Univad's Diploma in Data Science program is your gateway to the dynamic and rapidly evolving field of data analytics and machine learning. In today's data-driven world, organizations rely on data scientists to extract valuable insights from complex datasets and make data-driven decisions. This comprehensive program will equip you with the knowledge and skills needed to excel in this exciting and high-demand field.

Throughout this program, you'll dive deep into data manipulation, statistical analysis, machine learning, and data visualization. You'll work on real-world projects, applying the latest data science tools and techniques to solve complex problems. Our experienced instructors and industry experts will guide you through the entire data science lifecycle, from data collection to model deployment.

Upon completion of this program, you'll not only be proficient in data analysis but also have the leadership and communication skills to convey your insights effectively to stakeholders. Whether you aspire to become a data scientist, data analyst, or data-driven decision-maker, our Diploma in Data Science will prepare you for a successful and rewarding career in this booming field.

10 Things You Will Learn:

1. Data Cleaning and Preprocessing

2. Statistical Analysis and Hypothesis Testing

3. Machine Learning Algorithms

4. Data Visualization Techniques

5. Python and R Programming

6. Big Data Technologies (e.g., Hadoop, Spark)

7. SQL and Database Management

8. Data Ethics and Privacy

9. Data Science Project Management

10. Communication and Presentation Skills

Five Skills You Will Have:

1. Data Analysis and Interpretation

2. Machine Learning Model Development

3. Data Visualization and Storytelling

4. Big Data Management

5. Project Leadership and Communication

Top Jobs for Each Skill:

1. Data Scientist

2. Data Analyst

3. Machine Learning Engineer

4. Data Visualization Specialist

Salary Expectations and Demand: Data scientists are in high demand across various industries, and salaries for this role are among the highest in the technology sector. Entry-level data scientists can expect salaries starting at $70,000 to $90,000 per year, with experienced data scientists earning well into six figures. The demand for data science professionals is expected to continue growing as organizations increasingly rely on data-driven strategies for success.

Sample Certificate

Semester 1: Introduction to Data Science

Defining Data Science and What Data Scientists Do

• Course Syllabus

• Professional Certificate Career Support

• What is Data Science?

• Fundamentals of Data Science

• The Many Paths to Data Science

• Advice for New Data Scientists

• Data Science: The Sexiest Job in the 21st Century

• [Optional]Data Science: The Sexiest Job

• Test: Data Science: The Sexiest Job

• Lesson Summary

• A day in the Life of a Data Scientist

• Old problems, new problems, Data Science solutions

• Data Science Topics and Algorithms

• Cloud for Data Science

• Discussion: Introduce Yourself

• What Makes Someone a Data Scientist?

• Reading: What makes Someone a Data Scientist?

• Test: What Makes Someone a Data Scientist?

• Reading: Lesson Summary

Data Science Topics

• Foundations of Big Data

• What is Hadoop?

• How Big Data is Driving Digital Transformation

• Data Science Skills & Big Data

• Data Scientists at New York University

• Data Mining

• Reading: [Optional] Data Mining

• Test: Data Mining

• Reading: Lesson Summary

• What's the difference?

• Neural Networks and Deep Learning

• Applications of Machine Learning

• Reading: [Optional] Regression

• Test: Regression

• Reading: Lesson Summary

• Lab: Exploring Data using IBM Cloud Gallery

• Reading: Exploring Data using IBM Cloud Gallery

Data Science in Business

• How Data Science is saving lives

• How Should Companies Get Started in Data Science?

• Applications of Data Science

• Applications of Data Science

• Reading: [Optional] The Final Deliverable

• Test: The Final Deliverable

• Reading: Lesson Summary

• How Can Someone Become a Data Scientist?

• Recruiting for Data Science

• Careers in Data Science

• High School Students and Data Science Careers

• Reading: Lesson Summary

• The Report Structure

• Reading: [Optional] The Report Structure

• Test: The Report Structure

• Reading: Lesson Summary

• Peer-graded Assignment: Final Assignment

• Final Exam

Semester 2: Data Science Tool Kit

Overview of Data Science Tools

• Course Introduction

• Reading: Learning goals for the course

• Categories of Data Science Tools

• Open Source Tools for Data Science - Part 1

• Open Source Tools for Data Science - Part 2

• Commercial Tools for Data Science

• Cloud Based Tools for Data Science

• Reading: Module 1 Summary

• Practice Quiz - Data Science Tools

Language of Data Science

• Languages of Data Science

• Introduction to R Language

• Introduction to SQL

• Other Languages for Data Science

• Reading: Module 2 Summary

• Practice Quiz - Languages

Packages, APIs, Datasets and Models

• Libraries for Data Science

• Application Programming Interfaces (APIs)

• Data Sets - Powering Data Science

• Lab plugin: Additional Sources of Datasets

• Sharing Enterprise Data - Data Asset eXchange

• Predicting with Learned Machine Learning Models

• The Model Asset eXchange

• Lab: Getting Started with Model and Data Exchange

• Reading: Module 3 Summary

• Practice Quiz - Libraries, APIs, Data Sets, Models

Jupyter Notebooks and JubpyterLab

• Introduction to Jupyter Notebooks

• Getting Started with Jupyter

• Lab: Getting Started with Jupyter Notebook

• Jupyter Kernels

• Lab: Using Markdown in Jupyter Notebooks

• Jupyter Architecture

• Lab: Working with Files in Jupyter Notebooks

• Additional Anaconda Jupyter Environments

• Additional Cloud Based Jupyter Environments

• Lab: Download & Install Anaconda

• Lab: Jupyter Notebooks on the Internet

• Reading: Module 4 Summary

• Practice Quiz - Jupyter Notebooks and Jupyter Lab

Lab Plugin: RStudio and GitHub

• Introduction to R and RStudio

• Optional Reading: Download & Install R and RStudio

• Lab: R Basics with RStudio

• Plotting in RStudio

• Getting started: Installing packages in RStudio

• Lab: Creating Data Visualizations using ggplot

• Lab: Plotting with RStudio

• Overview of Git/GitHub

• Introduction to GitHub

• GitHub Repositories

• GitHub - Getting Started

• Lab plugin: Getting Started with GitHub

• GitHub - Working with Branches

• Lab: Getting Started with Branches using Commands

• Lab plugin: Branching and Merging (Web UI)

• Reading: Module 5 Summary

• Glossary

• Practice Quiz - RStudio

• Practice Quiz - GitHub

Create and Share your Jupyter Notebook

• Lab plugin: Create and Share Your Jupyter Notebo

• Lab: Create your Jupyter Notebook

• Graded Assignment

• Final Exam

IBM Watson Studio

• Introduction to Watson Studio

• Optional: Creating an account on IBM Watson Studio

• Lab: Get IBM Cloud Feature Code with Trial Account

• Jupyter Notebooks in Watson Studio - Part 1

• Jupyter Notebooks in Watson Studio - Part 2

• Lab plugin: Watson Studio Project with JupyterNote

• Linking GitHub to Watson Studio

• Assignment using Watson Studio

• Reading: Summary

• Quiz - Watson Studio

Semester 3: Data Science Methodology

From Problem to Approach

• Syllabus

• Welcome

• Lab plugin: Introduction to CRISP - DM

• Business Understanding

• Analytic Approach

• Lab: From Problem to Approach

• Quiz: From Problem to Approach

• Reading: Lesson Summary

• Data Requirements

• Data Collection

• Lab: From Requirements to Collection

• Quiz: From Requirements to Collection

• Reading: Lesson Summary

From Modeling to Evaluation

• Data Understanding

• Data Preparation - Concepts

• Reading: Correction

• Data Preparation - Case Study

• Lab: From Understanding to Preparation

• Quiz: From Understanding to Preparation

• Reading: Lesson Summary

• Modeling - Concepts

• Modeling - Case Study

• Evaluation

• Lab: From Modeling to Evaluation

• Quiz: From Modeling to Evaluation

• Reading: Lesson Summary

From Deployment to Feedback

• Deployment

• Feedback

• Quiz: From Deployment to Feedback

• Reading: Lesson Summary

• Graded Assignment

• Final Exam

Semester 4: Python Project for Data Science

Crowdsourcing short squeeze Dashboard

• Welcome

• Intro to Web Scraping

• HTML for Webscraping

• Webscrapping

• Intro to Web Scraping Using BeautifulSoup

• Reading: Project Overview

• Reading: Stock shares

• Lab: Extracting Stock Data Using a Python Library

• Quiz: Extracting Stock Data Using a Python Library

• Lab: Extracting Stock Data Using Web Scraping

• Quiz: Extracting Stock Data Using a Web Scraping

• Optional: Gamestop stock vs Tesla

• Lab: Analyzing Historical Stock/Revenue Data

• Obtain IBM Cloud Feature Code and Activate Trial

• Lab plugin: Lab: Create IBM Cloud account

• Jupyter Notebook to complete your final project

• Lab plugin: Add notebook to Watson Studio

• Reading: Analyzing Historical Stock/Revenue Data

• Lab plugin: Share your notebook from Watson Studio

• Assignment: Analyzing Historical Stock/Revenue

Semester 5: Spatial Data Science

Understanding of Spatial Data Science

• Introduction to the course

• Introduction to Spatial Data Science

• Survey: Introduction to Spatial Data Science

• Why is Spatial Special? (I) - A Business Perspect

• Survey: Why is Spatial Special? (I) - A Business Perspect

• Why is Spatial Special? (II) - Technical Perspect

• Why is Spatial Special? (III) - A Data Perspective

• Quiz: Understanding Spatial Data Science

• Survey: Quiz: Understanding Spatial Data Science

Solution Structures of Spatial Data Science

• Four Disciplines for Spatial Data Science and Apps

• Open Source Software's

• Reading: QGIS vs. ArcGIS

• Spatial Data Science Problems

• Spatial Data vs. Spatial Big Data

• Reading: What is spatial Big Data?

• Quiz: Solution Structures of Spatial Data Science

• Survey: Quiz: Solution Structures of Spatial Data Science

Geographic Information System (GIS)

• Five Layers of GIS

• Spatial Reference Framework

• Discussion prompt

• Survey: Discussion prompt

• Spatial Data Models

• Spatial Data Acquisition

• Reading: Sources of Spatial Data

• Spatial Data Analysis

• Geo-visualization and Information Delivery

• Reading: Making Sense of Maps

• Quiz: Geographic Information System (GIS)

• Survey: Quiz: Geographic Information System (GIS)

Spatial DBMS and Big Data Systems

• Database Management System (DBMS)

• Spatial Database Management System (SDBMS)

• Big Data System – MapReduce

• Reading: DBMS vs. MapReduce

• Big Data System – Hadoop

• Hadoop Ecosystem

• Spatial Big Data Systems

• Quiz: Spatial DBMS and Big Data Systems

• Survey: Quiz: Spatial DBMS and Big Data Systems

Spatial Data Analytics

• Spatial Data Analytics

• Proximity and Accessibility

• Reading: Starbucks GIS

• Spatial Autocorrelation

• Spatial Interpolation

• Spatial Categorization

• Hotspot Analysis

• Network Analysis

• Reading: Happy Maps

• Quiz: Spatial Data Analytics

• Survey: Quiz: Spatial Data Analytics

Practical Applications of Spatial Data Science

• Finding Optimal Counties for Timber Investment

• Survey: Finding Optimal Counties for Timber Investment

• An Integration of Municipal Spatial Databases

• Variables of Regional Disease Prevalence Rate

• Military Infiltration Route Analysis

• Reading: Infiltration route analysis

• Taxi Trajectory Analysis for Finding Pick-up Hotsp

• Quiz: Practical Application of Spatial Data Scienc

• Survey: Quiz: Practical Application of Spatial Data Scienc

The next application deadlines are:

• Priority Deadline: September 1, 2024

• Final Deadline: September 15, 2024

Have questions? Attend an upcoming Information Session or use the chat box at the bottom right corner of the screen.

For more information about the Univad Diploma Tuition, session durations, and deadlines, kindly visit the Tuition page to access this information and even more.

Self-paced

• Students who hold Bachelor's degrees and wish to concentrate or expand their knowledge in the field

• Professionals who have several years of working experience

• Anyone who wishes to gain a new skill for personal growth or to start a career.