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Scholarship For Software Coding & Web Development In Kaduna State - Career - Nairaland

Nairaland Forum › Nairaland General › Career › Scholarship For Software Coding & Web Development In Kaduna State (384 Views)

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Scholarship For Software Coding & Web Development In Kaduna State by DomineIctHubs(op): 3:13pm On Aug 16, 2021
DOMINE ICT ACADEMY
Yy5 Abuja Road By Lagos Street R/About, Kaduna.
Email: domineicthubs@gmail.com Tel: 08036949015, 07045598289; 08068765146

To help you remain a competitive job candidate, here are some of the top skills you should prepare for to land you that dream job:

1. Full Stack Java developer
Core Java + JSP Servlets + Hibernate +Spring
+ Java Web-service/RestFul API + Spring boot

2. Python and Django Full Stack Web Developer
Learn to build websites with HTML, CSS, Bootstrap, JavaScript, jQuery, Python 3, and Django!

3. Full Stack Web Developer
Learn HTML 5, CSS 3, JavaScript, Bootstrap 4,
Media Queries, PHP, OOP, MYSQL, Database

4. Net Core Full Stack Developer
C#, Asp.Net Core,Typescript.js, Angular Js,
Database Development

5. Data Sciences & Data Analytics
What You Will Learn
 Find out how to grab and load data into an analysis environment.
 Perform descriptive analysis to extract meaningful summaries from data.
 Discover probability, parameter estimation, hypothesis tests, and experiment design best practices.
 Get to grips with resampling and bootstrapping in Python
 Delve into statistical tests with variance analysis, time series analysis, and A/B test examples.
 Understand the statistics behind popular machine learning algorithms.
Modules
Module 1: Getting Started with Statistics for Data Science
Module 2: Fundamentals of Data Collection, Cleaning, and Preprocessing
Module 3: Essential Statistics for Data Assessment
Module 4: Visualization with Statistical Graphs
Module 5: Essentials of Statistical Analysis
Module 6: Sampling and Inferential Statistics
Module 7: Common Probability Distributions
Module 8: Parametric Estimation
Module 9: Statistical Hypothesis Testing
Module 10: Statistics for Machine Learning
Module 11: Statistics for Regression
Module 12: Statistics for Classification
Module 13: Statistics for Tree-Based Methods
Module 14: Statistics for Ensemble Methods

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