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Romeo Kienzler, Alex Aklson, Rav Ahuja, Polong Lin, Joseph Santarcangelo, SAEED AGHABOZORGI, Sourav Mazumder, Linda Liu, Svetlana Levitan, Maureen McElaney, Yan Luo, Saishruthi Swaminathan, Azim Hirjani, and Saeed Aghabozorgi

Data science and machine learning skills continue to be in highest demand across industries, and the need for data practitioners is booming. Upon completing this Professional Certificate program, you will be armed with the skills and experience you need to start your career in data science and machine learning. Through hands-on assignments and high-quality instruction, you will build a portfolio using real data science tools and real-world problems and data sets. The curriculum will cover a wide range of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. There is no requirement for prior computer science or programming knowledge in order to take this program.

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Data science and machine learning skills continue to be in highest demand across industries, and the need for data practitioners is booming. Upon completing this Professional Certificate program, you will be armed with the skills and experience you need to start your career in data science and machine learning. Through hands-on assignments and high-quality instruction, you will build a portfolio using real data science tools and real-world problems and data sets. The curriculum will cover a wide range of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. There is no requirement for prior computer science or programming knowledge in order to take this program.

Anyone with some computer skills and a passion for self-learning can succeed as we begin small and build up to more complex problems and topics.

With the tremendous need for data science and data analyst professionals in the market today, this program will jumpstart your path in data science and prepare you with a portfolio of data science deliverables to give you the confidence to take the plunge and start your data science career.

What you'll learn

  • Apply various Data Science and Machine Learning skills, techniques, and tools to complete a project and publish a report.
  • Practice with various tools used by Data Scientists and become experienced in using some of them like Jupyter notebooks.
  • Master the key steps involved in tackling a data science problem and learn to follow a methodology to think and work like a Data Scientist.
  • Write SQL to query databases and explore relational database concepts.
  • Understand Python and practice Python programming using Jupyter.
  • Import and clean data sets, analyze data, build and evaluate data models and pipelines using Python.
  • Utilize several data visualization tools, techniques and libraries in Python to present data visually.
  • Understand and apply various supervised and unsupervised Machine Learning models and algorithms to address real world challenges using Python.

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What's inside

Ten courses

Data Science Tools

(35 hours)
In this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these tools today.

Analyzing Data with Python

(15 hours)
Learn to analyze data using Python in this introductory course. You will go from understanding the basics of Python to exploring many different types of data through lecture, hands-on labs, and assignments. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

Visualizing Data with Python

(15 hours)
A picture is worth a thousand words. This especially applies when trying to explain the insights obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.

The Data Science Method

(35 hours)
Despite an influx in computing power and access to data over the last couple of decades, our ability to use data within the decision-making process is either lost or not maximized all too often. This course shares the methods, models, and practices that can be applied within data science to ensure that the data used in problem-solving is relevant and properly manipulated to address business and real-world challenges.

Introduction to Data Science

(27 hours)
The art of uncovering insights and trends in data has been around for centuries. This field is data science and in this course, you will meet some big data science practitioners and we will get an overview of what data science is today.

Data Science and Machine Learning Capstone Project

(21 hours)
Now that you've taken several courses on data science and machine learning, it’s time to put your learning to work on a data problem involving a real life scenario. Employers really care about how well you can apply your knowledge and skills to solve real world problems, and the work you do in this capstone project will make you stand out in the job market.

SQL for Data Science

(12 hours)
Much of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and extracting various data types from databases. A working knowledge of databases and SQL is necessary to advance as a data scientist or a machine learning specialist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language.

Python Basics for Data Science

(21 hours)
Kickstart your Python for data science journey with this beginner-friendly course. You'll learn Python basics, work with data in Python, and create your own Python scripts. Upon completion, you'll be able to perform basic hands-on data analysis using Jupyter Notebooks.

Machine Learning with Python: A Practical Introduction

(25 hours)
This Machine Learning with Python course dives into the basics of machine learning using Python. You'll learn about supervised vs. unsupervised learning, statistical modeling, and popular algorithms like Classification, Regression, Clustering, and Dimensional Reduction. Real-life examples will showcase the impact of machine learning on society. Hands-on labs will transform your theoretical knowledge into practical skills.

Python for Data Science Project

(4 hours)
This mini-course is intended for you to demonstrate foundational Python skills for working with data. You will work on a hands-on project where you will develop a simple dashboard using Python.

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