Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field.
This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.
You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.
In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations.
This Specialization can also be applied toward the IBM Data Science Professional Certificate.
Course 1: What is Data Science? - Offered by IBM Skills Network. Do you want to know why Data Science has been labelled as the sexiest profession of the 21st century? After ... Enroll for free.
Course 2: Tools for Data Science - Offered by IBM Skills Network. In order to be successful in Data Science, you need to be skilled with using tools that Data Science ... Enroll for free.
Course 3: Data Science Methodology - Offered by IBM Skills Network. If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data ... Enroll for free.
Course 4: Databases and SQL for Data Science with Python - Offered by IBM Skills Network. Working knowledge of SQL (or Structured Query Language) is a must for data professionals like Data ... Enroll for free.
Do you want to know why Data Science has been labelled as the sexiest profession of the 21st century? After taking this course you will be able to answer this question, and get a thorough understanding of what is Data Science, what data scientists do, and learn about career paths in the field.
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. Since then, people using data to derive insights and predict outcomes 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 Machine Learning, Deep Learning, and Neural Networks 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 the thriving field that is Data Science!
If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. Most of the established data scientists follow a similar methodology for solving Data Science problems. In this course you will learn and then apply this methodology that can be used to tackle any Data Science scenario.
The purpose of this course is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:
- The major steps involved in practicing data science
- Forming a business/research problem, collecting, preparing & analyzing data, building a model,
deploying a model and understanding the importance of feedback
- Apply the 6 stages of the CRISP-DM methodology, the most popular methodology for Data Science and Data Mining problems
- How data scientists think!
To apply the methodology, you will work on a real-world inspired scenario and work with Jupyter Notebooks using Python to develop hands-on experience.
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.
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.
-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.