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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
Information extraction, data recovery, and a more thorough study of statistics are the areas of specialization in applied data science. By using theoretical, theoretical models and methodologies on the underlying data, it is the analysis of data to produce workable solutions to business problems. Dr. Satyajit Das, Assistant Professor, Computer Science & Engineering at the Indian Institute of Technology Palakkad, developed the Professional Certificate Programme in Applied Data Science online classes, which is provided by Emeritus.
Professional Certificate Programme in Applied Data Science online training is a 26-week program that involves video-based lectures along with assignments, assessments, quizzes, projects, and case studies that revolve around topics like data analysis, machine learning, data engineering, data cleaning, data models, data visualization, data science, data manipulation and more. Professional Certificate Programme in Applied Data Science syllabus is designed for professionals who are interested in pursuing a career in data science and who want to comprehend the inner workings of the field to differentiate themselves from the competition.
The Highlights
- Certificate of completion
- Self-paced course
- 4-6 hours of weekly effort
- 4 office hours with industry experts
- 7.5 hours of assessments
- 6 Assignments
- 24 quizzes
- 1 project
- 2 case studies
Programme Offerings
- Online Course
- 26-week program
- Learning resources
- peer learning
- Unlimited access
- Accessible on mobile devices and TV
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIT Palakkad |
What you will learn
After completing the Professional Certificate Programme in Applied Data Science online classes, participants will gain a practical understanding of the principles of data science and applied data science using machine learning and data engineering tools including python, pandas, and Matplotlib. Participants will explore the fundamentals associated with data manipulation, data cleaning, data analysis, data visualization, linear regression, non-linear regression, time series analysis, social network analysis, and text analysis. Participants will also learn about methodologies involved with decision trees, data models, and clustering models.
Who it is for
The Syllabus
- Probability - Joint, Conditional, Marginal
- Probability Distributions
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
- Sample and Population
- Sampling Techniques
- Sampling Size Calculations
- Correlation, Covariance and Interquartile Ranges
- Inferential Analysis-Hypothesis Testing
- Introduction to Data Science
- Working with Data Types and Operators in Python
- Writing Functions in Python
- Popular Data Science Packages in Python
- Advanced Functions
- Data Manipulation and Analysis with Pandas
- Data Visualization with Matplotlib
- Random Variables and Statistical Inferences
- Statistical Distributions and Hypothesis Testing
- Data Cleaning
- Exploratory Data Analysis
- Getting Started with Linear Algebra for Machine Learning
- Data Engineering
- Data Storage
- Data Visualization
- Classification and Decision Trees
- Linear and non - linear regression
- Evaluating Data Models
- Time series analysis forecasting
- Clustering Models
- Exploratory Data Analysis
- Topic Model
- Text Analysis
- Social Network Analysis
Instructors
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