IBM Data Science Professional Certificate course

Learn what data science is, the various activities of a data scientist’s job, and methodology to think and work like a data scientist Develop hands-on skills using the tools, languages, and libraries used by professional data scientists Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python Apply various data science skills, techniques, and tools to complete a project and publish a report

Please find below the coursewise key learnings. 10 Courses in this Professional Certificate.

  1. What is Data Science?
    In this course, we met data science practitioners and got an overview of what data science is today.
  2. Tools for Data Science
    In this course, I learnt about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. Also 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 Skills Network Labs, I was able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, created a final project with a Jupyter Notebook on IBM Watson Studio
  3. Data Science Methodology
    In this course, I learnt: – The major steps involved in tackling a data science problem. – The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. – How data scientists think!
  4. Python for Data Science, AI & Development
    Indepth learning of programming in Python — learnt Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and a final project to demonstrate the new skills. Developed comfort in creating basic programs, working with data, and solving real-world problems in Python. Gained a strong foundation for more advanced learning in the field.
  5. Python Project for Data Science
    This mini-course helped to demonstrate foundational Python skills for working with data. A simple dashboard was developed using Python.
  6. Databases and SQL for Data Science with Python
    Working knowledge of databases and SQL
    Also got introduced to the relational database concepts that helped learn and apply foundational knowledge of the SQL language. Started with performing SQL access in a data science environment. Worked with real databases, real data science tools, and real-world datasets. Created a database instance in the cloud. Through a series of hands-on labs practiced building and running SQL queries. Learned to access databases from Jupyter notebooks using SQL and Python.
  7. Data Analysis with Python
    Learnt to analyze data using Python. Learnt to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!Topics covered:
    • Importing Datasets
    • Cleaning the Data
    • Data frame manipulation
    • Summarizing the Data
    • Building machine learning Regression models
    • Building data pipelines
  8. Data Visualization with Python
    Learnt to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we used several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.
  9. Machine Learning with Python
    Learning about the purpose of Machine Learning and where it applies to the real world. Secondly, got a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
  10. Applied Data Science CapstoneThis capstone project course gave a taste of what data scientists go through in real life when working with real datasets. Assumed the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process followed the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting the results to the stakeholders.