My solutions to the peer-reviewed assignments in the Data Science Professional Specialization offered by IBM on Coursera.

Course 2: Tools for Data Science

Course 5: Python Project for Data Science

Course 6: Databases and SQL for Data Science with Python

Course 7: Data Analysis with Python

Course 8: Data Visualization with Python

Course 10: Applied Data Science Capstone

Prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 5 months. No prior knowledge of computer science or programming languages is required.

Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes. The demand for skilled data scientists who can use data to tell compelling stories to inform business decisions has never been greater.

You’ll learn in-demand skills used by professional data scientists including databases, data visualization, statistical analysis, predictive modeling, machine learning algorithms, and data mining. You’ll also work with the latest languages, tools,and libraries including Python, SQL, Jupyter notebooks, Github, Rstudio, Pandas, Numpy, ScikitLearn, Matplotlib, and more.

Upon completing the full program, you will have built a portfolio of data science projects to provide you with the confidence to excel in your interviews. You will also receive access to join IBM’s Talent Network where you’ll see job opportunities as soon as they are posted, recommendations matched to your skills and interests, and tips and tricks to help you stand apart from the crowd.

This program is ACE® recommended—when you complete, you can earn up to 12 college credits.

Applied Learning Project

This Professional Certificate has a strong emphasis on applied learning and includes a series of hands-on labs in the IBM Cloud that give you practical skills with applicability to real jobs.

Tools you’ll use: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio

Libraries you’ll use: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.

Projects you’ll complete:

• Extract and graph financial data with the Pandas Python library

• Use SQL to query census, crime, and school demographic data sets

• Wrangle data, graph plots, and create regression models to predict housing prices with data science Python libraries

• Create a dynamic Python dashboard to monitor, report, and improve US domestic flight reliability

• Apply and compare machine learning classification algorithms to predict whether a loan case will be paid off or not

• Train and compare machine learning models to predict if a space launch can reuse the first stage of a rocket

👤 Aras Güngöre

• LinkedIn: @arasgungore
• GitHub: @arasgungore