What is Data Science?
Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For most organizations, data science is employed to transform data into value in the form of improved revenue, reduced costs, business agility, improved customer experience, and the development of new products.
The business value of data science
The business value of data science depends on organizational needs. Data science could help an organization build tools to predict hardware failures, allowing the organization to perform maintenance and prevent unplanned downtime. It could help predict what to put on supermarket shelves, or how popular a product will be based on its attributes.
“A better world won’t come about simply because we use data; data has its dark underside.”
― MikeLoukides, Ethics and Data Science.
About IBM Data Science Professional Certificate:
This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience.
Anyone with a passion for learning can take this Professional Certificate no prior knowledge of computer science or programming languages required and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.
The program consists of 9 online courses that will provide you with the latest job-ready tools and skills, including open-source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You will be able to learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.
After successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in data science.
Applied Learning Project
The IBM data science professional certificate will give you a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you practical skills with applicability to real jobs, such as:
Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio.
Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
Projects: random album generator, predict housing prices, best classifier model, battle of neighborhoods.
There are 9 Courses in this Professional Certificate
1. What is Data Science? (Content ratings 96%)
The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year.
- Defining Data Science and What Data Scientists Do- In this module, you will view the course syllabus to learn what will be taught in this course.
- Data Science Topics- In this module, you will hear from Norman White, the Faculty Director of the Stern Centre for Research Computing at New York University.
- Data Science in Business- In this module, you will learn about the approaches companies can take to start working with data science.
2. Tools for Data Science (83%)
In this course, you will learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience.
- You will learn about analytics, story-telling- This week, you will get an overview of the programming languages commonly used, including Python, R, Scala, and SQL.
- Open Source Tools- This week, you will learn about three popular tools used in data science: GitHub, Jupyter Notebooks, and RStudio IDE.
- IBM Tools for Data Science- This week, you will learn about an enterprise-ready data science platform by IBM, called Watson Studio. You’ll learn about some of the features and capabilities of what data scientists use in the industry.
- Final Assignment: Create and Share Your Jupyter Notebook- This week, you will demonstrate your skills by creating and configuring a Jupyter Notebook.
3. Data Science Methodology (93%)
In this course of the IBM data science program, you will learn the major steps involved in tackling a data science problem.
- From Problem to Approach and From Requirements to Collection- In this module, you will learn about why you are interested in data science, what a methodology is, and why data scientists need a methodology.
- From Understanding to Preparation and From Modeling to Evaluation- In this module, you will learn what it means to understand data and prepare or clean data.
- From Deployment to Feedback- In this module, you will learn about what happens when a model is deployed and why model feedback is important.
4. Data Science and AI with Python (92%)
You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules of the IBM data science program, and create a final project to demonstrate your new skills.
- Python Basics.
- Python Data Structures.
- Python Programming Fundamentals.
- Working with Data in Python.
- Analyzing US Economic Data and Building a Dashboard.
5. Databases and SQL for Data Science (92%)
The purpose of this course in the IBM data science program is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language.
- Introduction to Databases and Basic SQL- You will be introduced to databases. You will create a database instance on the cloud. You will learn some of the basic SQL statements.
- Advanced SQL- You will learn the following: (1) Learn how to use string patterns and ranges to search data and how to sort and group data in result sets. (2) Learn how to work with multiple tables in a relational database using join operations.
- Accessing Databases using Python- You will learn how to explain the basic concepts related to using Python to connect to databases and then create tables, load data, query data using SQL.
- Course Assignment- You will be working with multiple real-world datasets for the city of Chicago. You will be asked questions that will help you understand the data just like a data scientist would.
6. Data Analysis with Python (94%)
Learn how to analyze data using Python. This course in the IBM data science professional certificate program will take you from the basics of Python to exploring many different types of data.
- Importing Datasets.
- Data Wrangling.
- Exploratory Data Analysis.
- Model Development.
- Model Evaluation.
- Final Assignment.
- IBM Digital Badge.
7. Data Visualization with Python (90%)
The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people.
- Introduction to Data Visualization Tools- In this module, you will learn about data visualization and some of the best practices to keep in mind when creating plots and visuals.
- Basic and Specialized Visualization Tools- In this module, you learn about area plots and how to create them with Matplotlib, histograms, and how to create them with Matplotlib, bar charts..
- Advanced Visualizations and Geospatial Data- In this module, you will learn about advanced visualization tools such as waffle charts and word clouds and how to create them.
8. Machine Learning with Python (93%)
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.
- Introduction to Machine Learning- In this week, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication.
- Regression- You will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications.
- Classification- You will learn about classification techniques. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression, and SVM.
- Clustering- In this section, you will learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping the same vehicles, and also clustering of weather stations.
- Recommender Systems- In this module, you will learn about recommender systems. First, you will get introduced to the main idea behind recommendation engines.
- Final Project- In this module, you will do a project based on what you have learned so far. You will submit a report on your project for peer evaluation.
9. Applied Data Science with Capstone (86%)
This capstone project course will give you a taste of what data scientists go through in real life when working with data.
- Learning Objective and Syllabus- In this module, you will learn about the scope of this capstone course and the context of the project that you will be working on.
- Getting The Car Accident Severity Data- In this module, you will decide on what data you will use for the Capstone. As mentioned in Week-1, you have two options. The first option is to use a shared dataset in this Capstone. The second option is to use another dataset that you find from different resources as mentioned in the week-1 video.
- Building Your Solution- In this module, you will learn how to start working on real-world data. You will try different supervised machine learning algorithms.
What you will learn from this Course?
- You will learn what data science is, the various activities of a data scientist’s job, and the methodology to think and work like a data scientist
- Develop hands-on skills using the tools, languages, and libraries used by professional data scientists
- Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python
- Apply various data science skills, techniques, and tools to complete a project and publish a report
What skills you will gain from this Course?
- Data Science, Statistical Analysis, Machine Learning, Python Programming, Business Intelligence, Data Analysis, Pandas, and Numpy.
- Cloud databases and python.
- Relational Database management system (RDBMS)SQL.
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- The course module has been designed especially for beginners to understand the basics of Data Science.
- Hands-on projects will help you to clear your technical concepts.
- Course provides detail and clear information about Pandas, Numpy, statistical and data analysis.
- Users expect more exercise involving the tools of data science methodology.
- Should have prior knowledge of calculus and linear algebra.
Specification: IBM Data Science Professional Certificate