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

Homepage:

https://www.udemy.com/course/business-management-analytics/