldsAnyone interested in enhancing their data analysis skillsJoin us on this comprehensive journey to becoming a skilled business analyst capable of making data-driven decisions that drive success. Enroll now and take the first step towards mastering business analytics!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Introduction to Business Analytics

Lecture 2 What is Business Analytics?

Lecture 3 Importance and Applications of Business Analytics

Lecture 4 Types of Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive

Lecture 5 The Analytics Process

Section 3: Mathematical and Statistical Foundations for Analytics

Lecture 6 Descriptive Statistics

Lecture 7 Probability Distributions

Lecture 8 Inferential Statistics

Lecture 9 Correlation and Regression Analysis

Lecture 10 Multivariate Statistical Analysis

Section 4: Python Programming for Analytics

Lecture 11 Introduction to Python

Lecture 12 Anaconda & Jupyter & Visual Studio Code

Lecture 13 Google Colab

Lecture 14 Environment Setup

Lecture 15 Python Syntax & Basic Operations

Lecture 16 Data Structures: Lists, Tuples, Sets

Lecture 17 Control Structures & Looping

Lecture 18 Functions & Basic Functional Programming

Lecture 19 Intermediate Functions

Lecture 20 Dictionaries and Advanced Data Structures

Lecture 21 Modules, Packages & Importing Libraries

Lecture 22 File Handling

Lecture 23 Exception Handling & Robust Code

Lecture 24 OOP

Lecture 25 Data Visualization Basics

Lecture 26 Advanced List Operations & Comprehensions

Section 5: Closure

Lecture 27 The End

Aspiring data analysts and business analysts,Professionals transitioning into data-centric roles,Students in data science, business, or related fields,Anyone interested in enhancing their data analysis skills