Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based | Weekends |
Course Overview
The global business landscape has comfortably entered an era where multi-functional business decisions based on robust data-driven insights are not an optional extra, but a necessity. It has been proven to show solidarity that integrating data science and analytics results in priceless business insights never acted on before. This is industry agnostic and drives corporate strategy.
The Programme in Advanced Data Science offered by IIM Kozhikode introduces the participants to data management, data analysis, as well as the application of Machine Learning functions. Duly supported by Python and R, Advanced Data Science for Managers online course builds managerial and strategic acumen across the usage of data science.
Given that the majority of the input data is in a raw form, enrolling for this year-long Advanced Data Science for Managers online course will enable managers to mine text and engage in Social Media analytics. These aspects are potent to boost business growth and innovation. Moreover, the Professional Certificate Programme in classes contains case studies from real-world companies that are presented in order to get a real feel of how these tools are worth their weight in gold.
The Highlights
- One year program
- Live online classes
- Veteran faculty
- Real-world case studies
- Special corporate pricing
- Certificate issued on completion
Programme Offerings
- Flexible online classes
- Expert Faculty
- Industrial case studies
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIM Kozhikode |
The total program fee for the Advanced Data Science for Managers is as follows:
Programme Fee: INR 1,95,000 + GST
| Particulars | Amount in INR |
| Booking Amount | INR 20,000 + GST |
| Instalment 1 | INR 29,000 + GST |
| Instalment 2 | INR 78,000 + GST |
| Instalment 3 | INR 48,000 + GST |
| Instalment 4 | INR 20,000 + GST |
Application Fee Details
Instalments | Application Fee in INR |
Round 1 | INR 1,500 + GST |
Round 2 | INR 2,000 + GST |
| Round 3 | INR 2,500 + GST |
| Round 4 | INR 2,500 + GST |
Eligibility Criteria
Work experience
Minimum 1 year work experience for Graduates
Education
Graduates and above can apply
What you will learn
The students are likely to gain tremendous knowledge of the following subjects post completion of the Advanced Data Science program:
- Get equipped with the first principles associated with Data structures and their commercial applications
- Develop a sound understanding of SQL and Relational databases
- Apply data visualization techniques under the broad segment of Statistics and Exploratory data analysis
- Application of Data Analytics using Python and R
- Master the founding principles of statistical inferences
- Become proficient in the nuances associated with data modelling
- Recognize the techniques involved in machine learning algorithms such as linear, non-linear and logistic regression
- Utilize Time series analysis, classification, and clustering as part of Machine learning algorithms
- Gain a deep understanding of text mining and Social Media analysis in today’s evolving digital landscape
- Expedite the understanding and inculcation of optimization and simulation
- Equip yourself with industry best practices and their application in the Capstone project
Who it is for
The Professional Certificate Programme in Advanced Data Science is ideal for managers who want to address their Strategic Management career path. Other than this, the program is recommended to professionals leading machine learning and data science projects in their organizations
- Entrepreneurs and small business owners looking to unearth the capabilities of data science
- Data analysts seeking to make a career in Data science, thereby requiring a formal period of education of the subject
- Recent professionals in the data science field looking to further their technical capabilities in this ever-evolving, exciting arena of advanced data science
- Partners, Business heads, and C-suite professionals aspiring to merge synergies created by data-driven decisions on the backbone of Data science
- Management Consultants involved in B2B consulting assignments requiring extensive use of MS Excel and other analytics tools
Admission Details
The steps to subscribe to the Advanced Data Science for Managers are explained in the content that follows:
Step 1: Please check if the program’s commencement date has been communicated. If yes, please proceed to join the Advanced Data Science for Managers training from the homepage.
Step 2: For new registrants, please provide a valid, accessible email address. You will receive a one time password on it as a verification measure. Enter the same in its designated spot and move to the application portal. It consists of a Personal and Employment section that needs to be filled out to the best of your knowledge.
Step 3: In the final step, please make a selection of the payment method you would like to use such as credit/debit card, Internet banking, or UPI to make the payment for the program tuition.
Application Details
The application form requires basic personal information such as First and Last name, schooling and college details, and work experience, if any.
The Syllabus
- Data Science - Scope, and Relevance
- Data Science: Capabilities and Challenges
- Introduction to Python and data types
- Operations, functions, execution, and data processing
- Modules, packages, exceptions, and working with files
- Introduction R - packages, data frames, data types, and functions
- Importing, Loading, & Storing data and Writing output from analysis
- Data aggregation & Data Handeling, and Applications of dplyr package
- Statistical inference, hypothesis testing, one-sample and paired sample t-test
- Test of proportions, two-sample t-test for difference in means
- Chi-square test for goodness of fit of multinomial data, ANOVA
- Data cleaning and Data transformation
- Outliers, Binning and data balancing techniques
- Using EDA to generate useful insights, uncover anomalous fields & Application of PCA
- Data visualisation and execution of various types of plots
- Bivariate and multiple regressions, dummy variable regression, and regularized regressions (RIDGE & LASSO regressions)
- Supervised vs. unsupervised learning, and understanding/evaluating the model
- Logistic regression: Bivariate and multinomial logistic regression
- KNN algorithm for classification and prediction
- Naïve Bayes Algorithm
- Support vector machines
- Decision trees, and Random Forest
- Bagging and gradient boosting
- Clustering
- Affinity analysis and market basket analysis
- Principal component analysis (PCA)
- Deep learning, and artificial neural networks (ANN)
- Web Performance Analytics, and Google analytics
- Optimal publisher strategy and complete copy improvement analysis in online platforms
- Allocating resources between offline and online media
- Introduction to social media analytics
- Social network analysis (SNA) and generating social network metrics
- Advanced SNA concepts using NodeXL and Gephi
- Introduction to text analysis using WordArt, LIWC, and Weka
- Deriving informative insights by combining social network and text analyses