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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

IBM Data Science Professional Certificate Highlights

Fee

$879 - $1729

Fee

$879 - $1729

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

This program is offered through a series of 10 courses.

Your IBM Professional Certificate will be issued once you have completed the full program and your capstone project has been successfully evaluated by IBM.

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Earn your certificate

Once you have successfully completed this program, you will earn IBM Data Science Professional certificate

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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.