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Course overview
Course Title: Introduction to Data Science and Analytics
Note: After enrolment please contact here or email - mentor@ipreptoday.org
Week 1: Foundations of Data Science
Session 1: Overview of Data Science
- Understanding the role and significance of data science in various industries.
- Introduction to key concepts, terminologies, and the data science lifecycle.
Session 2: Basics of Statistics for Data Science
- Exploring fundamental statistical concepts relevant to data analysis.
- Hands-on exercises using common statistical tools.
Week 2: Data Wrangling and Cleaning
Session 3: Data Collection and Cleaning
- Techniques for acquiring and cleaning raw data for analysis.
- Introduction to data cleaning tools and best practices.
Session 4: Exploratory Data Analysis (EDA)
- Analyzing and visualizing data to gain insights.
- Use of EDA tools and interpretation of results.
Week 3: Introduction to Machine Learning
Session 5: Fundamentals of Machine Learning
- Overview of machine learning concepts and types.
- Understanding supervised and unsupervised learning.
Session 6: Hands-on with Machine Learning Tools
- Practical application of machine learning algorithms using popular tools.
- Building and evaluating basic machine learning models.
Week 4: Data Visualization and Communication
Session 7: Data Visualization Techniques
- Principles of effective data visualization.
- Tools for creating impactful visualizations.
Session 8: Communicating Insights
- Strategies for presenting data findings to non-technical stakeholders.
- Developing a compelling narrative using data.
Assessment and Certification:
- Final project: Participants apply learned concepts to a real-world scenario.
- Online assessment at the end of each week.
- Participants who successfully complete the course receive a virtual certification in "Introduction to Data Science and Analytics."
This 4-week capsule course provides a comprehensive introduction to the foundations of data science, machine learning, and data visualization, equipping participants with essential skills for a career in this dynamic field.
What will i learn?
- Foundational Understanding: Participants gain a solid foundation in the fundamental concepts and principles of data science and analytics.
- Data Analysis Skills: Acquire skills for collecting, cleaning, and analyzing data using various tools and techniques.
- Statistical Knowledge: Understand basic statistical concepts relevant to data analysis and interpretation.
- Machine Learning Basics: Gain an introduction to machine learning, covering key concepts and applications.
- Communication Skills: Learn strategies for effectively communicating data findings to both technical and non-technical stakeholders.
- Practical Application: Engage in hands-on exercises and real-world applications to reinforce theoretical concepts.
- Problem-Solving Skills: Develop problem-solving skills in the context of data-related challenges and decision-making.
- Career Readiness: Enhance employability and be better prepared for roles requiring data analysis skills in various industries.
- Certification: Many courses offer a certification upon completion, providing participants with a credential to showcase their understanding and skills.
Requirements
- Educational Background: Most introductory courses in data science and analytics do not have strict educational prerequisites. They are often open to individuals with diverse educational backgrounds, including undergraduate students, working professionals, and those seeking a career change.
- Basic Computer Skills: A basic understanding of computer usage and proficiency in using common software applications is often beneficial. Familiarity with spreadsheet programs like Microsoft Excel is usually sufficient.
- English Proficiency: Since course materials and instructions are often presented in English, a basic proficiency in the English language may be required.
- Interest in Data and Analytics: The primary requirement is a genuine interest in data science and analytics. Enthusiasm for working with data, solving problems, and gaining insights is crucial.
- Access to a Computer and Internet: Participants typically need access to a computer with internet connectivity to access course materials, participate in online activities, and complete assignments.
- No Prior Data Science Experience Required: Introductory courses are designed for beginners, and participants are not expected to have prior experience in data science or analytics.
Curriculum for this course
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Foundations of Data Science
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Data Wrangling and Cleaning
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Introduction to Machine Learning
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Data Visualization and Communication
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Assessment and Certification
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