Dual Certification Program in Data Science & AI with Windows Administration

In today’s data-driven world, professionals with a strong foundation in data science, AI, and systems administration are invaluable. This program combines two high-demand skill sets to empower individuals to work seamlessly in tech-intensive roles. Our dual certification in Data Science & AI with Windows Administration is designed to provide hands-on knowledge across essential data science techniques and robust system administration skills on the Windows platform.

Who Should Enroll:
This program is ideal for aspiring data scientists, AI enthusiasts, IT professionals, system administrators, and recent graduates seeking a career that combines data science with foundational Windows Administration skills.

Key Program Highlights

Dual Certification: Gain credentials in both Data Science & AI and Windows Administration.
Comprehensive Curriculum: From Python programming and statistical analysis to Windows server setup and network management.
Hands-On Learning: Real-world projects and lab sessions.
Industry-Relevant Tools & Technologies: Training in Python, SQL, Power BI, TensorFlow, Windows Server, and Active Directory.
Flexible Learning Options: Available both online and in-person

Why Choose Us?

Expert Faculty: Learn from industry experts with years of experience in data science and Windows administration.
Comprehensive Learning Materials: Gain access to exclusive tutorials, case studies, and project workbooks.
State-of-the-Art Infrastructure: Fully-equipped labs and cloud-based learning environments.
Post-Certification Support: Lifetime access to webinars, Q&A sessions, and learning materials.

Module 1

Data Science Essentials
Software Installation: Practical exercise on setting up Anaconda and Jupyter.
Fundamentals of Excel: Introduction to Excel for data analysis.
Master Data Cleaning Techniques: Learn effective data cleaning in Excel.
Visualize Data with Excel Charts: Basic data visualization techniques.

Module 2

Advanced Excel for Data Analysis
Efficient Subtotaling and Analysis: Using Excel's functions for data analysis.
Pivot Tables for Data Summarization: Summarizing data efficiently in Excel.
Data Analysis and Visualization: Deepening Excel data analysis and visualization skills.
Data Linking for Comprehensive Reports: Practical exercise on creating comprehensive reports with Excel.

Module 3

Python Programming for Data Science
Python Fundamentals: Basics, operators, control flow, loops, and functions.
Advanced Data Manipulation with Lambda: Using lambda functions for data manipulation.
NumPy Fundamentals: Working with arrays, indexing, slicing

Module 4

Data Manipulation & Visualization with Python
Data Manipulation with Pandas: Mastering Pandas for data analysis.
Data Visualization: Techniques to visualize data insights with Python.

Module 5

SQL and Data Storage
Introduction to SQL & Basic Querying: SQL fundamentals for data retrieval.
Data Modeling Fundamentals: Introduction to data modeling concepts.
Advanced SQL Operations and Joins: Deepening SQL knowledge for complex queries.

Module 6

Introduction to Machine Learning
Introduction to Machine Learning and Regression Basics:
Understanding the basics of machine learning and regression analysis.
Data Preprocessing Essentials: Techniques for preparing data for modeling.
Linear and Multiple Linear Regression: Predictive modeling and evaluation metrics.

Module 7

Advanced Machine Learning Technique
Logistic Regression and Classification Metrics: Mastering classification models and metrics.
Decision Trees and Ensemble Learning: Understanding tree-based models and methods to enhance model performance.
Clustering Techniques: Introduction to K-Means and Hierarchical Clustering for unsupervised learning.
Support Vector Machines (SVM) and K-Nearest Neighbors (KNN): Advanced machine learning models for classification.

Module 8

Time Series Analysis and Ensemble Learning
Time Series Modeling with ARIMA and SARIMA: Fundamentals of modeling time series data.
Practical Exercise: Implementing ensemble algorithms like XGBoost and LightGBM for advanced model performance.

Module 9

Time Series Analysis and Ensemble Learning
Deep Learning Architectures and Training: Introduction to CNNs and RNNs and the basics of training deep models.
Natural Language Processing (NLP): Essentials of NLP, text preprocessing, and model creation.

Module 10

Data Visualization and Reporting Tools
Power BI: Introduction to Power BI for creating interactive reports.
Tableau: Fundamentals of using Tableau for datavisualization.
Introduction to R: Basics of the R language, working with data frames, and applying functions for data analysis.

Certification Details:
Upon successful completion, participants receive dual certification in Data Science & AI and Windows Administration, recognized by industry leaders.

Career Support:
Our dedicated career services include:

Interview Preparation: Resume building, mock interviews, and technical assessments.
Networking Opportunities: Access to our network of alumni and industry experts.
Job Placement Assistance: Guidance to connect you with job opportunities and internships.