Data Science Methodology
Course
Data Science Methodology
Gain the critical skills you need to tackle problems effectively. Learn how to solve problems through analyzing data to address real-world business scenarios.
Develop your skills in this critical aspect of data science and take a great step forward in your data science career.
Self-Paced
Mentored
BEGINNER
Duration
5 weeks, online
2 hrs/week
Fee
The data a business holds is a valuable asset. Through careful data analysis conducted utilizing proven methodologies, organizations can gain important insights that give them a competitive edge. To achieve this, data scientists must have a firm understanding of what questions to ask in the first place, as well as the necessary knowledge of how to analyze the data. Developing strong competencies in these areas is critical, therefore, for a successful career in data science.
During this course, you will be introduced to the major steps required for tackling a data science problem. You will learn about the key stages in data science methodology. You will develop your understanding of which methodologies are used for different analytic approaches. You will discover how to solve data science problems and come to appreciate the value and purpose of data modeling. And you will explore the characteristics of the modeling process, what happens after model deployment, and the importance of model feedback.
Having a firm grasp of data science methodology is critical for a career in this field. In this course, you will develop the skills you need to ensure the data you are using is both relevant and properly manipulated in order to address business and real-world challenges.
This FutureSkills Prime/IBM course comprises five purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 2 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include videos, reading material, and online exam questions.
As part of our mentoring service you will have access to valuable guidance and support throughout the course. 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 will vary across packages.
Once you have successfully completed the course, you will earn your Certificate of Completion. You will also receive an IBM Certificate.
You will be able to:
- Tackle a data science problem.
- Understand why data scientists need a methodology and an approach.
- Understand how to prepare and clean data.
- Form a concrete business question or research.
- Individuals seeking to understand the fundamentals of dealing with a data science problem.
- Individuals seeking to understand the process of resolving a data science query.
- Individuals seeking to understand models for solving a data science problem.
No experience required
This course is aligned with industry-approved occupational standards set by SSC NASSCOM. Once youve successfully completed this course, you will receive a Certificate of Completion that confirms you have:
- Job-ready competencies
- Practical experience
- Assessed technical knowledge
The national occupational standards to which this course is aligned relates to the following job roles:
- Business Intelligence Analyst
- Data Scientist
Course Outline
Earn your certificate
Once you have completed this course, you will earn your certificate.
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.
Course Offering
Type of certificate
IBM Certificate, Certificate of Participation
About this course
05 Modules
01 Skill
Includes
01 Final exam
05 Quizzes
12 Videos
Exercises to explore
Python
Jupyter notebook
This course has been created by
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FAQs
Data science is a field that integrates programming tools, statistical analysis, algorithms, and machine learning concepts in order to extract useful insights from massive amounts of data. It entails applying a range of disciplines, including statistics, scientific techniques, artificial intelligence (AI), and data analysis.
During this course you will learn:
- The most important steps in solving a data science problem.
- The primary phases involved in data science practice, from defining a specific business or research challenge to collecting and analyzing data, constructing a model, and comprehending the feedback received after model deployment.
- To think like a data scientist.
Job roles in data science include:
Data Analyst
A data analyst examines data and draws conclusions from their results. They assist in the development of a comprehensive picture of the company's market position through data extraction, data cleansing, and first-data analysis using high-tech computer models. They also evaluate data quality and report back to management on their findings.
Data Engineers
Data engineers are the backbone of a business because they are in charge of database design and management. They supervise the installation of data pipelines and ensure that information is sent to the appropriate departments. They also work with other data experts and use data visualization to convey their results with the rest of the company.
Business Intelligence Analyst
A business intelligence analyst examines data to help a company become more productive and efficient. They know how to use a variety of specialized machines and tools. They also serve as a link between business and technology, assisting both in achieving better results.
Marketing Analyst
A marketing analyst assists the marketing department of a corporation. They conduct research and provide recommendations on what should be mass-produced versus what should be scrapped. Customer satisfaction surveys are also used to improve present products and services, as well as to identify new things to sell to target clients.
Yes, it is a self-paced course that you can do at your own pace. It does not follow a predetermined timetable, unlike scheduled live sessions. You are free to work at your own pace if you complete the modules and the course before the deadline.
Yes. Data science methodology is a 100% online course. You will therefore need a good connection to the internet in order to be able to access the course materials.
Course Offering
Type of certificate
IBM Certificate, Certificate of Participation
About this course
05 Modules
01 Skill
Includes
01 Final exam
05 Quizzes
12 Videos
Exercises to explore
Python
Jupyter notebook
This course has been created by
Newsletters & Updates
Subscribe to get the latest tech career trends, guidance, and tips in your inbox.