IBM Data Science Professional Certificate

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 4 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 and FIBAA recommended —when you complete, you can earn up to 12 college credits and 6 ECTS credits.

There will be 12 courses in this professional certificate program. They are

What is Data Science?

• Define data science and its importance in today’s data-driven world.
• Describe the various paths that can lead to a career in data science.
• Summarize  advice given by seasoned data science professionals to data scientists who are just starting out.
• Explain why data science is considered the most in-demand job in the 21st century.

Tools for Data Science

• 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. 

Data Science Methodology

• Describe what a data science methodology is and why data scientists need a methodology.
• Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
• Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
• Determine appropriate data sources for your data science analysis methodology.

Python for Data Science, AI and Development

• 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.

Python Project for Data Science

• 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.

Databases and SQL for Data Science with Python

• 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.

Data Analysis with Python

• Develop Python code for cleaning and preparing data for analysis – including handling missing values, formatting, normalizing, and binning data
• Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy
• Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
• Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Data Visualization with Python

• Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story
• Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble
• Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps
• Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library

Machine Learning with Python

• Job-ready foundational machine learning skills in Python in just 6 weeks, including how to utilizeScikit-learn to build, test, and evaluate models.
• How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.
• How to implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks.
• How to evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability.

Applied Data Science with Capstone

• Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders 
• Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation
• Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors
• Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model 

Generative AI: Elevate your data science career

• Leverage generative AI tools, like GPT 3.5, ChatCSV, and tomat.ai, available to Data Scientists for querying and preparing data
• Examine real-world scenarios where generative AI can enhance data science workflows
• Practice generative AI skills in hand-on labs and projects by generating and augmenting datasets for specific use cases
• Apply generative AI techniques in the development and refinement of machine learning models

Data Scientist Career Guide and Interview Preparation

• Describe the role of a data scientist and some career path options as well as the prospective opportunities in the field.
• Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.
• Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.
• Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

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. You’ll also have the option to learn how generative AI tools and techniques are used in data science.

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 improve US domestic flight reliability
• Apply machine learning classification algorithms to predict whether a loan case will be paid off
• Train and compare machine learning models

What you will learn from this IBM Data Science Professional Certificate:

• Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles
• Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL
• Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines
• Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers

Rating: 4.6 | Level: Beginner | Course Duration: 4 months