Prepare for a career in the high-growth field of data science. In this program, I developed the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist. 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.
I learned in-demand skills used by professional data scientists, including databases, data visualization, statistical analysis, predictive modeling, machine learning algorithms, and data mining. I also worked 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, I had built a portfolio of data science projects. This Professional Certificate had a strong emphasis on applied learning and included a series of hands-on labs in the IBM Cloud that gave me practical skills.
Course Link: IBM Data Science Professional Certificate
- Jupyter
- JupyterLab
- GitHub
- R Studio
- Watson Studio
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Folium
- ipython-sql
- Scikit-learn
- ScipPy, etc
- Extracting and graphing financial data with the Pandas Python library.
- Using SQL to query census, crime, and school demographic data sets.
- Wrangling data, graphing plots, and creating regression models to predict housing prices with data science Python libraries.
- Creating a dynamic Python dashboard to monitor, report, and improve US domestic flight reliability.
- Applying and comparing machine learning classification algorithms to predict whether a loan case would be paid off or not.
- Training and comparing machine learning models to predict if a space launch could reuse the first stage of a rocket.
- Dr. Pooja
- Romeo Kienzler
- Joseph Santarcangelo
- Polong Lin
- Alex Aklson
- Rav Ahuja
- Saishruthi Swaminathon
- Saeed Aghabozorgi
- Hima Vasudevan
- Azim Hirjani
- Aije Egwaikhide
- Yan Luo
- Svetlana Levitan
- What is Data Science?
- Defining Data Science and What Data Scientists Do
- Data Science Topics
- Applications and Career in Data Science
- Data Literacy for Data Science
- Tools for Data Science
- Overview of Data Science Tools
- Languages of Data Science
- Packages, APIs, Datasets and Models
- Jupyter Notebooks and JupyterLab
- Data Science Methodology
- From Problem to Approach and From Requirements to Collection
- From Understanding to Preparation and From Modeling to Evaluation
- From Deployment to Feedback and Final Evaluation
- Final Project and Assessment
- Python for Data Science, AI & Development
- Python Basics
- Python Data Structers
- Python Programming Fundamentals
- Working with Data in Python
- APIs, and Data Collection
- Python Project for Data Science
- Databases and SQL for Data Science with Python
- Getting Started with SQL
- Introduction to Relational Databases and Tables
- Intermediate SQL
- Accessing DAtabases using Python
- Course Assignment
- Advanced SQL for Data Engineering
- Data Analysis with Python
- Importing Data Sets
- Data Wrangling
- Exploratory Data Analysis
- Model Development
- Model Evaluation and Refinement
- Final Assignment
- Data Visualization with Python
- Introduction to Data Visualization Tools
- Basic and Specialized Visualization Tools
- Advanced Visualizations and Geospatial Data
- Creating Dashboards with Plotly and Dash
- Final Project and Exam
- Machine Learning with Python
- Introduction to Machine Learning
- Regression
- Classification
- Linear Classification
- Clustering
- Final Exam and Project
- Applied Data Science Capstone
- Introduction
- Exploratory Data Analysis (EDA)
- Interactive Visual Analytics and Dashboard
- Predictive Analysis (Classification)
- Presentation