About this Course
Build the Foundation for your Data Science career. Develop hands-on experience with Jupyter, Python, SQL. Perform Statistical Analysis on real data sets. Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data-driven decisions has never been greater. The Data Science Fundamentals Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.
The Data Science Fundamentals specialization consists of 4 self-paced online courses that will provide you with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. You’ll learn these data science pre-requisites through hands-on practice using real data science tools and real-world data sets.
Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper in Data Science and work on more advanced Data Science projects.
No prior knowledge of computer science or programming languages required.
What you will learn from this course?
- Working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio.
- Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy.
- Statistical Analysis techniques including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression.
- Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables.
There are 5 Courses in this Data Science Fundamentals Specialization:
What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you’ll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers.
Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been a greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.
This course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills.
By the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field, and develop skills to help advance your career.
This course can be applied to multiple Specialization or Professional Certificate programs. Completing this course will count towards your learning in any of the following programs:
- IBM Applied AI Professional Certificate
- Applied Data Science Specialization
- IBM Data Science Professional Certificate
- Upon completion of any of the above programs, in addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital Badge from IBM recognizing your expertise in the field.
This mini-course is intended for you to demonstrate foundational Python skills for working with data. The completion of this course involves working on a hands-on project where you will develop a simple dashboard using Python.
This course is part of the IBM Data Science Professional Certificate and the IBM Data Analytics Professional Certificate.
Python for Data Science, AI and Development course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data.
NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.
This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including – data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.
At the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of foundational statistical thinking and reasoning.
The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately.
This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background.
We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided.
After completing this course in the Data Science Fundamentals program, a learner will be able to:
- Calculate and apply measures of central tendency and measures of dispersion to grouped and ungrouped data.
- Summarize, present, and visualize data in a way that is clear, concise, and provides a practical insight for non-statisticians needing the results.
- Identify appropriate hypothesis tests to use for common data sets.
- Conduct hypothesis tests, correlation tests, and regression analysis.
- Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks.
Project Data Science Fundamentals with Python and SQL
All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets. The projects range from building a dashboard with Python, analyzing socio-economic data with SQL, and performing regression analysis with housing data.
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- 3+ Months
- Paid Course (Paid certificate)
- Python R Scala
- Git Jupyter Notebook RStudio Watson Studio
- None Pre-requisite
- Artificial intelligence Data Analysis Data Science Data Science with 'Python' Data Visualization Practical Statistics Probability