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IIM Kozhikode | Certificate Programme in Data Science

Globalizing Indian Thought

Course Snapshot
  • FeeINR 71,750 + GST
  • Work Experience5 - 30 Years
  • Duration12 Weeks (4-5 hours/ week)
  • Delivery MethodOnline
Course Detail

Program Overview :
This programme is designed for professionals from any domain looking upskill in order to advance their careers. It will provide you with tools that will give you an insight into modern data science practices. Understand how to quantify data, implement algorithms, use data tools for problem-solving, and how to gain a better understanding of how data can improve your business and drive revenue.

Who Is This Programme For :
This cutting-edge programme is a must-have skillset for :
● Business Leaders who look to data science to drive organisational transformation across teams.
● Professionals looking to understand Data Science methodologies and implement them to achieve team/ organisation goals.
● Project Leaders who want to lead data-driven projects and teams within their organisation.
● Professionals who aspire to lead data-driven disruptions in businesses across various domains like Retail, Pharma, Healthcare, Material Sciences, etc.

Program Highlights :
● Taught by eminent IIM Kozhikode Faculty
● 121 Video Lectures and Demos
● 32 Assignments and 7 Discussions
● Office Hours with an Industry Expert
● Peer Learning and Feedback
● 1 Final Project

Desired Candidate Profile

● Graduate or Diploma (10+2+3) in any discipline
● Basic Knowledge of Coding required

Course Modules

MODULE 1: Data Analytic Thinking
- Outline the importance of data in making business decision
- Describe types of data and data catagories
- Describe the people and processes involved in data cycle
- Compare the characteristics of small data and big data
- Discuss the importance of data analytic for business decision making
- Identify actions taken during the cross industry standard process of data mining Discuss the data science links with other discipline

MODULE 2: Data Analysis with Excel
- Describe the benefits of using Microsoft Excel for making data-driven decisions Calculate stastical averages using Microsoft Excel functions
- Apply basic Microsoft Excel tools for data analysis
- Perform different analysis on data using techniques like what-if analysis, goal seek analysis, sensitivity analysis
- Use filter function to remove duplicates and calculate subtotals

MODULE 3: Data Analysis with Python
- Review and delineate the evolution and purpose of Python
- Describe and set up Python development environment
- Practice coding with basic Python commands, operators and conditional statements
- Explore and apply Python data structure concepts such as array, list, tuple, set and dictionary Import python modules and packages
- Import Python libraries such as NumPy, Pandas

MODULE 4: Data Analytic Thinking
- Describe the need of data preparation
- Describe the sources of data
- Evaluate and improve quality of data
- Differentiate between hypothesis testing and exploratory data analysis Explore categorical variables

MODULE 5: Types of Data Analytics
- Describe spectrum of business analytics
- Describe application of descriptive analytics
- Draw conclusions from a given set of data by using descriptive analytic techniques Describe application of diagnostics analytics
- Draw conclusions from a given set of data by using diagnostics analytic techniques Describe application of predictive analytics
- Draw conclusions from a given set of data by using predictive analytic techniques Describe application of prescriptive analytics
- Draw conclusions from a given set of data by using prescriptive analytic techniques

MODULE 6: Data Modeling: Predictive Modeling
- Discuss Cross Industry Standard Process in Data Modeling
- Discuss a Generic data modeling process
- Apply prior knowledge the address the business problems

MODULE 7: Data Modeling: Fitting a Model
- Discuss overfitting modeling
- Explain data driven modeling
- Describe decision tree and its types
- Design a classification tree to resolve uncertainties

MODULE 8: Data Clustering
- Describe concepts of clustering and visualize data
- Apply K-means algorithm to cluster the data
- Apply Z-score method to standardise the data
- Interpret the cluster centre and create product segment
- Use Dendrogram and Elbow Curve for estimating the number of clusters Estimate the quality of clustering using Silhouette scores

MODULE 9: Data Clustering: Hierarchical clustering
- Explain the limitations of K-means clustering
- Apply hierarchical clustering to the product segmentation and the Gaussian distributed dataset
- Describe the DBSCAN clustering technique and its benefits
- Apply K-Means, Hierarchical and DBSCAN clustering to the moon dataset Discuss the limitations of clustering algorithms and techniques to address them

MODULE 10: Association and Co-occurences: Items That go Together
- Discuss correlation and its characteristics
- Perform association analysis by generating general association rules between market variables
- Apply association rule to restrict frequently appearing data items
- Apply association rule to identify frequency of conditional probability

MODULE 11: Association and Co-occurences: Measuring Suprises
- List the measures of surpise
- Use Naïve method for measuring leverage
- Use Apriorism algorithm for measuring leverage

MODULE 12: Project Brief and Course Summary