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, Campus Based/Physical Classroom | Video and Text Based | Weekends |
Course Overview
The Executive Programme in Data Science by Eruditus is a nine-month, intensive course ideal for candidates who want to make a career in data science and machine learning. The course curriculum has been carefully designed in partnership with IIM Lucknow, to help you gain fluency in data science’s key concepts, like data analysis, structure, tools, techniques, machine learning algorithms, statistics concepts, and more.
The Executive Programme in Data Science training consists of a cutting-edge, multi-modular training curriculum, where you will indulge in real-world case interactions, hands-on assignments, and expert guidance. With the industry-expert faculty and immersive classes, you will learn about the various techniques and tools for data handling, analysing, managing, interpreting, and mastering the basics of machine learning.
Moreover, the online Executive Programme in Data Science at Eruditus features online live classes, in-class discussions, campus immersion sessions, and IIM Lucknow’s prestigious MDP Alumni status. After successful completion, you will get the course certification as well.
The Highlights
- Live online classes
- Renowned faculty
- Nine months course
- Industry-expert mentors
- In-campus immersions at IIML
- Multi-modular programme
- Intensive course
- Course completion certificate
- Discussions on real-time cases
Programme Offerings
- multi-modular learning
- online live classes
- renowned faculty members
- industry-relevant case discussions
- intensive curriculum
- IILM’S MDP Alumni Status
- completion certificate
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIM Lucknow |
Eligibility Criteria
Education
To enrol in the Executive Programme in Data Science training, candidates must have completed graduation and should have one year of prior work experience. Also, candidates should be comfortable with learning some programming language, in addition to having adequate knowledge in high-school mathematics.
Moreover, any prior knowledge or expertise in programming is not required but candidates must have a mathematical orientation for a hassle-free learning experience.
What you will learn
After completing the Executive Programme in Data Science classes, you will: -
- Have a better understanding of data science, and its key concepts such as data analysis and data structure
- Have a managerial account of data science and machine learning’s techniques and tools
- Have the necessary skills to make real-time, day-to-day, data-driven organisational decisions
- Understand the required concepts of statistics to make effective and strategic business decisions
- Have the knowledge and skills to manage analytics and data science teams at an organisation
- Have the skills for implementing machine learning algorithms
Who it is for
The Syllabus
- Data Science Life Cycle
- R
- Data Cleaning
- Handling Missing Data and Outliers
- Data Visualization
- Basic Statistical Measures
- Probability
- Random Variable
- Binomial and Normal Distributions
- Sampling
- Hypothesis Testing - One Sample and Two Samples
- Chi-Square Tests
- ANOVA
- Linear Regression
- Logistic Regression
- Time Series Analysis
- PCA
- Factor Analysis
- Fundamentals of Python
- Supervised and Unsupervised Machine Learning
- NLP: Text, Web, Recommender Systems
- Data Scraping: Extracting Data from Twitter, Instagram
- Data Storage: No SQL and Hadoop Ecosystem
- Data Processing: PIG, HIVE, Spark and In Memory Analytics
- Artificial Intelligence
- Deep Learning: Backpropagation
- RNN
- CNN
- Reinforced Learning
- Generative AI
- How to use ML algorithms for CRM and Personalization
- Fraud Detection
- Sentiment Analysis and Customer Feedback
- Dynamic Price Optimization
- How to use Cognitive Analytics and Deep Learning algorithms for automation
- Building Recommendation Systems
- Analysing Consumer Data
- Conducting Risk Management
- Understanding Cloud Infrastructure essentials
- Cloud Based ML Services and Databases
- Cloud Enablement - scalability and flexibility
- Understanding emerging themes: FaaS, Edge Computing, Federated Learning, AutoML, Explainable AI, Cloud ML-Ops