Business & Management Analytics
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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:
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Mathematical and Statistical Foundations: Understand the core principles of statistics and mathematics that form the backbone of data analysis.
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Python Programming: Learn the basics of Python programming and how to use it for data analysis.
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Principles of Data Analytics: Gain insights into data collection, cleaning, preprocessing, and exploratory data analysis.
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Predictive Analytics and Modeling: Explore various predictive modeling techniques including linear regression, logistic regression, decision trees, and more.
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Optimization and Decision Models: Learn about linear programming, integer programming, nonlinear programming, and other optimization techniques.
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Machine Learning: Get hands-on experience with supervised and unsupervised learning algorithms, model evaluation, and neural networks.
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Advanced Machine Learning Techniques: Dive into support vector machines, reinforcement learning, natural language processing, and more.
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Network Analytics: Understand social network analysis, community detection, and graph algorithms.
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Data Structures and Algorithms: Build a strong foundation in data structures and algorithms crucial for efficient data processing.
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Database Technologies: Master SQL, NoSQL, and distributed systems for effective data management.
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Data Wrangling and Visualization: Learn data cleaning, transformation, integration, and visualization techniques using tools like Tableau and Power BI.
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Multi-Criteria Decision Making: Analyze decision-making processes using techniques like AHP and TOPSIS.
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Simulation Modeling: Explore discrete-event simulation, system dynamics, agent-based modeling, and Monte Carlo simulation.
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Stochastic Optimization: Learn about stochastic linear programming, chance-constrained programming, and other stochastic optimization methods.
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Web and Social Network Analytics: Analyze web and social media data for business insights.
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Performance Analytics with DEA: Measure efficiency and performance using Data Envelopment Analysis.
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Soft Computing Techniques: Apply fuzzy logic systems, genetic algorithms, and neural networks in soft computing.
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Customer Analytics: Manage and analyze customer data for better decision-making.
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Big Data Technologies: Understand big data frameworks like Hadoop and Spark.
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Practical Data Science Projects: Implement end-to-end data science projects, from data collection to model deployment.
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Communication and Data Storytelling: Effectively communicate data insights and build compelling data narratives.
Who This Course Is For:
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Aspiring data analysts and business analysts
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Professionals looking to transition into data-centric roles
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Students pursuing degrees in data science, business, or related fields
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Anyone interested in enhancing their data analysis skills
Join 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!
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6Introduction to PythonVídeo Aula
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7Anaconda & Jupyter & Visual Studio CodeVídeo Aula
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8Google ColabVídeo Aula
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9Environment SetupVídeo Aula
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10Python Syntax & Basic OperationsVídeo Aula
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11Data Structures: Lists, Tuples, SetsVídeo Aula
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12Control Structures & LoopingVídeo Aula
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13Functions & Basic Functional ProgrammingVídeo Aula
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14Intermediate FunctionsVídeo Aula
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15Dictionaries and Advanced Data StructuresVídeo Aula
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16Modules, Packages & Importing LibrariesVídeo Aula
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17File HandlingVídeo Aula
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18Exception Handling & Robust CodeVídeo Aula
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19OOPVídeo Aula
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20Data Visualization BasicsVídeo Aula
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21Advanced List Operations & ComprehensionsVídeo Aula
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27Data QualityVídeo Aula
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28Data Cleaning TechniquesVídeo Aula
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29Handling Missing ValuesVídeo Aula
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30Dealing With OutliersVídeo Aula
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31Feature Scaling and NormalizationVídeo Aula
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32StandardizationVídeo Aula
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33Encoding Categorical VariablesVídeo Aula
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34Feature EngineeringVídeo Aula
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35Dimensionality ReductionVídeo Aula
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36Mathematical Modeling - IntroVídeo Aula
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37Mathematical Modeling SymbolsVídeo Aula
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38Linear Programming ScenarioVídeo Aula
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39Linear Programming - Math ModelVídeo Aula
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40Linear Programming with PuLPVídeo Aula
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41Linear Programming OutputVídeo Aula
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42Nonlinear Programming (NLP) - IntroVídeo Aula
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43Nonlinear Programming - ScenarioVídeo Aula
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44Nonlinear Programming - CodeVídeo Aula
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45Nonlinear Programming - OutputVídeo Aula
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46Network Flows - IntroVídeo Aula
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47Network Flows - ProjectsVídeo Aula
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48Network Flows - Minimum Cost ProblemVídeo Aula
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49Network Flows - Maximum Flow & Ford-FulkersonVídeo Aula
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50Network Flows - Graphical Representation With NetworkxVídeo Aula
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51Network Flows - Bellman-FordVídeo Aula
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52Network Flows - Edmonds-KarpVídeo Aula
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53Genetic AlgorithmVídeo Aula
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54Genetic Algorithm TermsVídeo Aula
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55Genetic Algorithm ScenarioVídeo Aula
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56Genetic Algorithm - Math ModelVídeo Aula
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57Genetic Algorithm - CodeVídeo Aula
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58Genetic Algorithm - OutputVídeo Aula
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59Large Scale OptimizationVídeo Aula
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64Robust Optimization - Intro and ProjectVídeo Aula
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65Robust Optimization - Mathematical ModelVídeo Aula
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66Robust Optimization - Code & OutputVídeo Aula
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67Dynamic Programming - IntroVídeo Aula
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68Dynamic Programming - ScenarioVídeo Aula
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69Dynamic Programming - Mathematical ModelVídeo Aula
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70Dynamic Programming - CodeVídeo Aula
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71Dynamic Programming - OutputVídeo Aula
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72Multi-Period Portfolio Optimization with JuliaVídeo Aula