Coursera – Data Science Fundamentals with Python and SQL Specialization
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Build the Foundation for your Data Science career. Develop hands-on experience with Jupyter, Python, SQL. Perform Statistical Analysis on real data sets.
What you’ll learn
• 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
Skills you’ll gain
• Probability And Statistics
• Basic Descriptive Statistics
• Statistical Hypothesis Testing
• Data Visualization
• Regression Analysis
• Data Science
• Data Analysis
• Python Programming
• Numpy
• Pandas
• Jupyter notebooks
• Cloud Databases
• Relational Database Management System (RDBMS)
• SQL
• Github
• Rstudio
Specialization – 5 course series
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. This 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 specialization consists of 5 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.
This program is ACE® recommended-when you complete, you can earn up to 8 college credits.
1. Tools for Data Science
2. Python for Data Science, AI & Development
3. Python Project for Data Science
4. Statistics for Data Science with Python
5. Databases and SQL for Data Science with Python
5 Courses, Total, 409 Files, 93 Folders
Applied Learning Project
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. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:
Extracting and graphing financial data with the Pandas data analysis Python library
Generating visualizations and conducting statistical tests to provide insight on housing trends using census data
Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools.
RAPIDGATOR
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TURBOBIT
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