Currently, the need for specialists has quadrupled along with the trend of annual big data usage. These professionals are to achieve big practice and theory knowledge in both data science and AI, thus our program “Dual Certification in Data Sciences and AI with Windows Administration” fits perfectly to their needs. Apart from leveling up the learner’s data science skills through numerous practicals, the course also aims at giving system administration skills on the Windows platform. Additionally, the intensive educational program enables participants to gain the hands-on experience necessary to easily execute technical roles in diverse industries and secure their career trajectory.
covers the fundamentals of data science, including software installation with practical exercises on setting up Anaconda and Jupyter. It introduces Excel for data analysis, focusing on mastering data cleaning techniques for effective data processing. Additionally, it explores data visualization in Excel using charts for basic visualization techniques.
advances Excel skills for data analysis, emphasizing efficient subtotaling and analysis using Excel functions. It delves into pivot tables for effective data summarization and enhances data analysis and visualization skills. The module also includes data linking for comprehensive reports, providing hands-on exercises for creating detailed reports using Excel.
introduces Python programming for data science, covering fundamental concepts such as basics, operators, control flow, loops, and functions. It teaches advanced data manipulation using lambda functions and provides a strong foundation in NumPy, focusing on arrays, indexing, and slicing for efficient data handling.
focuses on data manipulation and visualization with Python. It covers data handling using Pandas and techniques for mastering data analysis. Additionally, it explores data visualization methods to present insights effectively using Python-based tools.
introduces SQL and data storage, starting with SQL fundamentals for data retrieval and basic querying. It explains data modeling fundamentals and progresses to advanced SQL operations, including joins, to enhance SQL knowledge for handling complex queries.
provides an introduction to machine learning, focusing on regression basics and understanding core concepts. It covers essential techniques for preprocessing data for modeling and explains predictive modeling using linear and multiple linear regression, along with evaluation metrics for assessing model performance.
advanced machine learning techniques, including logistic regression and classification metrics for evaluating model accuracy. It introduces decision trees and ensemble learning methods to enhance model performance. Additionally, it explores clustering techniques such as K-Means and hierarchical clustering for unsupervised learning, along with advanced classification models like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN)..
focuses on time series analysis and ensemble learning, covering fundamental modeling techniques using ARIMA and SARIMA. It further explores advanced model performance techniques with ensemble algorithms like XGBoost and LightGBM for more efficient predictive analytics.
focuses on data visualization and reporting tools, introducing Power BI for creating interactive reports that enhance data presentation and insights. It also covers the fundamentals of Tableau, enabling users to build effective data visualizations for better decision-making. Additionally, the module provides an introduction to R, covering the basics of the R language, working with data frames, and applying functions for efficient data analysis.
