IBM Data Science Professional Certificate
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Program
IBM Data Science Professional Certificate
Become a data science specialist. Acquire valuable competencies to further your journey in data science and enhance your career.
Earn your IBM Professional Certificate after successful completion of the program
Self-Paced
Mentored
Intermediate
Duration
30 weeks, online
6-8 hours/week
The IBM Data Science Professional Certificate provides learners with a thorough grounding in data science, the role of the data scientist in our world, and the approaches they use to solve real-world challenges. You will grow familiar with popular data science tools, including Jupyter notebooks, RStudio IDE, and IBM Cloud. And you will become skilled in using data science methodology to build, test, and train data models.
Additionally, you will learn how to work with Python, and become proficient in various Python libraries such as pandas, NumPy and BeautifulSoup. You will develop a project in Python to test and demonstrate your knowledge. Other skills you learn during the course will also enable you to design data science models and AI applications.
Through practice SQL exercises on real-world data sets, you will develop a thorough understanding of the role of SQL and databases. You will also create impactful visual representations of data with Python data visualization libraries – Matplotlib, Seaborn, Folium, Plotly and Dash. And you will be introduced to machine learning, including regression, classification, and clustering.
Your IBM capstone project will be to design and build a data model that endeavours to solve a real-world issue. This project will be submitted to IBM for evaluation.
This IBM Professional Certificate program comprises ten purposely designed courses that take you on a carefully defined learning journey. At the end, you will submit a multi-faceted capstone project for evaluation by IBM.
It is a self-paced program, which means it isn’t run to a fixed schedule with regard to completing courses or submitting assignments. To give you an idea of how long the program takes to complete, it is anticipated that if you work 6-8 hours per week, you will complete the program in 30 weeks. However, as long as the program is completed before the end date, you can work at your own pace. The materials for each course module will become available when you start the particular course
You can choose to enrol for the complete certification program in one go or sign up for individual courses. Each course that you complete will take you a step closer to acquiring the IBM Professional Certificate. And it is worth noting that some courses may also qualify for other programs.
As part of our mentoring service, you will have access to valuable guidance and support throughout the program. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.
Overall, for students keen to formalise their skills in data science, the IBM Data Science Professional Certificate is a superb opportunity to showcase your applied understanding of the subject. Please note that you must successfully complete the capstone project to complete the program and earn the IBM Professional Certificate.
After completing this course, you will have:
- Competence in building, testing, and training data models.
- Practical knowledge of Python libraries; pandas and NumPy.
- A thorough understanding of data science methodology.
- An understanding of databases and SQL; practical application using real-world datasets.
- A deep understanding of how machine learning works using Sci-Kit and SciPy.
- In-depth knowledge of how data scientists solve real-world problems using data sets.
- Valuable skills in Matplotlib, Seaborn, Folium, Plotly and Dash libraries to create impactful data visualizations
- Hands-on experience of implementing data science tools: Jupyter Notebook, RStudio IDE, GitHub, DB2 and IBM Cloud.
- Individuals who are keen to begin their career in data science.
- Individuals looking to land their first data science job.
- Experienced developers seeking to upskill, enhance their data science knowledge, and participate in projects.
- There are no prerequisites for this program.
Program Outline
Discover the insights and trends in data.
Topic Covered:
Why is data science such a sought-after field?
Key skills required to pursue a career in data science.
Insights from data science professionals.
Tools and algorithms used in data science practice
What skills do you need to be a successful data scientist?
What is the role of data science within a business?
You can choose to enroll for this individual course. Click here to see course details.
Learn how to use data science tools effectively.
Topic Covered:
Popular data science tools; their features, and usage.
Mastering Jupyter Notebook, RStudio IDE, GitHub, and Watson Studio.
Testing tools on the IBM cloud platform using cognitive class labs.
Running simple code in Python and R.
Creating and sharing a Jupyter notebook.
You can choose to enroll for this individual course. Click here to see course details.
Acquire skills for tackling data science problems effectively.
Topic Covered:
The major steps involved in tackling a data science problem.
The key stages in data science methodology; business understanding and analytic approach.
Solving data science problems; business understanding, analytic approach stages, data requirements, and data collection stages.
What is the purpose of data modeling? The characteristics of the modeling process.
What happens after model deployment?.
The importance of model feedback.
You can choose to enroll for this individual course. Click here to see course details.
Begin your data science programming journey with Python.
Topic Covered:
Understanding Python; its fundamentals and uses.
Applying Python to data science.
Defining variables in Python.
Data structures and data analysis.
Performing I/O operations on files in Python.
How to use pandas; data analysis and manipulation in Python.
You can choose to enroll for this individual course. Click here to see course details.
This mini course is intended to demonstrate foundational Python skills for working with data.
Topic Covered:
Demonstrate your skills for working with Python and Data.
Web scraping specific data set.
You can choose to enroll for this individual course. Click here to see course details.
Work with real databases, real data science tools, and real-world datasets.
Topic Covered:
Applying foundational knowledge of the SQL language.
Creating a database in the cloud.
Using string patterns and ranges to query data.
Sorting and grouping data in result sets and by data type.
Analyzing data on a database using Python.
You can choose to enroll for this individual course. Click here to see course details.
Acquire skills to predict future trends from data using Python.
Topic Covered:
Analyzing data using Python.
Preparing data for analysis.
Creating meaningful data visualizations and predicting future trends from data.
Using pandas; learning to load, manipulate, analyze, and visualize datasets.
Understanding scikit-learn; how machine learning algorithms are used to build smart models and make predictions.
You can choose to enroll for this individual course. Click here to see course details.
Create impactful data visuals using various Python libraries.
Topic Covered:
Data visualization.
Data visualization libraries in Python; Matplotlib, Seaborn, Folium, Plotly and Dash.
Specialized visualization tools; pie charts, box plots, scatter plots, and bubble plots.
Advanced visualization tools; waffle charts, word clouds, Seaborn and regression plots.
Creating maps and visualizing geospatial data.
Creating Dashboards with Plotly and Dash.
You can choose to enroll for this individual course. Click here to see course details.
Gain a thorough understanding of machine learning and how it impacts society.
Topic Covered:
Machine learning applications.
Overview of supervised and unsupervised learning.
Unsupervised learning algorithms; clustering and dimensionality reduction.
Relating statistical modeling to machine learning; how to compare them.
How machine learning affects society; real-life examples.
Recommender systems; two main types of engine.
You can choose to enroll for this individual course. Click here to see course details.
Apply your new skills and knowledge to building a data model and checking its effectiveness.
Topic Covered:
Data science and machine learning; a real-life scenario.
Analyzing and visualizing data using Python.
An engineering exercise using Python.
Building and validating a predictive machine learning model using Python.
Actionable insights into real-life data problems; create and share.
Capstone project for submission to IBM for evaluation.
You can choose to enroll for this individual course. Click here to see course details.
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
Program Offering
Type of certificate
IBM Professional Certificate
About this program
10 Courses
09 Skills
Includes
Discussion Space
100 Hands on labs - over 80 hours
60 Quizzes
198 Videos
Create
Jupyter Notebook using IBM Watson Studio
Data collection model
Stock data extracting code/web scraper
Dashboard
Analyzing and prediction model
Future prediction model
Exercises to explore
Python
NumPy
Pandas
Web Scrapping
Matplotlib
Plotly
GitHub
RStudio
IBM Cloud
Db2
Jupyter Notebook
This program has been created by these experts
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FAQs
Data science is a blend of programming tools, statistical analysis, algorithms, and machine learning principles. The goal of a data scientist is to utilize all of these instruments to discover meaningful patterns hidden in raw data. And it is data science that provides the framework and methodologies to achieve this.
Data scientists should have a good understanding of data analysis as well as strong programming skills. They need to develop skills in two distinct areas:
Technical Skills - To be a good data scientist, you must excel in mathematics and statistics. However, other technological expertise is also required, including programming, knowledge of analytical tools, and the ability to manage unstructured data.
Non-Technical Skills - An individual’s soft skills - sometimes referred to as people skills - are classified as non-technical. To be a good data scientist, you need exceptional business sense, strong communication skills, and data intuition.
Data scientists work collaboratively with business stakeholders to figure out how data can help them accomplish their goals. They create algorithms and prediction models to extract the required data for a given objective in order to then analyze the data, create meaningful, visual reports, and share information.
Within data science, there are many roles for data scientists to perform:
- Data Scientist - A data scientist investigates data trends in order to assess their influence on a company.
- Data Analyst - A data analyst analyzes data to determine market trends.
- Data Engineer – A data engineer is in charge of creating, designing, and managing an organization’s database(s).
- Business Intelligence Analyst - A business intelligence analyst assists in the analysis of data, with the aim of increasing the efficiency of the organization and generating more revenue.
- Marketing Analyst - A marketing analyst researches and recommends which products should be produced for which target clients, and at what prices they should be offered.
Python is a popular language for those who work in data science. Many employers will advertise roles stating that applicants require coding skills in Python. We therefore recommend that you seek to develop skills in Python if you wish to become a data scientist.
This IBM Data Science Professional Certificate is a self-paced program. It means that you can work at a pace that suits you. It does not follow a predetermined timetable, unlike scheduled live sessions. You are free to work at your own speed if you complete the modules and all the courses before the deadline. .
This program comprises ten purposely designed IBM data science courses that take you on a carefully defined learning journey. .
Yes, you can sign up for an individual IBM data science course. Each course that you complete will take you a step closer to acquiring the IBM Professional Certificate. And it is worth noting that some courses may also qualify for other programs. .
Program Offering
Type of certificate
IBM Professional Certificate
About this program
10 Courses
09 Skills
Includes
Discussion Space
100 Hands on labs - over 80 hours
60 Quizzes
198 Videos
Create
Jupyter Notebook using IBM Watson Studio
Data collection model
Stock data extracting code/web scraper
Dashboard
Analyzing and prediction model
Future prediction model
Exercises to explore
Python
NumPy
Pandas
Web Scrapping
Matplotlib
Plotly
GitHub
RStudio
IBM Cloud
Db2
Jupyter Notebook
This program has been created by these experts
Newsletters & Updates
Subscribe to get the latest tech career trends, guidance, and tips in your inbox.