IBM
Data Science Fundamentals with Python and SQL Specialization
IBM

Data Science Fundamentals with Python and SQL Specialization

Build the Foundation for your Data Science career. Develop hands-on experience with Jupyter, Python, SQL. Perform Statistical Analysis on real data sets.

Murtaza Haider
Romeo Kienzler
Joseph Santarcangelo

Instructors: Murtaza Haider

Sponsored by Pontificia Universidad Católica del Perú

57,641 already enrolled

Get in-depth knowledge of a subject
4.6

(2,973 reviews)

Beginner level

Recommended experience

2 months
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.6

(2,973 reviews)

Beginner level

Recommended experience

2 months
at 10 hours a week
Flexible schedule
Learn at your own pace

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

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Taught in English

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Specialization - 5 course series

Tools for Data Science

Course 118 hours4.5 (29,399 ratings)

What you'll learn

  • Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools 

  • Utilize languages commonly used by data scientists like Python, R, and SQL 

  • Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features  

  • Create and manage source code for data science using Git repositories and GitHub. 

Skills you'll gain

Category: Statistical Programming
Category: Data Analysis
Category: Data Science
Category: Version Control
Category: R Programming
Category: Statistical Machine Learning
Category: Jupyter
Category: Git (Version Control System)
Category: GitHub
Category: Integrated Development Environments
Category: Software Development Tools
Category: Python Programming
Category: Software Versioning
Category: Software Development
Category: Development Environment
Category: Application Programming Interface (API)
Category: Software Configuration Management
Category: CI/CD
Category: Devops Tools
Category: IBM Cloud

Python for Data Science, AI & Development

Course 225 hours4.6 (39,468 ratings)

What you'll learn

  • Learn Python - the most popular programming language and for Data Science and Software Development.

  • Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.

  • Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.

  • Access and web scrape data using APIs and Python libraries like Beautiful Soup.

Skills you'll gain

Category: Python Programming
Category: Computer Science
Category: Computer Programming
Category: Data Processing
Category: Web Scraping
Category: Data Science
Category: Pandas (Python Package)
Category: Data Analysis
Category: Data Manipulation
Category: NumPy
Category: Algorithms
Category: Object Oriented Design
Category: Application Programming Interface (API)
Category: Object Oriented Programming (OOP)
Category: Information Management
Category: Jupyter
Category: Software Development
Category: Data Engineering
Category: Data Structures
Category: Extract, Transform, Load

Python Project for Data Science

Course 38 hours4.5 (4,452 ratings)

What you'll learn

  • Play the role of a Data Scientist / Data Analyst working on a real project.

  • Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.

  • Apply Python fundamentals, Python data structures, and working with data in Python.

  • Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

Skills you'll gain

Category: Data Science
Category: Data Analysis
Category: Pandas (Python Package)
Category: Data Processing
Category: Python Programming
Category: Jupyter
Category: Computer Science
Category: Data Engineering
Category: Web Scraping
Category: Extract, Transform, Load
Category: Information Management
Category: Computer Programming

Statistics for Data Science with Python

Course 414 hours4.5 (423 ratings)

What you'll learn

  • Write Python code to conduct various statistical tests including a T test, an ANOVA, and regression analysis.

  • Interpret the results of your statistical analysis after conducting hypothesis testing.

  • Calculate descriptive statistics and visualization by writing Python code.

  • Create a final project that demonstrates your understanding of various statistical test using Python and evaluate your peer's projects.

Skills you'll gain

Category: Statistical Analysis
Category: Statistics
Category: Data Analysis
Category: Probability & Statistics
Category: Statistical Methods
Category: Data Science
Category: Analytics
Category: Statistical Inference
Category: Correlation Analysis
Category: Probability Distribution
Category: Regression Analysis
Category: Descriptive Statistics
Category: Exploratory Data Analysis
Category: Statistical Modeling
Category: Statistical Visualization
Category: Statistical Hypothesis Testing
Category: Business Analytics
Category: Jupyter
Category: Statistical Machine Learning
Category: Statistical Programming

Databases and SQL for Data Science with Python

Course 520 hours4.7 (20,954 ratings)

What you'll learn

  • Analyze data within a database using SQL and Python.

  • Create a relational database and work with multiple tables using DDL commands.

  • Construct basic to intermediate level SQL queries using DML commands.

  • Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

Skills you'll gain

Category: Database Development
Category: Database Management
Category: SQL
Category: Query Languages
Category: Relational Databases
Category: Database Systems
Category: Database Management Systems
Category: Stored Procedure
Category: Databases
Category: Database Design
Category: Data Modeling
Category: Data Access
Category: Data Storage
Category: Data Management
Category: Database Architecture and Administration
Category: Database Theory
Category: Database Administration

Instructors

Murtaza Haider
IBM
3 Courses43,054 learners
Romeo Kienzler
IBM
10 Courses718,638 learners

Offered by

IBM

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