Name: Business Analysis and Data Science
Price: 6000 BDT
Availability: InStock

Business Analysis and Data Science

Overview:
In the contemporary landscape of business, effective decision-making is contingent upon harnessing the insights embedded within vast datasets. The course on Business Analysis & Data Science seeks to empower participants with the requisite tools and methodologies to navigate this data-rich environment. Focused on Smart PLS and Neural Networks, the course delves into the realms of statistical modeling and machine learning, offering a dual-pronged approach to extracting meaningful patterns from complex business datasets.

Objective:
This course aims to equip participants with a profound understanding of Smart PLS and Neural Networks, enabling adept analysis of business data. Integrating structural equation modeling and neural networks, attendees will develop skills for informed decision-making. Moreover, participants will gain expertise in research methodologies, facilitating faculty members to publish impactful research projects and papers, enriching academic and professional discourse.

কোর্স কারিকুলাম

Understanding the importance of data analysis in business decision-making
Introduction to the Smart PLS methodology and its applications.
Preprocessing data using Smart PLS: cleaning, transforming, and handling missing values.
Hands-on exercises: Preprocessing a sample dataset using Smart PLS.

Exploring the key concepts of SEM and its relevance in business research.
Building and estimating SEM models using Smart PLS.
Assessing model fit and reliability.
Hands-on exercises: Constructing and analyzing a SEM model using Smart PLS.

Understanding supervised learning and differentiating between classification and regression models.
Defining linear and logistic regression models to perform regression and classification tasks.
Applying gradient descent algorithm to optimize regression parameters.

Understanding how artificial neural networks work.
Understanding and tuning different hyperparameters such as learning rate, activation functions, number of hidden layers, number of neurons, optimizers, and so on.

Training Artificial Neural Networks using SPSS.
Evaluating classification and regression models using appropriate metrics.

Combining Smart PLS and Neural Networks for enhanced data analysis
Leveraging the strengths of both methodologies in solving complex business problems
Case studies and practical examples of integrating Smart PLS and Neural Networks
Hands-on exercises: Applying Smart PLS and Neural Networks to real-world business datasets

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Learning Outcomes:
* Possess a solid understanding of Smart PLS and Neural Networks
* Competently preprocess and analyze business data using Smart PLS
* Build, estimate, and assess Structural Equation Models
* Apply optimization techniques for regression and classification tasks
* Demonstrate proficiency in training Neural Networks using SPSS
* Integrate Smart PLS and Neural Networks for advanced data analysis

Methodology:
The course employs a blend of theoretical sessions, hands-on exercises, and practical case studies. Participants will actively engage with Smart PLS and Neural Network tools, ensuring a holistic learning experience. The methodology emphasizes the application of acquired knowledge to real-world business scenarios, fostering a deep understanding of the subject matter.

Who Should Attend:
This course is designed for professionals and decision-makers involved in business analysis, research, and data-driven decision-making. It is suitable for individuals seeking to enhance their skills in statistical modeling, machine learning, and the integration of these methodologies for strategic decision support. No prior experience with Smart PLS or Neural Networks is required, making it accessible to a broad audience.