Alexander Afriyie (Alexander A.) is a lecturer, private investigator, journalist, author, course content developer, cyber security and Artificial Intelligent (AI) expert. With such credentials, he has become an authority in his fields with knowledge and experience that truly stands out.
Alexander Afriyie, also known as Alexoo or Prof by his buddies, is an author who has written more than 500 books and online courses.
Alexander Afriyie (Alexander A.) is currently pursuing PhD in Business Management.
He is an accomplished academic, having earned multiple master's degrees from the University of Maryland Global Campus in both Master of Science in Cyber Security and master's in business administration in USA. Alexander studied a Bachelor of Arts degree in Religious Studies and History at the University of Ghana, Legon. Additionally, the biographer has obtained a Bachelor of Arts Degree in Communication Studies with a major in Journalism from the Ghana Institute of Journalism (GIJ). Furthermore, he has a Higher National Diploma in Hotel Catering and Institutional Management (HCIM) from Kumasi Polytechnic. He has Diploma in Media Law and Ethics from the United Kingdom. The author's academic background began at Kumawu Anglican School where he attended from Class One to Form Four; then progressed to Tweneboa Kouda Senior School in Kumawu for his "O" levels. Subsequently, Alexander completed his "A" Levels privately at Kumasi Workers College.
His vast repertoire of works includes academic books about cyber security, management, financials, artificial intelligence, catering, story books, and history books. As a highly accomplished writer, Alexander Afriyie is also the CEO of several companies such as America Street Thinkers, Lexisv.com, udeme.us, CKC.institute.com, pettutor.us, petcoach.us, Certifiedcourses.com, ghanacrimereport.com ,apextechtraining.com, Readonline.us, booksghana.com , pupilslibrary.us, pupilslibrary.com, Healthcaretraining.us, dogbasicsinfo.us.
Introduction
Welcome to Introduction to Data Science. This course will give you an overview of the exciting and growing field of data science. You will explore the fundamentals of data science, learn how data science is used in various industries, and gain hands-on experience using Python programming language. Through lectures, case studies, and exercises, you will develop the skills necessary to understand data and create applications that can help answer important questions. At the end of this course, you will have a foundational understanding of data science and its impact on modern business operations.
Objectives
1. Develop the students' understanding of the basic concepts surrounding data science.
2. Provide hands-on experience with important software tools such as Python and SQL for manipulating, analysing and visualizing data.
3. Equip students with tools for communicating data science topics effectively to stakeholders.
4. Facilitate development in particular topics such as machine learning, predictive analytics, and natural language processing for practical applications in data science projects.
5. Foster an understanding of the ethical implications of conducting data analysis on large datasets from diverse populations.
Course Outline
Module 1 - Introduction to Data Science
• Overview of data science
• Role of data scientist
• Applications of data science in the industry
• Exploring the different datasets available for analysis.
Module 2 - Getting Started with Data Science
• Introduction to Statistics
• Descriptive and Inferential Statistics
• Mathematical Foundations of Data Science
Module 3 - Working with Data Sources and Formats
• Preparing data for analysis
• Importing, manipulating, and exporting large datasets into various formats like JSON, CSV, XML etc.
Module 4 - Exploratory Data Analysis (EDA)
• Exploring the relationship between variables using univariate and bivariate analysis techniques
• Dimensionality Reduction Techniques like Principal Component Analysis (PCA) Module
5- Machine Learning Algorithms
• Types of Machine Learning algorithms like Supervised, Unsupervised & Reinforcement Algorithms
• Various machine learning techniques such as Naïve Bayes Classifier, Support Vector Machines (SVM), Decision Trees, Random Forest etc.
Module 6 - Building Predictive Models
• Building predictive models through Model Selection & Evaluation techniques such as Hyperparameter Tuning, Cross Validation and Regularization Methods.
Module 7 - Big Data Technologies & Tools
Module 8 – Visualizing Results using Tableau/R/Python
Título : Data Science
EAN : 9798223835288
Editorial : Alexander Afriyie
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