Prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 5 months. No prior knowledge of computer science or programming languages is required.
Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes. The demand for skilled data scientists who can use data to tell compelling stories to inform business decisions has never been greater.
You’ll learn in-demand skills used by professional data scientists including databases, data visualization, statistical analysis, predictive modeling, machine learning algorithms, and data mining. You’ll also work with the latest languages, tools,and libraries including Python, SQL, Jupyter notebooks, Github, Rstudio, Pandas, Numpy, ScikitLearn, Matplotlib, and more.
Upon completing the full program, you will have built a portfolio of data science projects to provide you with the confidence to excel in your interviews. You will also receive access to join IBM’s Talent Network where you’ll see job opportunities as soon as they are posted, recommendations matched to your skills and interests, and tips and tricks to help you stand apart from the crowd.
This program is ACE® and FIBAA recommended —when you complete, you can earn up to 12 college credits and 6 ECTS credits.
Overview
Syllabus
Course 1: What is Data Science?
- Offered by IBM. Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, ... Enroll for free.
Course 2: Tools for Data Science
- Offered by IBM. In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as ... Enroll for free.
Course 3: Data Science Methodology
- Offered by IBM. If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. ... Enroll for free.
Course 4: Python for Data Science, AI & Development
- Offered by IBM. Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the ... Enroll for free.
Course 5: Python Project for Data Science
- Offered by IBM. This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course ... Enroll for free.
Course 6: Databases and SQL for Data Science with Python
- Offered by IBM. Working knowledge of SQL (or Structured Query Language) is a must for data professionals like Data Scientists, Data Analysts ... Enroll for free.
Course 7: Data Analysis with Python
- Offered by IBM. Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the ... Enroll for free.
Course 8: Data Visualization with Python
- Offered by IBM. One of the most important skills of successful data scientists and data analysts is the ability to tell a compelling story ... Enroll for free.
Course 9: Machine Learning with Python
- Offered by IBM. Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to ... Enroll for free.
Course 10: Applied Data Science Capstone
- Offered by IBM. This is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python ... Enroll for free.
- Offered by IBM. Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, ... Enroll for free.
Course 2: Tools for Data Science
- Offered by IBM. In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as ... Enroll for free.
Course 3: Data Science Methodology
- Offered by IBM. If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. ... Enroll for free.
Course 4: Python for Data Science, AI & Development
- Offered by IBM. Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the ... Enroll for free.
Course 5: Python Project for Data Science
- Offered by IBM. This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course ... Enroll for free.
Course 6: Databases and SQL for Data Science with Python
- Offered by IBM. Working knowledge of SQL (or Structured Query Language) is a must for data professionals like Data Scientists, Data Analysts ... Enroll for free.
Course 7: Data Analysis with Python
- Offered by IBM. Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the ... Enroll for free.
Course 8: Data Visualization with Python
- Offered by IBM. One of the most important skills of successful data scientists and data analysts is the ability to tell a compelling story ... Enroll for free.
Course 9: Machine Learning with Python
- Offered by IBM. Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to ... Enroll for free.
Course 10: Applied Data Science Capstone
- Offered by IBM. This is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python ... Enroll for free.
Courses
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Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field. The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes. Recently, they have carved out a unique and distinct field for the work they do. This field is data science. In today's world, we use Data Science to find patterns in data and make meaningful, data-driven conclusions and predictions. This course is for everyone and teaches concepts like how data scientists use machine learning and deep learning and how companies apply data science in business. You will meet several data scientists, who will share their insights and experiences in data science. By taking this introductory course, you will begin your journey into this thriving field.
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If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems. Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python.
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In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
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Working knowledge of SQL (or Structured Query Language) is a must for data professionals like Data Scientists, Data Analysts and Data Engineers. Much of the world's data resides in databases. SQL is a powerful language used for communicating with and extracting data from databases. In this course you will learn SQL inside out- from the very basics of Select statements to advanced concepts like JOINs. You will: -write foundational SQL statements like: SELECT, INSERT, UPDATE, and DELETE -filter result sets, use WHERE, COUNT, DISTINCT, and LIMIT clauses -differentiate between DML & DDL -CREATE, ALTER, DROP and load tables -use string patterns and ranges; ORDER and GROUP result sets, and built-in database functions -build sub-queries and query data from multiple tables -access databases as a data scientist using Jupyter notebooks with SQL and Python -work with advanced concepts like Stored Procedures, Views, ACID Transactions, Inner & Outer JOINs through hands-on labs and projects You will practice building SQL queries, work with real databases on the Cloud, and use real data science tools. In the final project you’ll analyze multiple real-world datasets to demonstrate your skills.
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Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models. Topics covered include: - collecting and importing data - cleaning, preparing & formatting data - data frame manipulation - summarizing data - building machine learning regression models - model refinement - creating data pipelines You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them. In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge.
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Visualizing data is used by virtually every discipline these days. It is used for analyzing web traffic to determine peak server load, growth and death rate of populations for biological analysis, analyzing weather patterns over time, stock market trends, and so on. Simply put, Data Visualization brings meaning to numbers that help people understand it. Seeing the data change can draw attention to trends and spikes that may otherwise go unnoticed. Python is an open-source (free) programming language has libraries that can be used to read and make useful graphics to present the data. In this course, you will create an application that reads data from CSV files. You will learn how to visualize the data using various techniques using existing Python libraries. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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This is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python Specialization. This capstone project course will give you the chance to practice the work that data scientists do in real life when working with datasets. In this course you will assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting your results to stakeholders. You will be tasked with predicting if the first stage of the SpaceX Falcon 9 rocket will land successfully. With the help of your Data Science findings and models, the competing startup you have been hired by can make more informed bids against SpaceX for a rocket launch. In this course, there will not be much new learning, instead you’ll focus on hands-on work to demonstrate and apply what you have learnt in previous courses. By successfully completing this Capstone you will have added a project to your data science and machine learning portfolio to showcase to employers.
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Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.
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Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher. This introduction to Python course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn about Python basics and the different data types. You will familiarize yourself with Python Data structures like List and Tuples, as well as logic concepts like conditions and branching. You will use Python libraries such as Pandas, Numpy & Beautiful Soup. You’ll also use Python to perform tasks such as data collection and web scraping with APIs. You will practice and apply what you learn through hands-on labs using Jupyter Notebooks. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating real-world tasks using Python. This course is suitable for anyone who wants to learn Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps as well as a number of other job roles.
Taught by
Alex Aklson, Joseph Santarcangelo, Polong Lin, Rav Ahuja and SAEED AGHABOZORGI
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4.0 rating, based on 1 Class Central review
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Beginner-level introduction to Python, notebooks, pandas, numpy, scikit-learn, matplotlib, regression, visualisation, etc.
